DEV Community: Decipher Zone

Top 10 AI Trends To Watch in 2023

Priyanshi Sharma — Thu, 02 Mar 2023 06:09:21 +0000

Artificial Intelligence is no longer fiction. With voice assistants like Alexa and Siri or personalized recommendations on social media platforms, AI has become an integral part of daily lives.

Moreover, AI continuously evolves with advancements in Machine Learning and Deep Learning algorithms, making it hard to predict the future. However, based on recent developments and industry trends, we have listed the top 10 AI trends to watch in 2023.

While many are aware of Artificial Intelligence technology, some might still be unaware of the basics. So, before we move on to the top 10 AI trends, let’s define AI.

Artificial Intelligence: An Overview

Artificial Intelligence, aka AI, is the human intelligence stimulation so machines can perceive, synthesize, and conclude information. AI systems work by ingesting labeled data in a large amount, analyzing the data for patterns and correlations, and using analysis results to make informed predictions.

AI programming is focused on three intellectual skills:

Learning
Reasoning
Self-Correction

AI is popular because it reduces the time to complete data-heavy, detail-oriented tasks while delivering consistent results. Some industries where AI has made its way include healthcare, education, banking, finance, corporate, law, manufacturing, transportation, and security.

Google Search, YouTube, Netflix, Amazon, Siri, Cortana, Alexa, Self-driving Cars, and ChatGPT are real-life examples of artificial intelligence performing specific tasks.

Top AI Trends You Must Know About
With a better understanding of AI, it’s time to glance at the top AI trends you should be aware of:

Generative Models: The Future of AI-Generated Content
Explainable AI: Bringing Transparency and Trust to AI Systems
Human-Centric AI: Designing AI for Human Interaction and Collaboration
Edge AI: Moving Intelligence Closer to Devices
AutoML: Automating the Machine Learning Process
Quantum AI: Bridging the Gap Between Quantum Computing and AI
Federated Learning: Collaborative Machine Learning Across Devices
AI-powered CyberSecurity: Addressing the cyber risks with AI
Sustainable AI: Reducing carbon footprints AI
Autonomous AI: Self-Learning and Self-Optimizing AI Systems

Now, let’s look at each of these AI trends in detail.

Generative Models:

Generative AI is machine learning’s sub-field that is used to generate new content using existing data such as photos, videos, code, text, or sound. ChatGPT, DALL-E, ArtBreeder, and Pikazo. Generative AI aims to create original results by processing large data sets using unsupervised or semi-supervised learning.

Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the commonly used generative models.

Generative Adversarial Networks (GANs) comprise generative and discriminative neural networks. While the generative network creates outputs on request, the discriminative network tries to differentiate between real-world and fake data to improve the content quality.

On the other hand, VAE encodes data into a low-dimensional representation that apprehends essential features, structure, and relationships of data by training a single machine learning model.

Explainable AI (XAI):

Explainable AI refers to the process and methods that allow humans to comprehend and trust machine learning algorithm-generated outputs. It is used to describe potential biases and impacts of AI models.

For AI decision-making to be successful, businesses must know how the AI decision-making process works, with model monitoring and accountability of AI.

In simple terms, XAI helps humans to understand the predictions and decisions made by the AI model. It differs from the “black box” machine learning, where even its designers can’t explain why the AI reached a decision. Instead, Explainable AI helps signify an AI-based decision's accuracy, transparency, fairness, and outcomes.

Human-Centric AI:

Human-Centric AI is an emerging practice aimed at creating AI systems that amplify and augment instead of replacing human abilities. HCAI will preserve human control while ensuring AI meets our needs by working transparently, respecting privacy, and producing unbiased outcomes.

Simply put, HCAI combines artificial intelligence, machine learning, and human-centered design to transform how businesses use, operate, and take advantage of data acquired without creating new algorithms for new jobs.

Edge AI:

The blend of artificial intelligence and edge computing is what we call Edge AI. Edge AI is an AI technology that brings the computational power of AI algorithms to the edge of the network rather than relying on a centralized cloud computing infrastructure.

The AI models used in edge AI are typically lightweight and optimized for a specific task, such as object detection, speech recognition, or natural language processing. As a result, edge AI improves the response time speed, increases privacy and security, reduces network latency and bandwidth usage, and makes edge computing cost-efficient.

AutoML:

Automated machine learning refers to the processes and methods that improve Machine Learning efficiency and make it available to non-ML experts. AutoML is used to apply the ML model to solve real-world problems through automation. It makes ML processes user-friendly and provides more accurate output than manually written algorithms.

Data preparation, Feature engineering, Ensembling, Model Selection, Hyperparameter Optimization, Pipeline Selection, Problem Checking, and Result Analysis are some ML processes that can be automated through AutoML.

Quantum AI:

As the name suggests, Quantum AI combines quantum computing and artificial intelligence. By using quantum AI, scientists can achieve results that conventional computers would not be able to achieve. The aim of quantum AI is to create algorithms for decision problems, learning, searching, game theory, etc., that work better than classical ones.

Federated Learning:

Federated Learning focuses on training machine learning algorithms across multiple decentralized data servers or edge devices without changing them. Multi-actor machine learning models can be built using Federated Learning without sharing data, which allows it to address critical issues such as data privacy, security, and access rights.

AI-powered CyberSecurity:

With the growth of cyber threats in complexity and volume, AI can help security operations analysts stay ahead by curating threat intelligence from millions of news stories, research papers, and blogs on cyberattacks. In addition, AI can provide insights to improve response times drastically. What makes AI-powered cybersecurity a trend is its ability to continuously learn, identify threats faster, and remove time-consuming tasks.

Sustainable AI:

Sustainable AI is devising energy-efficient, highly accurate, and explainable machine learning algorithms that support data processing from distributed sources.

In simple terms, it refers to the use of AI technologies that are socially and environmentally responsible. Sustainable AI aims to create AI systems that can reduce carbon emissions on the environment while empowering long-term viability and ethical use.

Moreover, to achieve sustainable AI, developers must consider various factors such as energy efficiency, data privacy, bias and fairness, and transparency.

Autonomous AI:

Autonomous AI refers to creating AI systems that can operate autonomously and make independent decisions without human intervention. These AI systems can learn and adapt using data analysis and machine learning algorithms to improve performance. Some popular examples of Autonomous AI systems are self-driving cars, robots, and drones that don’t require human intervention.

Takeaway

We live in an era of artificial intelligence. Several industries are about to be revolutionized by artificial intelligence, which is advancing exponentially. Needless to say, AI will continue to evolve, integrate more seamlessly into everyday life, and transform how we work, live, and interact.

Front-end Developer Roadmap

Priyanshi Sharma — Wed, 18 Jan 2023 05:37:34 +0000

You might have gone through many websites and web apps ever since you got your smartphone and personal computer. And we are pretty sure that the first thing that you notice before moving further with any website is its look and feel.

Obviously, no one wants a web app with a confusing and unappealing user interface.

The one who is responsible for making the web app look better and user-friendly is none other than a front-end Developer!

And if you are one of those folks interested in building their career in front-end development, you are at the right place.

You might be wondering what front-end development is and what a front-end developer does.

No worries, today we are about to cover everything that you need to know to become a front-end developer.

So without further ado, let’s get started!

Front-end Development: An Overview

Any website, web app, mobile app, or desktop app is categorized into two parts: front-end (client side) and back-end (server side).

While the back-end covers the work and logic behind the screen, the front-end is the part of the website visible on your screen including graphics, text, menus, audio or video elements, buttons, toolbars, dialogue boxes, themes, customizations, and other features.

Responsibilities of a Front-end Developer

A front-end developer is responsible for developing and designing a website’s user interface. Put simply, the role of a front-end developer is to ensure that user interaction with the website is hassle-free.

Analytical thinking, out-of-the-box creativity, and good technical knowledge are some must-have criteria.

Some of the key responsibilities that a front-end developer has are:

Turning design concepts into reality
Determining structures and designs of the web page
Making web design secure, scalable, responsive, and reliable
Adding better features for better UX
Testing the usability of the website interface and fixing bugs
Creating a balance between aesthetic and functional design
Ensuring that the website is mobile friendly
Building reusable codes for future
Optimizing the page speed of the website
Maintaining brand consistency
Using markup languages to create functional web pages.

Basic Roadmap to Become a front-end Developer in 2023

Now that you are much aware of the roles and responsibilities of the front-end developer as well as what front-end development is. It’s time to move ahead and discuss the roadmap one needs to follow for a successful career in the front-end development market.

The most important thing to remember is that to become a front-end developer you don’t need any specific qualifications. All you need to do is make your command over required technologies and programming languages with practical exposure.

Understand the Internet

Internet refers to the global network of interconnected computers that uses internet protocol to communicate with other devices and networks. And to be a front-end, back-end, or even full-stack developer, you need to understand how the internet, browsers, DNS, Domain name, and hosting work, and what is the role of HTTP (HyperText Transfer Protocol).

Learn Programming Language for Web

Developers use programming languages to communicate with computer systems. And these languages act as a set of instructions written in a certain language to help the computer understand the task you want to perform.

HTML, CSS, JavaScript, and jQuery are the programming languages used by front-end developers to create web pages.

So to become a front-end developer, the most crucial task is to learn the basics along with writing semantic HTML, conventions, best practices, forms, validations, etc.

Other than that a front-end developer also needs to learn about accessibility, layouts, responsive designing, DOM manipulation, ES6, modular JavaScript, hoisting, scope, event bubbling, strict, Shadow DOM, and the basics of SEO.

Acquire Basic Knowledge of Version Control Systems
Version control is the system used by developers to keep track of all the changes made in the source code of the software development project over time.

It also stores information like who made the changes and what changes were made by creating a separate branch for every contributor. Moreover, these changes are not merged with the original source code before they get analyzed.

Moreover, if the updated version does not work as expected, it allows you to omit the new changes and roll back to the previous version.

Some of the popular version control systems you can learn about are BitBucket, GitHub, and GitLab.

Download Roadmap: PDF

Gain Web Security Knowledge

Web security is the part of information security that deals with the security of web apps, websites, and web services. It refers to the security measures taken by developers to protect their web apps or websites from cyber attacks.

To design the front-end of a software application, you need to get basic knowledge of HTTPS (Hypertext Transfer Protocol Secure), CORS (Cross-Origin Resource Sharing), OWASP (Open Web Application Security Project) Security Risks, and Content Security Policy.

Learn About CSS Preprocessors and CSS Architecture

As a front-end developer, you have to learn different technologies that make working on the project much easier. A few of these technologies and tools you need to know are CSS preprocessors and CSS Architecture.

To help you understand, we have defined each of these technologies below.

While CSS Preprocessors like SaSS and PostCSS are an extension of CSS that comes with advanced features to be used by developers to create projects with more features easily. They allow developers to use logic like nesting, mixins, inheritance, variables, and mathematical operations in the CSS code.

It can be difficult to deal with CSS in large, continuously iterated, complex systems. However, some CSS Architecture methodologies allow writing more manageable CSS like BEM ( Block, Element, Modifier).

Understand Build Tools

Build tools are programs through which the creation of executable apps from source code can be automated. It includes compiling, packaging, and linking the code in an executable format. It helps in automating repetitive tasks that would otherwise waste a lot of your time.

As a front-end developer, you will need to know about build tools like package managers, task runners, module bundlers, and module loaders.

A package manager is a software tool collection that is used to automate the process of configuring, installing, updating, and removing application programs. Package managers handle the distribution of data and software in archive files - packages. Some of the package managers you must learn about are npm, yarn, pnpm, etc.

Task Runners runs off a configuration file and is used to automate repetitive tasks like testing, linting, and minification. Grunt and npm Script are the most popular examples of task runners. For linting, you can go for ESLint.

Module Bundlers are used to bundle pieces of JavaScript code and their dependencies into one file for browser use. While Module Loaders are used to interpret and load an already written at runtime. Some examples of module bundlers/loaders are Webpack, Browserify, Vite, esbuild, Parcel, Rollup, etc.

Pick a Framework

A software framework serves as a foundation for building software applications and is associated with a programming language. Frameworks differ in their functions and capabilities. It also helps in establishing rules and defining the structure of the application.

Although there are multiple frameworks in the market for front-end development, some popular ones are React, Angular, Vue.js, Svelte, Solid JS, etc. for JavaScript, and Mantine, Chakra UI, Daisy UI, Material UI, Radix UI, and Tailwind for CSS.

Learn to Test Your Web Apps

Before releasing the app in the market, it is important to ensure that all the required functionalities and features are working properly. Also, it is essential to identify any sort of unintended functioning in the app - called bugs. To check that, we test the application through different methods including integration testing, unit testing, and functional testing.

Cypress, Applitools, Jest, Playwright, react-testing-library, Selenium, Katalon, and Squish are some popular tools used for front-end testing of web applications. And as a front-end developer, you need to know at least one or two of them.

Understand Authentication Strategies

To verify the user's identity, a web app or website should have authentication tools/methods/technologies to grant access to a restricted resource. A few popular authentication strategies are Session-based authentication, Token based authentication, OAuth, Basic authentication, SSO (Single Sign On), JWT (JSON Web Token), etc.

Acquire Knowledge of Web Components

A suite of different technologies using which you can create reusable custom elements while keeping their functionality encapsulated from the rest of the code is web components. Some of these web components that you will need to know about are HTML templates, Shadow DM, ESModule, and Custom Elements.

Learn Typecheckers

Type checking helps developers to verify and enforce constraints of types in values. It ensures that the written code has fewer bugs by trying to catch type errors and removing them from the code. Typescript is one of the most used JavaScript type checkers used by developers.

Understand Progressive Web Apps

A progressive web app (PWA) is a website with enhanced features to function like a native, installed app on supporting platforms. PWAs are delivered through web built using front-end technologies.

To build a PWA as a front-end developer you need to have basic knowledge of storage, web sockets, server-sent events, service workers, PRPL pattern, RAIL model, performance metrics, Lighthouse, and DevTools. Knowing Web APIs for location, notification, device orientation, credentials, and payments is also required.

Learn Server-Side Rendering

SSR is the ability of an application to convert a static HTML file on the server into a completely rendered/interactive HTML page for the client. It also addresses the search engine optimization and performance issues of single-page web apps. Thus, server-side rendering is another technique that you must be aware of as a front-end developer.

Understand Static Site Generators

Want to build a static HTML website with a set of templates and some raw data? Instead of doing so manually, you can use static site generators like Astro, Next.js, GatsbyJS, Gridsome, Elevently, Jekyll, Hugo, Nuxt.js, Remix, and Vuepress to achieve your goals.

Learn GraphQL

It is an open-source data manipulation and query language for APIs (Application Programming Interfaces) that also acts as a runtime for processing queries with existing data.

Using GraphQL, you can make multiple resource requests in a single call, reducing the trips to the server and saving a lot of bandwidth and time.

Apollo is a platform that you can use with GraphQL to create a communication layer to handle the data flow between client and server.

Relay modern is yet another platform that can be used with GraphQL for fetching data in React apps.

Know Technologies used for Mobile Apps and Desktop Apps
While getting into front-end development, it is crucial to understand different technologies and their use cases, especially the ones you will have to use for mobile or desktop apps.

For example, React Native, Ionic, Flutter, and NativeScript are the frameworks that are used to build mobile apps. On the other hand, Electron and Tauri are used to build the front-end of desktop applications.

Others

Some other topics that you can cover to expand your knowledge in the front-end space are UI/UX, Design Systems, Visual Programming, Houdini, WebRTC, IDLs, and WebAssembly.

Conclusion

So that was the roadmap you need to follow as a front-end developer, we hope it has given you an idea of what to expect and which tools and technologies to learn more about. But remember that we have just scratched the surface and there are going to be many more tools and technologies in the future that you will have to learn.

Source: Decipher Zone

A Guide To Data Streaming Tools

Priyanshi Sharma — Mon, 29 Aug 2022 06:40:55 +0000

In today’s digitized world, everyone expects their data the second it is updated. Moreover, large-scale companies depend on this data to predict the supply and demand of their product in the market and anticipate user behavior for a better experience. Real-time data streaming has become an integral part of modern business strategy.

To stay ahead of the competition, businesses need to leverage the opportunities provided by data streaming platforms to make insightful decisions.

But, what do we mean by data streaming, and what are these tools that help with data streaming? What are the benefits and limitations of data streaming platforms?

To help you out, this blog will cover all that you need to know about data streaming tools. Without further ado, let’s get started.

An Overview of Data Streaming

Streaming data refers to the continuously generated data by numerous data sources. By using data streaming platforms, data can be stored, processed, analyzed, and used to derive valuable insights.

A data stream includes data like log files produced by users through eCommerce purchases, social media information, telemetry from connected devices, financial trading, in-game activity, or data centers.

This data needs to be processed consecutively and incrementally on either a sliding time window or over a record-by-record basis to be used for sampling, correlations, filtering, and aggregations. The analytics retrieved from the data give businesses visibility into different aspects such as server activity, users’ and products’ geo-location, service usage for billing, etc. It enables businesses to respond to upcoming situations quickly.

How Does Data Streaming Work?

As we have discussed before, businesses have multiple data sources that get channeled to different destinations. However, to process the data you can use data stream processing techniques like event-stream processing which includes small data chunks.

Streaming data enables data chunks to be processed in near real-time or real-time. Moreover, to be able to provide real-time data, streaming platforms should offer low latency, high scalability, and high availability. Real-time analytics and streaming media are the most common use cases of data streaming.

Although initially data stream was introduced for stock exchange values and media streaming, it is now being adopted by different businesses to process data in near real-time while allowing them to monitor and analyze different aspects of the business.

Benefits of Data Streaming Platform

The core aim and benefit of a data streaming platform is the ‘real-time insights’. But there’s much more that adds to the advantages a data streaming platform offers to its users. Some of them are:

Increased Customer Satisfaction
With data streaming, it becomes easier for a business to get insights into user behavior, and their likes as well as dislikes. Using these insights, an organization can make their online web application more engaging that converts the user into their customer with a custom-made user experience.

Better Return on Investment (ROI)
The capability to gather, analyze, and act on the data using data streaming gives a business a competitive advantage in the market. Real-time analytics makes a business more adaptive and responsive to customer needs, market trends, and opportunities, leading to an increase in ROI.

Reduced Infrastructure Cost
Large volumes of data are often stored in data warehouses in the traditional data processing. Organizations often have a hard time affording these storage systems and hardware. Since data stream processing doesn't store large amounts of data, processing systems have lower hardware costs.

Minimized Losses
With real-time data intelligence that comes along with data streaming, businesses can get warnings on upcoming problems in the application system including but not limited to financial downturns, system outages, and data breaches that can create a negative impact on the business. Using this insight, businesses can prevent the effect of such events.

Data Streaming Challenges

Data streaming systems have to deal with real-time, highly volatile, and continuous data which is often incomplete and heterogeneous that can pose several potential problems. Some of the challenges that data streaming platforms might face are as follows:

Due to huge data volumes, data streaming platforms may have to deal with damaged data packets.
If the data streaming system isn’t fast enough, the relevance of streaming data will reduce over time.
It is important for data streaming systems to provide high elasticity otherwise they will not be able to allocate resources according to the data volume leading to system failure or high latency.
Some data streaming platforms are not fault-tolerant, which can lead to downtime in data transmission from source to destination or analyzing real-time data that’s not good for a business.
Data in a streaming system may either come from the same or different sources. But, the data has to move through a distributed system leading to problems in the system like calling its data and transmitting it to the user.

Top 5 Data Streaming Tools

Now that we know about the data streaming platforms, their advantages, and their challenges, it is time to look at the top 5 data streaming tools that you should know about before working with a tremendous volume of data.

1. Google Cloud Dataflow
Google Cloud Dataflow is a fully-managed cloud-based streaming platform that is equipped with Python 3. It allows one to extract data from edge sources, define data pipelines, and process streams.

With Google Cloud Dataflow, developers can automate provisioning, and processing resource management. It also provides horizontal autoscaling of worker resources to maximize utilization. Besides, the processing of Dataflow is both consistent and reliable.

In simple terms, Dataflow allows a fast, simplified, low-latency data streaming pipeline, and removes operational overhead from the workload so that developers can focus on programming rather than managing server clusters.

Some of the key features of Google Cloud Dataflow include:

Flexible pricing and scheduling for batch processing
Resources’ autoscaling
Dynamic rebalancing of work
Smart diagnostics
Streaming engine
Flexible resource scheduling
Real-time change data capture

Besides, the billing in Google Dataflow is based on batch or streaming use per second.

2. Azure Stream Analytics
Azure stream analytics is an easy-to-use, real-time analytics platform designed for workloads that are mission-critical. It is an end-to-end pipeline that can be ready for production within a few minutes with extensible C# and JavaScript code and SQL syntax.

Being hybrid architecture, it helps businesses to run queries in both edge and cloud. The stream analytics of Azure comes with elastic capacity and scalability making robust streaming data pipelines that analyze millions of events in a fraction of the time.

3. Amazon Kinesis
With Amazon Kinesis, it becomes easier to gather, process, and analyze streaming data like audio, video, IoT telemetry, and application logs in real-time for analytics or machine learning purposes. It helps you get quick insights and the ability to act accordingly. Kinesis is cost-effective regardless of the scale of your business and provides you with the flexibility to choose the ideal tools for your project.

Kinesis is a fully managed tool that runs the streaming app without external infrastructure management. Moreover, it can handle large volumes of data from a plethora of sources with minimal latencies.

4. Apache Kafka
Apache Kafka is an open-source, distributed event streaming platform that is written in Scala and Java by Apache Software Foundation. Kafka aims to provide a high-throughput, unified, low-latency platform to manage data feeds in real-time.

Apache Kafka is used to develop real-time streaming applications and data pipelines that process and transfer data from source to destination while consuming data streams.

What makes Kafka different from other streaming platforms is its ability to store data streams in a fault-tolerant, distributed, and durable cluster that can be stretched or scaled easily over availability zones to provide higher availability.

5. IBM Stream Analytics
IBM Stream analytics assesses a wide range of streaming data to help businesses find the risks and opportunities to make real-time and impactful decisions.

With IBM Stream Analytics, developers can easily connect with any data sources and integrate with Spark or Hadoop. It also comes with an in-built for natural language, machine learning, text, acoustics, etc to help you create adaptive applications.

Using existing Python code for stream apps, Stream Analytics by IBM offers faster delivery of streaming applications. Besides, its speech-to-text toolkit allows you to build a web application that can store voice, convert it into text, and perform natural language processing on it to unlock voice insights with ease.

It has a rich visual IDE that allows you to develop applications using popular programming languages like Python, Java, and Scala easily.

Conclusion

So that was it for data streaming platforms. We hope you have found the blog insightful to help you understand the basic concepts as well as the data streaming tools that you can go for.

To conclude, data streaming refers to the communication between a sender and a receiver through either single or multiple streams of data. They also store, process, and analyze data to be used by businesses. And yes, data streaming also has its pros and cons like any other technology in the marketplace.

Although there are a multitude of data streaming tools that you might have come in touch with. The top data streaming tools that you can choose from are Google Cloud Dataflow, Azure Stream Analytics, Amazon Kinesis, Apache Kafka, and IBM Stream Analytics.

Source: Decipher Zone

Custom Software Development

Priyanshi Sharma — Tue, 23 Aug 2022 12:47:14 +0000

Whether you’re an entrepreneur, business owner, or a team of software developers, you know how crucial good software development and management is.

Software development has become a crucial business need since the advent of the digital economy. However, software development isn’t easy. It’s time-consuming and requires not only technical expertise but also deep knowledge of business and management practices. You need to hire an experienced team of software developers to deliver quality software. If you want to build, manage, and maintain a software development team in the long term, you need to know the basics of software management.

Fortunately for you, this guide is your comprehensive, up-to-date handbook for custom software development.
In this guide, you will learn:

What is custom software management?
Why is custom software management important?
How to build effective custom software?
Why Outsource Custom Software Development Projects?

What is Custom Software Development?

Custom software development is the process of designing, developing, deploying and maintaining software for certain businesses, functions, and users. Unlike off-the-shelf software, developing custom software is focused on defined requirements.

Although having a diverse spectrum, the software market often becomes unable to meet all the demands of modern management and business processes. It is not uncommon for businesses to demand unique features for their application.

Large-scale companies often develop custom software for important functions, including inventory management, human resource management, content management, customer management and so on. While the idea of developing custom software for your business may sound appealing, it isn’t best suited for everyone. The custom software development process can not only be expensive but also time-consuming.

Custom software is designed to fulfill certain requirements, like an online banking application that meets the unique needs of the bank and the customers.

Typically, custom software development is performed either by outsourced or in-house development teams. However, the processes that include requirement gathering, code construction, testing and deployment and methodologies like DevOps, Agile, or Rapid App Development are used in custom software development are the same as those used in other software development projects.

But why should one opt for custom software development for their business? Let’s find out!

The Importance of Custom Software Development

The importance of custom software development can be attributed to the ability to meet unique requirements at a cost-competitive rate with buying, handling, and changing commercial software.

Some advantages of developing custom software are as follows:

Scalability: As a business or an organization grows, so does custom software. During the requirements gathering process, developers and designers can assess future needs. Instead of purchasing additional licenses or subscriptions of packaged applications, these factors can be incorporated into the application.

Efficiency: With custom software, you won't have to mess with or adjust a COTS (commercial off-the-shelf) application to implement processes efficiently and effectively.

Lower integration costs: A primary concern when purchasing commercial software is: can it integrate with existing and legacy applications? If it cannot be integrated or work with existing apps, businesses will have to invest more money to get commercial software to communicate with existing infrastructure. However, custom software can be built to integrate with existing infrastructure.

Independence: The benefits of using custom software and being free from the vendor of commercial software can go both positive and negative ways. On the positive side, businesses can easily neglect the hike in licensing and support pricing or avoid getting stuck in maintaining packaged software if the vendor terminates a product or goes out of business. On the downside, the maintenance cost of custom software will completely fall on the business that had it developed. Organizations must carefully analyze each equation before proceeding with custom software development.

How To Build Effective Custom Software?

If you want to develop custom software for your business, here is the list of considerations that will help you assure the efficiency and effectiveness of your software.

Decide whether to build or buy

As a business owner, you can easily buy off-the-shelf software that matches your business needs, so it becomes even more important to make sure that you truly need custom software. One of the best approaches to analyse if you want to build or buy software is to check if there’s software in the market that can deliver more than 80% of the functions to:

Automate business processes or transactions unique to your business
Manage industry-specific data and information
Achieve the required level of security and privacy
Provide integration support for legacy applications and data
Replace or help in merging existing solutions at a lower cost to achieve better productivity
Easily adapt to changing requirements.

If that’s not the case, you can proceed with hiring developers for building custom software.

Requirements are required

Once you have decided to move ahead with custom software development, another thing to consider is requirements. As an organization, you must define and manage your business that can help you to assure that they are meeting the needs of your customers and at the same time addressing compliance while being on the budget and schedule. This will help in delivering a high Return on Investment (ROI).
A good requirement must be:

Technically and legally correct
Express complete idea
Explicitly clear leaving no space for confusion
Consistent with other requirements
Verifiable
Traceable
Feasible Modular so it can be changed without impacting excessively Independent of design.

Methodologies, technologies and practices

After defining the requirements of the custom software, the next step is to decide the methodologies, technologies and practices that will be used for development.

Development methodologies to consider:

Agile:
An agile methodology is an iterative approach to software development and project management that allows development teams to deliver value to their customers by working in smaller but usable augmentation.

DevOps:
DevOps (a blend of development and operations) is a set of practices that combines IT operations and software development together to shorten the system development cycle that includes design, development, deployment and support of software. Besides, different aspects of DevOps are derived from the agile methodology that increases the ability of the development team to provide continuous delivery of high-performing software.

Rapid application development (RAD):
RAD is a development model with a non-linear approach that prioritizes rapid prototyping and feedback over testing cycles to help developers make numerous iterations and updates of software without starting from scratch.

Scaled Agile Framework (SAFe):
SAFe is a collection of organisational and workflow principles that aid in the scaling of lean and agile approaches at the enterprise level. It facilitates communication, alignment, and delivery across a broad spectrum of agile teams.

Technologies and practices to consider:

Open-source
Open-source technologies are software or platforms that are distributed under a license that allows users to use, study, change and distribute the software and its source code to anyone and for any purpose.

Cloud-based development
Cloud-based development is the process of creating and developing software that makes use of cloud computing and hosts the development environment on the cloud. These cloud-based environments enable design, coding, integration, testing, and other development processes to be performed to build both cloud-native and on-premises apps with the ease, cost-efficiency and speed that the cloud promises.

Artificial Intelligence (AI)
With artificial intelligence, applications can learn and make decisions like humans. AI can be applied to improve the development process. For instance, natural language processing systems can be used to examine suggested improvements and requirements text according to some best practices.

Blockchain
Blockchain is a digitally linked, secure ledger that eliminates the expense and vulnerability introduced by parties such as banks, regulatory organisations, and other intermediaries. Blockchain technology can help financial sectors release money and improve corporate processes, so it's being used by developers to create secure transactional and financial apps.

Low code
Low code is a development practice that minimizes the requirement for coding and allows non-coders to develop or help in developing applications rapidly at a lower cost.

Analytics technologies
Using analytical technologies developers build software applications that can analyse a deluge of data through visualizations, dashboards and predictive capabilities and offer insights to the users. Moreover, cloud-based services, AI and APIs make it easier to integrate analytics into custom software.

Source: Decipher Zone

Event-Driven Microservices using Spring Boot

Priyanshi Sharma — Fri, 19 Aug 2022 07:33:00 +0000

With IT systems producing, gathering, and analyzing gallons of data, developers are expected to create complex applications that are highly scalable, resilient, and available more cheaply and quickly.

Different architectures and programming paradigms like DevOps, microservices, and event-driven architecture get used to achieving these goals. Moreover, developers are now combining microservices with event-driven architecture to make project delivery exceptional by making applications highly concurrent, scalable, fault-tolerant, and easy to maintain.

Although event-driven microservices architecture might sound easy to implement, understanding different technologies and methods to make it successful can be challenging.

So, in this blog, we will discuss the basic concepts of event-driven microservices and how you can implement them using Spring Cloud Stream.

An Overview of Microservices & Event-Driven Architecture

Microservices is not a layer in a monolithic architecture. Microservices are self-contained, distributed, clear-interfaced, de-coupled, independent services that perform well-defined tasks. Therefore, microservices can be built, tested, and deployed independently.

Microservices have been widely adopted by development teams for monolithic projects that need to be broken down into self-contained, independent services. Put simply, microservice is the architectural style to develop an application as a small services suite where each service has processes of its own. APIs allow these services to communicate with one another.

Event-Driven Architecture has existed for a long time. In an event-driven architecture, the application’s decoupled services are triggered by events to communicate. An event refers to the changes in the state by actions performed by users.

As the components that send notifications don’t know about receiving services’ identities while compiling, EDA is more loosely coupled than the client and server paradigm.

An event-driven architecture consists of event producers and event consumers. While event producers create streams of events, event consumers listen for the events. This architecture might work using either event streaming or the pub/sub model.

In the Publish/Subscribe model, the messaging infrastructure keeps track of subscriptions that helps in sending the event to the subscriber once it’s published. However, once the event is received and read by the subscriber, it cannot be replayed so the new subscribers will not see the previous events.

In the Event Streaming model, events are written to a log and are strictly ordered as well as durable. It helps clients to read from any part of the stream instead of subscribing to it.

To make it easier to understand how Microservices and Event-Driven Architectures compliment each other:

Microservices	Event-Driven Architecture
Bounded context which provides separation of concerns.	Loose coupling between components/services.
Supports polyglot programming.	Processing components that can be developed independently.
Services can be deployed and scaled independently.	Allows independent scalability of components.
Microservices are cloud native.	EDA has a high cloud relationship.
Has elastic scalability.	Has asynchronous nature and ability to throttle workload.
Better observability to detect failures.	Better resilience and fault tolerance.
Provides a repository of reusable implementation patterns.	Provides proven enterprise integration patterns.

By combining event-driven architecture with microservices, developers can develop highly scalable, distributed, fault-tolerant, available, and extensible systems.

What are Event-Driven Microservices?

In the event-driven architecture, a microservice/service publishes an event for communication when something crucial happens. Once the event is published by a microservice, other microservices also subscribe to those events. After receiving an event, the microservice can update its business entities leading to more events being published. Now, these events can be used to implement transactions to span different services.

A transaction in event-driven microservices consists of multiple steps where each step includes microservices updating the business entities for publishing events to trigger consecutive steps. Topics and queues are used for the exchange of events between microservices. Since microservices subscribe to queues and topics, at the time an event is published on the messaging service, the targeted microservice gets notified immediately to perform the requested tasks without external invocations.

The event-driven microservice architecture enables developers to implement transactions that traverse multiple services and deliver consistency. With event-driven microservices, material views can be maintained in an application.

Although event-driven microservice architecture is great, without a proper framework to write the source that helps in working with event messaging platforms, it can become messy.

For instance, if you want to write the logic for the event-driven microservices application, the boilerplate messaging code can make the work more difficult.

So, which framework can bring you out of the mess? Let’s check that out in the next section.

Spring Cloud Stream: The Solution

Spring Cloud Stream is a framework that is used by Java/Kotlin developers to build highly scalable event-driven microservices that are connected to the shared messaging system. It is a flexible model built on previously established Spring Boot best practices and idioms.

It offers flexible messaging abstraction that will handle the complex integration of messaging platforms and allow you to focus on the clear logic of a business. It brings a plethora of messaging platforms like Apache Kafka, RabbitMQ, Google Pub/Sub, Azure Event Hubs, Amazon Kinesis, and Apache RoketMQ behind one easy-to-use API.

Spring Cloud Stream also removes any minor differences in features or approaches between these platforms, so that you can create innovative event-driven applications.

Put simply, Spring Cloud Stream is a framework by Spring that abstracts boilerplate code and setup messaging services to enable developers to focus on the business logic and put it on the topic/queue without writing lengthy codes for setting up these messaging systems.

The core building blocks of SCS are Destination Binders, Destination Bindings, and Messages. While destination binders provide integration with external messaging systems, destination bindings bridge the gap between application code and external messaging systems. Finally, the message block is the canonical data structure that is used to communicate with Destination Binders by producers and users.

Running The Event-Driven Microservices Demo Application

Before running the event-driven microservices application, you need to install the latest versions of Java, Docker (to locally run Kafka or RabbitMQ), Git (optional), and Bash/Powershell on your PC.

Start with cloning the code repository from GitHub by opening a new terminal window and writing the following command:

git clone https://github.com/benwilcock/spring-cloud-stream-demo.git

This repository by Spring will contain two microservices: Loansouce that will act as an event message source and Loancheck acts as a loan processor.

Now, in a fresh terminal, move to the project’s root folder and write the command as follows to start Kafka:

./start-servers.sh

Then write -Pkafka to choose Kafka as a messaging platform.

Open a new terminal window, make /loansource current directory through cd and write the command as follows to start the microservice:

./mvnw clean package spring-boot:run -DskipTests=true -Pkafka

In a new terminal window, make /loancheck the current directory and write the following command to process the events of loan application:

./mvnw clean package spring-boot:run -DskipTests=true -Pkafka

Once this app starts, you will see a message stating Pending before it gets approved or declined.

Now that you have processed both microservices of the loan app, press Ctrl-C in each terminal window to stop the demo.

Conclusion

So that was all for event-driven microservices, and we hope you enjoyed the blog.

To conclude, developers can combine microservices and event-driven architecture to create a web application that is distributed, fault-tolerant, highly scalable, highly available, and provides high throughput. These event-driven microservices applications will be able to process a vast amount of data.

Source: Decipher Zone

Understanding Continuous Testing

Priyanshi Sharma — Thu, 18 Aug 2022 06:25:17 +0000

Getting fast feedback on the impact changes brings to the software development process is essential to developing a quality application.

In the good old days, developers used to work using the Waterfall methodology to build software. In the waterfall model, the software development gets divided into different phases, where the outcome from one stage becomes the input for the next. Although this model was easy to understand and manage with clearly defined phases, the main disadvantages of the waterfall model are the high amount of uncertainty and risks and the difficulty in measuring progress or making changes to develop complex and object-oriented applications.

Put simply, the waterfall model will have a different team for quality analysis in the very end, making it difficult to identify bottlenecks, bugs, and errors.

However, in today’s fast-paced digital world, where customer expectations are never-ending, businesses need to deliver web applications as fast as they can without hindering their quality.

Developers have been introduced to CI/CD (Continuous Integration/Continuous Delivery), Agile, and DevOps, which brings fast feedback loops, immediate responses, and fast adaptability to market changes through test automation and Continuous Testing.

So, in this blog, we will discuss the concept of continuous testing, why you need it, methodologies, and tools you can use.

Continuous Testing: An Overview

Continuous testing is the process of automating tests to get fast feedback on business risks at different software development life cycle (SDLC) phases for better efficiency while managing deployments.

Continuous testing plays a crucial part in the CI/CD process and improving the timeline of SDLC by avoiding bottlenecks, better code quality, and accelerating processes in DevOps. Continuous Testing involves test automation that addresses the increased complexity and speed of software development.

Some of the key points to understanding continuous testing are:

Its primary goal is to analyze business risk coverage.
It provides immediate feedback on the risk of release before it proceeds to the delivery pipeline.
It establishes safety to protect user experience by avoiding software failures in accelerated development processes.
It has an easy integration into the DevOps toolchain and software delivery pipeline.
It covers everything from shift left to shift right including unit, integration, coverage, monitoring, and testing in production.
It involves running the right tests at the right delivery pipeline stage to avoid bottlenecks.
It evaluates every modern architecture layer, reduces false positives, and continuously reviews the test suite to eliminate redundancies.

Put simply, continuous testing leverages test automation that loads pre-defined QA scripts at all the production stages. These QA scripts minimize the interventions from humans at the time QA tests are executed to validate the efficiency of source code while providing immediate feedback to the team.

If in case the test automation fails, the development team gets a notification to make adjustments to the source code before it affects other functionality and features at different SDLC stages. On the other hand, if the test gets executed successfully, projects are transferred to the next stage automatically so that the development team can work on creating a reliable solution that maximizes coordination and productivity.

Pros of Continuous Testing

Implementation of continuous testing in the software development life cycle has a multitude of benefits for the business. The following are some of them that we will discuss:

Better Error Detection & Resolution
The multilayered and multifaceted architecture of web applications makes it difficult to identify errors and resolve them. However, with continuous testing, developers can break down these complexities through an automated, scalable testing solution that remarkably helps to improve error detection and rectification.

Reduced Disruption for Business
In an interconnected application system, an error in a module can cause unwanted downtime for the entire application, highly impacting productivity. However, implementing continuous testing at the granular level of the large application systems can help identify errors that might be invisible at higher levels. Continuous testing also helps businesses to avoid disruptions and additional costs to fix those disruptions.

Higher-Quality Deployments
With continuous testing comes automated methods for quality assurance management between different stages of SDLC. With continuous loops of feedback on unit testing and user modules, development teams can get actionable insights to improve the performance, quality, and compatibility of the code before it is deployed to production. It also helps to overcome disconnection issues between different DevOps teams leading to faster and high-quality product deployments.

Improved User Experience
With the insights provided by continuous testing to troubleshoot different user interface scenarios, developers can easily remove inefficiencies in the user experience while minimizing any unwanted disturbance after deployments.

Continuous Testing Methodologies

Continuous testing has multiple methodologies when it comes to ensuring the security, reliability, usability, performance, and operability of an application. These continuous testing methodologies that you must know are:

Shift-Left testing: In the shift-left approach, early system testing is prioritized to reduce significant debugging problems in the software development life cycle.

Shift-Right testing: Testing the product near its SDLC end is prioritized in the shift-right testing. This approach helps developers to improve overall performance, fault tolerance, user experience, and functionality of the web application system.

Smoke tests: Smoke tests, also known as build acceptance or verification testing, is a non-exhaustive analysis of the application that ensures the working of crucial functionalities. It is the preliminary software test that is done after build and before deployment. Moreover, it doesn’t delve into minor details but still offers an inexpensive solution to remove any major error in the software.

Unit testing: In unit testing, the smallest set of code that can be isolated from the system logically is tested to determine its usability. Unit testing is ideal for small-scale load, stress, memory leaks, or volume across the builds to detect any degradations at early SDLC stages.

Integration and messaging testing: This test is used to check bugs when different modules of the software are working together. It virtualizes missing dependencies to let the team test the performance of end-to-end processes simultaneously.

Performance testing: It is the process of testing and analyzing the capabilities and quality of the software in terms of stability, reliability, and speed under different workloads.

Functional testing: It is a type of black-box testing that analyzes if the functional workflows are executing as expected and meet the user's expectations or not. For example, in a B2C software like Amazon, the system should be able to track the product availability for shipment and delivery.

Regression testing: It is the test that involves re-running the non-functional and functional tests to ensure the system is working as expected even after a change. If it doesn’t pass the test, that would be known as regression.

User-acceptance testing: It is the final stage of testing the application before it is launched in the market for targeted users. In user-acceptance testing, the application is tested in real-world environments by some intended users to check if there are any bugs or errors in the product.

How to implement continuous testing?

With the basic knowledge that we have of continuous testing and the testing methodologies involved, it is time to understand how one can implement continuous testing into their projects.

Needless to say, if you want to develop high-quality application software, you must run both manual and automated tests throughout the software development life cycle. It will help in validating the architecture and functionality of the developing software.

Besides, research has shown that a team does better when:

They allow tests to work simultaneously with the developers throughout SDLC.
They allow developers to perform manual tests throughout the delivery process.

Developing and maintaining an automated set of test suites like Unit tests and acceptance tests are crucial activities for continuous testing.

In a deployment pipeline, every change runs a software package build and executes unit tests and other checks. If these packages clear the first stage, automated acceptance tests are run to check the performance and vulnerabilities of the software. In case of no errors in automated tests, manual tests are done before considering the web app releasable in the real world.

By running continuous tests as part of the pipeline, the development team can gain immediate feedback leading to faster delivery and lower bugs in the production environment.

Moreover, if the project has come to an end of the deployment pipeline and the team doesn’t feel that the product is ready for release yet, or they discover new errors in production, then they must improve the pipeline by updating or adding tests immediately.

Tools for Continuous Testing

Now that we know the ways to implement continuous testing into the project, let’s take a look at a few of the automation testing tools that you can use.

Travis CI
Travis CI is a continuous integration testing tool that can be used to test and deploy software projects hosted on GitHub or Bitbucket. It also offers on-premise and hosted variations to choose from.

Selenium
Selenium is the most widely used open source automation testing tool for Web UI. It offers tools for authoring functional tests across different web browsers including Chrome, Safari, Microsoft Edge, and Firefox.

Jenkins
Jenkins is an open-source, automated, continuous integration tool used for the Java programming language. It helps automate testing, building, and deploying the software development while facilitating continuous integration and delivery. Being a server system, Jenkins runs in servlet containers like Tomcat.

Conclusion

Continuous testing isn’t a choice for software development anymore, it has become the de facto for a successful project launch. In simple terms, to be successful in the market, modern businesses need to leverage continuous testing with the right tools and methodologies.

Source: Decipher Zone

Spring Boot: Choosing Right Tools, Version & Programming Languages

Priyanshi Sharma — Tue, 16 Aug 2022 06:56:49 +0000

Spring Boot is a popular framework for developing scalable web applications. Spring Boot is an opinionated, open-source, Java-based framework developed by Pivotal and used for creating production-grade, standalone applications that developers can just run. Spring boot provides a simple and efficient way to set up, build, and deploy web applications. It adds the Rapid Application Development (RAD) feature to the Spring framework that requires minimal configuration to run an app.

Although creating web apps with Spring Boot is relatively easy, choosing the right tools, programming languages, and versions can be tricky sometimes.

So in this blog, we will cover the basics of Spring Boot along with the IDEs, versions, tools, and languages you should be familiar with before getting started with Spring Boot app development.

An Overview of Spring Boot

As a combination of the Spring framework and Embedded Beans, Spring Boot does not require XML configuration to build a web app, reducing the efforts of developers.

Spring boot offers a wide range of benefits for web app developers, including

Embedded Servers
Spring has pre-built Jetty, Tomcat, and Undertow application servers. So, Spring Boot does not require any server installation, making fast and efficient deployment.

Auto-Configuration
Third-party frameworks, as well as Spring, are automatically configured in Spring Boot.

Standalone Application
Spring Boot can easily create a jar for the application to run with no deployment customization.

Starter Dependencies
Spring Boot provides ‘starter’ dependencies that simplify configurations and efficiently manage the project object model (POM).

Production-Ready Features
Spring Boot has several features that help manage and monitor the application after deploying it to production. Using HTTP endpoints with Secure Shell, Java Management Extensions (JMX) API, or Telnet, you can audit and manage the metrics and health of the web application.

Spring Boot works in layer architecture that includes:

Presentation Layer
The presentation layer is the top layer consisting of views/front-end of the web application. It handles HTTP requests, converts JSON parameters into objects (vice versa), authenticates requests, and sends them back to the business layer.

Data Access Layer
This layer consists of CRUD (create, read, update, and delete) operations on the database.

Service Layer
As the name suggests, a service layer includes service classes that use the services offered by the data access layer.

Integration Layer
The integration layer consists of different web services available on the internet and leverages XML messaging.

Another thing you should know about the Spring boot framework is that it provides a feature of dependency injection, allowing objects to define their dependencies that will be injected by Spring Container later. Dependency injections let developers build loosely coupled, modular applications, ideal for distributed applications networks and microservices.

Within the Spring Boot framework comes built-in support required by a web app to perform specific tasks, including validation, data binding, exception handling, type conversion, event management, resource allocation, and many more. Spring boot also has technologies like Java Web Services, Advanced Message Queuing Protocol (AMQP), Remote Method Invocation (RMI), and others to create cross-platform Java EE web applications. These web apps run in any production environment regardless of the operating system.

Now that we know the basics about Spring Boot, we can move on to the tools, languages, and versions that will be ideal for your future projects.

Maven or Gradle?

To choose between Maven or Gradle, you first need to understand what they are and what makes them different from each other.

Maven
Apache Maven is one of the popular choices for developers of automation systems that have been around for two decades. Maven is a Yiddish word that refers to the accumulator of knowledge. The core aim while developing Maven was to simplify the Jakarta Turbine’s build processes. It helps developers to understand the entire development state in the shortest time by providing a uniform build system, making the build process easy, providing quality information about the project, and empowering better practices for project development.

Let’s create a pom.xml file that will include all the plugins and dependencies required for the maven project.

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>2.6.10</version>
<relativePath/> <!-- lookup parent from repository -->
</parent>
<groupId>com.trial</groupId>
<artifactId>trial</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>demo</name>
<description>Spring Boot Trial project</description>
<properties>
<java.version>17</java.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>

<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
</dependencies>

<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<configuration>
<excludes>
<exclude>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
</exclude>
</excludes>
</configuration>
</plugin>
</plugins>
</build>

</project>

While executing the Maven Command, developers can specify a “phase” to complete. It will complete any task, including removing prior output, compiling, running, or packaging the application.

Maven creates and anticipates a certain structure of the project by convention.

However, if somehow the conventions or tightly structured methods of the Maven project become highly complicated and constrictive, you can go for Gradle.

Gradle
Gradle is another build automation tool like Maven that supports multiple programming languages software developments. Using Domain-Specific Languages (DSL), Gradle creates a flexible and minimal build.gradle file as follows:

plugins {
id 'org.springframework.boot' version '2.4.0'
id 'io.spring.dependency-management' version '1.0.10.RELEASE'
id 'java'
}
group = 'com.trial
version = '0.0.1-SNAPSHOT'
Maven or Gradle? | 9
sourceCompatibility = '17'
repositories {
mavenCentral()
}
dependencies {
implementation 'org.springframework.boot:spring-boot-starter'
testImplementation 'org.springframework.boot:spring-boot-starter-test'
}
test {
useJUnitPlatform()
}

Gradle also has a DSL (domain-specific language) for describing builts in one of the two programming languages, i.eGroovy and Kotlin. Gradle also has features like Java incremental classes compilation, Java compiles avoidance, and a dedicated daemon for product compilation, reducing the build waiting time.

Before you decide whether you want to go for Maven or Gradle, it is important to try each of them and find the ideal one for your project.

Java or Kotlin

Both Java and Kotlin are popular for Java Virtual Machine (JVM). But, it can be confusing to choose the one for your project. To help you out, we will look at each and decide which one might be better for you.

Java:
Java is a class-based, object-oriented, high-level programming language that has been around for more than two decades. It has been designed to have as few dependencies in the software as possible. It’s anything but inactive, though. Since the year 2017, Java has been on regular updates every 6 months, improving its features continuously and making it more vibrant than ever.

Java creates a strong foundation of Spring codebase and therefore is the significant choice for developing Spring Boot applications. With Java’s extensive API libraries, you can build cloud-native, complex, and middle-tier applications efficiently. Besides, Java is an easy-to-use language, i.e., you can easily write, compile, run, debug, and maintain. It also enables developers to create reusable code and modular programs.

Kotlin:
In comparison to Java, Kotlin is new in the market. Kotlin is a statically typed, cross-platform, general-purpose programming language. It was designed to work with Java. Moreover, its JVM version of the standard library depends on the Java class library but the type interface in Kotlin allows the syntax to be more concise.

The core aim of Kotlin was to fulfill the gaps perceived in the usability of Java. From the initial stage, it was built to be safe, concise, interoperable, and tool-friendly, making Kotlin a fluent language to work with.

Kotlin’s ability to define your language within a language makes it easier to create APIs and if we combine this feature with the concision of Kotlin, developing Spring Boot applications can become shorter and more readable than Java.

The most incredible part between Java and Kotlin is that you don’t have to choose. Moreover, Kotlin and Java compile to the same bytecode output. So you can create Spring projects using both Kotlin and source files of Java and call compilers for both. Therefore, you can choose the one that makes more sense to you even in the same project.

Choose Spring Boot Version

It is essential to choose the latest version of spring boot at the time of application development. However, there are some narrow and temporary exceptions where you can use older versions of Spring Boot. These exceptions are

If you are using older versions of Spring Boot while upgrading, deploying, and retesting the application in a way where you somehow haven’t reached the older version yet.

If you want to use features in a milestone, snapshot, or release candidate pre-general availability version and are willing to take risks inherited with code that is not yet ready for production use.

If you are running an older version of Spring Boot but there is a bug or conflict that has been reported to Spring Team and they asked you to wait for an update.

Integrated Development Environment

No matter which language or build automation tool you are using to build the Spring Boot project, you will need an integrated development environment (IDE) for writing code to make the software useful.

Although there are a plethora of IDEs in the market, the top three IDEs that we will discuss include

IntelliJ IDEA
Developed by JetBrains using JAR-based languages like Java, Groovy, and Kotlin. IntelliJ IDEA is among the top three Java IDEs. It is available as Apache 2 licensed community edition and a proprietary commercial one that you can choose from.

With intelligent coding assistance and minimal design, IntelliJ IDEA makes software development productivity. It provides instant and relevant code suggestions, on-the-fly code analysis, and tools for refactoring.

Apache NetBeans
NetBeans is an Apache project that aims to offer rock-solid web app development by addressing developers and business needs. Rather than being a mere text editor, NetBeans highlights the code both semantically and syntactically. It also allows you to refactor the source code with its in-built tools.

NetBeans is platform-independent and provides wizards, editors, and templates to easily develop web applications using different programming languages. It is open source and has incredible version control for a better development experience.

Eclipse
Eclipse is the most popular open source IDE that has been written in Java and C languages. Although it is primarily used to develop Java applications, it can be used with several programming languages like C, C++, C#, Erlang, JavaScript, PHP, Perl, Python, Rust, Groovy, Scala, etc.

Eclipse majorly includes a development environment and a plug-in system that helps add new functionalities and features to the IDE. It offers an incredible visual code debugging tool, has an easy-to-use interface, and supports project development frameworks for source navigation and toolchains.

Apart from these three IDEs, you can also go for Atom, Sublime Text, Visual Studio Code, and more to create a Spring Boot application. But before you make any choice, we will suggest that you give each one of them a try once. This will help you find the one that suits your requirements the most.

Conclusion

So that was all about the tools, languages, versions, and IDEs that you can leverage while developing a Spring Boot application for your business. We hope it has provided you with basic knowledge of Maven, Gradle, Java, Kotlin, Atom, VSCode, IntelliJ, Eclipse, and NetBeans.

Source: Decipher Zone

Deploy Microservices Using Serverless Architecture

Priyanshi Sharma — Tue, 15 Mar 2022 11:07:31 +0000

Businesses around the world are moving away from large and complicated monolithic applications to more flexible systems that can adapt according to the changing needs of the market - Microservices.

Microservices continues to be a trending topic in the world of web application development because it’s easier to build and scale compared to monolithic architecture. Development teams can build standalone functional services within a microservices model that can be called by other services whenever necessary.

But what’s the need for serverless architecture in microservices? Why should we deploy microservices serverless? Where can serverless microservices be used?

Let's move on to the blog to get all the answers you might need.

Defining Microservices and Serverless Architecture

Microservice architecture involves splitting up monolithic applications into a set of independent, loosely coupled services that can be independently deployed, owned and managed by small teams, and organized around business capabilities. The microservices architecture allows the frequent, rapid and reliable delivery of complex applications.

By associating microservices to fulfill certain tasks, a business can gain agility and greater capabilities to change or update a microservice as needed. Additionally, it can be used to eliminate or replace services without negatively impacting the overall workflow of the application.

When we rely on one service instance, a failure of that instance will have a catastrophic effect on the composite. With the increase in the use of cloud services and platforms, the traditional dependency on the physical platform is no longer ideal.

With the advent of orchestration tools, developers can provision microservices without having to understand underlying attributes. These orchestration systems also provide abilities to use the metadata that defines the use of resources. But that doesn’t deal with all issues related to microservices.

To eliminate the problems in microservices requires complete abstraction from physical attribution, and that’s where Serverless architecture comes in.

Serverless (Function-as-a-Service) is a cloud-native model allowing developers to build and deploy applications without managing the servers. Serverless doesn’t mean that there are no servers, there are servers but these servers are abstracted away from the development of the application. In serverless, the cloud provider handles the provisioning, scaling and maintaining the infrastructure. All the developers have to do is package their code in containers for deployment. In Serverless, apps can execute parts of their code on demand and invoke functions by using event triggers.

The Serverless Approach and Its Benefits for Microservices

The serverless concept refers to the automating of all services on a platform. It not only offers highly responsive resource flexing but also event-driven interactions. Using serverless architecture, microservices are deployed in the cloud environment offered by cloud service providers. The cloud providers use virtual machines or containers to isolate the services. Events such as API requests or file upload triggers code and when the assigned task is completed, the server goes idle until triggered again.

Development teams can run multiple instances of similar services across different data centers with serverless architecture. A serverless service is independent of the storage, the network or the CPU.

There are different serverless deployment environments like AWS Lambda, Azure Functions, and Google Cloud Functions that can be used by developers for their microservices applications. Although each one of them have similar functionalities, AWS Lambda provides the richest set of features that we will discuss in a matter of time.

But first let’s understand a bit about serverless microservices, their use cases and the challenges it causes.

Serverless Microservices

Serverless microservices are cloud-based services using serverless functions to complete a specific role in the application. The small segments of code that are executed by serverless functions as a response to the event triggers can be easily scaled and moduled which is highly suitable for microservices architectures based applications.

Some of the benefits of using serverless microservices include:

In serverless microservices, you don’t need undifferentiated, time-consuming heavy lifting to manage low-level infrastructure. So, the developers can focus on writing code instead.
The serverless microservices are extremely elastic as it facilitates automatic scaling of the services, depending on the load.
Instead of provisioning underutilized servers or containers, you simply pay for the requests you make.

Use Cases of Serverless Microservices

Serverless microservices include advantages of serverless architecture including improved cost-efficiency, less overhead. But its primary goal is to help you in combining managed services with serverless functions.

It allows you to easily integrate message queues, databases, API management tools with functions. Therefore, once you have used resources and functions in a microservice, they can be used as the basis of other microservices.

Serverless microservices are the best fit for complex and modern applications so that they can be easily managed and scaled. Further, if you are able to divide your app into small, independent services that can be converted into event-driven, short-running tasks, you can use serverless microservices too.

However, if your application receives continuous load and runs tasks that run for long then they might be better as monolithic applications.

Serverless microservice using AWS Lambda

AWS Lambda is securely integrated with API Gateway. The capability of making asynchronous calls from API Gateway to Lambda allows the creation of serverless microservice-based applications.

But before deploying microservices in AWS Lambda it is necessary to package the Java, NodeJS or Python code of the application’s service in a ZIP file and upload it to AWS Lambda. You can easily name functions that handle resource and event limits.

Once an event is triggered, AWS Lambda looks for an idle function instance, launches it and invokes the handler function. It runs only that many instances that are needed to handle the load.

Underlyingly, Lambda functions utilize containers for the isolation of every instance and run those containers on EC2 instances.

You can invoke a Lambda function in one of the four ways. One way is configuring Lambda functions to invoke when an event occurs via DynamoDB, S3 or Kinesis. For example:

Creation of an object in an S3 bucket
Creation, update or deletion of an item in a DynamoDB table
There’s an unread message from a Kinesis stream
An email was received through Simple email.

You can also configure the AWS Lambda Gateway for routing HTTP requests to your Lambda and invoke its function. This HTTP request is then converted into an event object by AWS Gateway to invoke the lambda function and create an HTTP response based on the result from the Lambda function.

Another way to invoke the lambda function is to use AWS Lambda Web Service API. Lambda functions are invoked using a JSON object passed to the function by your application and returned by the web service.

The last way in which you can invoke a lambda function is by using a cron-like mechanism where you tell AWS to invoke the lambda function in a definite amount of time.

For each function invocation duration, the charges will be defined. Usually, the invocation duration is measured in total memory consummate and 100 ms increments.

Challenges of Serverless Microservices

Some of the challenges of serverless deployment of microservices are:

Significant limitations and constraints
Using a serverless environment for the deployment of microservices often has more constraints than in Container-based or WM-based infrastructures. For instance, AWS Lambda has support for only a few languages and is only suitable for stateless applications deployment that runs in reaction to a request. It will not allow you to deploy a message broker or database which are long-running stateful apps.

Applications Often Don’t Startup Quickly
If you want your app to start quickly then serverless deployment may not be an ideal choice.

Input Resources are Limited
AWS Lambda only responds to requests coming from limited input sources. It is not intended to run services like subscribing to a message broker such as RabbitMQ, Apache MQ, etc.

Risk of high latency
Time taken to provision or initialize a function instance for the infrastructure might result in high latency. Besides, the serverless environment only responds to load increases. The capacity cannot be pre-provisioned, which often results in a latency of your application in case of a sudden and immense spike in load.

Monitoring Serverless Microservices

To monitor serverless microservices, there are numerous tools available in the market, namely:

Reimann uses stream processing language for aggregation of application or host events to send email notifications for exceptions and also track latency distribution of the app.
Datadog offers a single glass pane for microservices dependencies monitoring. With its serverless monitoring, you can analyze different functions and resources that are invoked by them.
LightStep is another tool for monitoring serverless microservices. It allows development teams to identify and resolve regressions, despite the scale and complexity of the application.

Source: How To Deploy Microservices Using Serverless Architecture

What is Apache Druid Architecture?

Priyanshi Sharma — Mon, 07 Mar 2022 05:55:31 +0000

Apache Druid is among the most popular open-source real-time solutions for Online Analytics Processing (OLAP). As pre-existing open-source databases like Relational Database Management System (RDBMS) and NoSQL were incapable of offering a low latency data ingestion and query platform for interactive applications, the need for Druid was stimulated.

Druid has been initially designed for problem-solving around ingesting and exploring large log data quantities. Apache Druid aims to quickly compute drill-downs and aggregate the data sets. Due to its features, Druid is being used by renowned brands like Netflix and Airbnb to run queries on data streams constituting billions of events per minute.

You might ask:

What is Apache Druid?
What is Apache Druid Architecture, and
Who can use Druid? So, let’s get started with Apache Druid and give you all the answers you are looking for.

What is Apache Druid?

Apache Druid is a database that offers real-time analytics, developed for quick slice-and-dice analytics of OLAP queries on a large number of data sets. Druid is optimized for data analysis of time series (a sequence of data points indexed over time). The core design of Druid comprises the idea of time-series databases, data warehouses, and search systems. Apache Druid is commonly used for highly-concurrent APIs that require quick aggregations or GUIs of analytical applications as a database backend.

Some of the areas of common implementation for Apache Druid include:

Business intelligence
Supply chain analytics
Advertising analytics
Web and mobile clickstream analysis
Application performance metrics
Server metric storage
Network telemetry analytics

Apache Druid Architecture

Apache Druid has a distributed multi-process architecture that is designed to be easy to operate and cloud-friendly. Every process type of Druid can be configured and scaled independently which gives high flexibility over the cluster. Some of the process types that Druid have are Coordinator (manages data availability), Overload (control data ingestion), Broker (handle external client’s queries), Router (route requests), Historical (store queryable data) and Middle Manager (ingest data).

One can deploy Druid processes into three server types, i.e., Master (to run coordinator and overload), Query (to run broker and router), and Data (to run historical and middle manager).

The above image shows how data and queries flow in Druid Architecture when we use Master/Query/Data server organization.

Apart from built-in processes, Druid has three external dependencies to leverage existing infrastructure. These external dependencies are Deep Storage, Metadata Storage, and Zookeeper.

Deep storage is an essential part of Druid’s elastic, fault-tolerant design. Deep storage is shared file storage used by Druid to store any data ingested into the system and can be accessed by every Druid server. Druid uses deep storage as data backup and as a way to transfer data between processes.

Metadata storage stores various shared system metadata like task information and segment usage information that is essential for Druid cluster’s working. While Zookeeper is used by Druid for management of cluster state. With Zookeeper it becomes easier to perform tasks like internal service coordination, discovery and leader election.

Key Features of Apache Druid

The key features of Apache Druid are as follows:

Scalable Distributed System
Data ingestion in Druid is done at the rate of millions of records per second. At the same time, Druid retains trillions of records while maintaining query latencies ranging from sub-seconds to a few seconds.

Columnar Storage Format
Column-oriented storage is used in Apache Druid so that it only loads the exact column required to conduct a query. The column storage format improves speed for queries, supports quick aggregations and scans.

Massive Parallel Processing
Druid facilitates processing every query parallelly across the complete cluster.

Realtime/Batch Ingestion
In Apache Druid, data can be ingested in real-time or in batches and becomes immediately available for query processing.

Cloud-native, fault-tolerant architecture
As Druid stores data copy in deep storage, it becomes easier to recover data even when all the Druid servers fail, making Apache Druid architecture fault-tolerant.

Time-based partitioning
Although you can implement additional partitioning of data based on the other fields, firstly, the data in Apache Druid is partitioned by time. The time-based queries in Druid only access the data partitions that match the query’s time range, improving query performance.

Self-balancing, Self-healing, and Easy to Operate
The cluster of Druid automatically re-balances itself without any downtime. That is, if a server fails, the system routes data around the damage in the server automatically so that server can be replaced.

Approximate Algorithms
For faster bounded memory usage, Druid includes algorithms for approximate ranking, approximate count-distinct, approximate quantiles and histograms. These algorithms are often faster than exact computations. However, when you require more accuracy than speed, you can use exact ranking and exact count-distinct algorithms.

Quick Filtering Indexes
To allow faster filtering and searching around multiple columns, Druid uses CONCISE or Roaring compressed bitmap indexes.

Automatic Ingest Time Summarization
Optionally, Druid supports data summarization at ingestion time that partially pre-aggregates data to reduce costing and boost performance.

When You Can Use Apache Druid?
With the understanding of Apache Druid architecture and features, it’s time to know where you can use it. If you have the following use cases then the chances of Druid being a choice for your project are high.

You mostly have aggregation, searching, scanning and reporting queries.
High data insert rates and fewer updates.
If you need query latencies between 100 ms and a few seconds.
The data you are using includes time components.
You need fast ranking and counting over high cardinality data columns like user IDs, and URLs.
You have two or more tables where each query hits a big distributed table.
You want the data to load from object storage like Amazon S3 or file distribution systems like HDFS, Kafka, flat files.

Conclusion

Druid is designed to empower applications that need up 24/7. Druid possesses several features that can ensure no data loss and system uptime. Apache Druid also offers data replication, automatic backups, independent services, and so much more to boost application performance.

Source: What is Apache Druid Architecture

How Docker Container Works?

Priyanshi Sharma — Fri, 04 Mar 2022 05:56:35 +0000

Docker has become the de facto standard in container-based implementation. Docker is an open-source platform, written in GO language, developed by DotCloud and used by developers to build, deploy and run applications. With Docker, one can easily separate their applications from the infrastructure for the fast delivery of the project. It enables developers to package and run the application in a loosely remote environment called a container. The isolation that Docker provides allows developers to run several containers at the same time on a given host.

Initially built on Linux Containers (LXC), Docker now uses container runtime libcontainer (a part of runc). Although containers can be made without using Docker, the platform makes it simpler, safer, and easier to build, deploy and manage containers with its set of tools, dependencies and libraries that are needed to run an application.

But, what does Docker architecture contain, how Docker container works, and when you can use Docker? Let’s understand every concept of Docker.

Docker Architecture

To understand how docker works, we will take a peek into the docker architecture (given in the image below) and understand the components it contains.

Docker Client

A Docker user interacts with Docker through the Docker client (docker). When a developer uses a command like docker run, the client sends this command to the Docker daemon, which carries it out. The Docker client can communicate with multiple daemons simultaneously, using Docker API.

Docker Daemon

Docker Daemon (dockerd) looks for requests from Daemon API and handles objects of Docker like containers, images, volumes, and networks. To maintain the services of Docker, a Docker Daemon can easily communicate with other daemons.

Docker Desktop

Docker Desktop is an application for Windows or Macs environments. It allows developers to develop and share microservices or containerized applications. A Docker Desktop comes with Docker Client (docker), Docker Daemon (dockerd), Docker Content Trust, Docker Compose, Credential helpers, and Kubernetes. With Docker Desktop, you can work with languages and development tools you prefer.

Docker Registries

Docker registries is a stateless, highly scalable storage and content distribution system for Docker Images. If you want to tightly control the storage for your image, and fully own an image distribution pipeline for your development workflow, you can use the Docker registry.

Whilst in case you are looking for zero maintenance, instant solution you can go for Docker Hub. Docker Hub comes with a free hosted registry, automated builds, organizational accounts, and other additional features.

Docker Objects

Docker objects are used in assembling an application. These objects can be images, containers, volumes, networks, plugins, etc. In this section, we will provide a brief of Docker Images and Docker Container which are two essential components of the Docker Objects.

Docker Images

A Docker image is a file used to execute code in the container. Docker Images are read-only, immutable templates that act as a set of instructions for creating Docker Containers. Docker images come with metadata describing the needs and capabilities of a container. Dockerfile containing specific instructions are used for building a Docker Image. When you make changes to the Dockerfile and rebuild the image, only the changed layers will be rebuilt, making Docker images small, lightweight, and fast in comparison to virtualization technologies.

Docker Containers

Docker containers are runnable image instances. Using the Docker CLI or API, you can easily create, start, move, stop, and delete a container. A Docker Container can be connected to one or more networks, have its storage, or even create a new image based on its current state. By default, the Docker container is well isolated from other host machines and containers. A container is also defined by its configuration option and image that is used at the time of creation.

How Docker Works?

A client-server architecture is used by Docker. In Docker, a Docker client talks to the Docker Daemon (that builds, runs, and distributes Docker containers). The Docker client and daemon communicate on the network interface or UNIX sockets using REST API. The client and daemon of Docker can either run on the same system or a developer can connect the client to the remote Docker daemon. To work with applications that consist of a set of containers, you can use Docker Compose which is another Docker client.

For example, if you run the docker run command while using the default configuration of the registry, the following will happen:

If you don’t have the local ubuntu image, Docker will pull it from the configured registry.
Docker will create a new container.
As a final layer, Docker will allocate a read-write filesystem to the container allowing a running container to build or edit files and directories in the local file system.
A network interface will be created to connect the container to the default network.
Then the container will be started and /bin/bash executed by Docker. As the container is attached to the terminal and running interactively, you can give input using the keyboard while the result is logged to the terminal.
To terminate the /bin/bash command, you can type exit and the container will stop.

Get started with Docker

Docker can be used as a core building block for creating modern platforms and applications. Undoubtedly, with Docker, you can easily deploy code into the CI/CD pipeline, build and run distributed microservices architecture, and create scalable data processing systems. Docker makes the delivery of an application consistent and fast.

Moreover, the platforms or applications that are based on Docker allow extremely portable workloads. Docker containers can run anywhere from a developer’s laptop to virtual machines by cloud providers, in a data centre, or a blended environment.

Being lightweight and fast, Docker offers a cost-effective alternative to virtual machines based on hypervisors. Allowing you to use more compute capacity and achieve your business goals. Now with the extensive knowledge of Docker and how Docker containers work, you can easily decide whether it’s best suited for your business or not.

Source: How Docker Container Works

What is OLAP Analysis?

Priyanshi Sharma — Thu, 03 Mar 2022 06:10:16 +0000

We live in a data-driven world where it’s impossible to come out on top if you aren’t using data for insights and decision making. You can easily see the data at work in the powerhouse systems driving retail and new service offerings like ride-sharing applications.

In the field of data science, data processing systems are of two types, i.e., OLAP and OLTP. The major difference between these systems is that one offers insights through data and the other is completely operational.

So, this blog will cover the part that uses data to provide insights - OLAP (Online Analytical Processing). But before moving ahead and getting a grip on OLAP, we first have to understand what is the difference between OLTP and OLAP; and why do we need OLAP in the first place.

OLTP vs OLAP

Online Transactional Processing (OLTP) allows the execution of a large volume of database transactions in real-time. Put simply, an OLTP system captures and handles transactional data in a database, performed by a large number of people, majorly over the internet. Each transaction in OLTP involves an individual database record made up of multiple rows and columns.OLTP systems are behind most of our day-to-day transactions like in-store purchases and ATM transactions. The main focus of OLTP is on quick data processing as the databases of OLTP are written, read, and updated frequently.

On the other hand, Online Analytical Processing (OLAP) is a system that is used by organizations for multi-dimensional analysis of large data volumes. Usually, the data that is used for analysis is from data warehouses, data marts, or data stores. The OLAP system is a perfect fit for data mining, complex analytical calculations, and business intelligence. It is also used for business reporting functions such as budgeting, sales forecasting, and financial analytics. The emphasis of OLAP systems is on the response time to perform complex queries, where each query includes one or more columns of aggregated data from multiple rows. For example, OLAP systems can be used for the analysis of marketing lead generation trends and year-over-year financial performance.

Several organizations use OLTP systems to offer data for OLAP. In short, the blend of OLTP and OLAP can be highly beneficial for businesses.

OLAP Analysis Definition

Online Analytical Processing (OLAP) is the technology behind many business intelligence applications. OLAP is a powerful tool for data discovery that provides capabilities for complex analytical calculations, limitless report viewing, and predictive “what if” scenario planning. OLAP is an approach to resolve multidimensional analytical problems in computing.

Most businesses have multiple categories to break down the data for tracking, presentation or analysis. However, data in the warehouse is stored in tables where each of the tables can organize data into two dimensions at a time. OLAP tools allow users to interactively analyze multidimensional data from numerous perspectives to enable fast processing and insightful analysis.

Three basic analytical operations of OLAP are roll-ups, drill-downs, and slicing and dicing, where roll-ups involve data aggregation, drill-downs allow detailed navigation of information, and slicing and dicing enable users to take out specific data sets (slicing) from the OLAP Cube and view these slices from different viewpoints (dicing).

What is an OLAP Cube?

The OLAP cube is the core of OLAP systems. An OLAP cube is a multi-dimensional database that efficiently process and analyze multiple data dimensions more swiftly than a traditional relational database.

A relational or SQL database is structured like a spreadsheet and stores records in a row by column format (two-dimensional). Although the reporting tools for relational databases can query, report and analyze multidimensional data, the performance slows down with the increase in the volume of data.

That is where OLAP Cube comes into view. The OLAP cube extends tables with additional layers that add dimensions to the data. According to theory, a cube can consist of multiple layers. An OLAP cube that represents more than three dimensions is also called a hypercube. In a practical sense, a data analyst will only make the OLAP cube with the layers they need to get optimal performance and analysis.

It’s an OLAP cube that enables the basic multidimensional analysis of data through:

Roll-ups - aggregates data by moving up in the hierarchy or reducing the dimensions.
Drill-downs - opposite of roll-ups, converts less detailed data into more detail either by adding new dimensions or moving down in the concept hierarchy.
Slice and Dice - “slicing” creates a sub-cube by opting for a single dimension from the OLAP cube. Whereas, “dicing” isolates sub cubes by selecting several OLAP cube dimensions to view data from a different point of view.
Pivot - allows rotation of OLAP cube to display a new data representation and enable dynamic multiple dimensional data views.

Types of OLAP Systems

OLAP systems are categorized into three types, i.e., ROLAP, MOLAP, and HOLAP. So let’s understand what these three systems are and what they can do.

ROLAP: Relational Online Analytical Processing or ROLAP works directly with relational databases without organizing the data into a cube. The dimension tables and the base data are stored in the relational database as relational tables and new tables are created to hold aggregate data. ROLAP can handle large volumes of data and enable businesses to leverage their existing RDBMS software investment.
MOLAP: MOLAP or Multidimensional Online Analytical Processing works directly into a multidimensional environment instead of providing the multidimensional view of relational databases as ROLAP does. For most use cases, MOLAP is the most practical and fastest type of OLAP.
HOLAP: Hybrid Online Analytical Processing or HOLAP attempts to combine the features of both ROLAP (higher scalability) and MOLAP (faster computation) to provide fast access to data at every level of aggregation. Simply put, HOLAP tools can use both pre-calculated cubes of MOLAP and relational data sources from ROLAP.

When to Use OLAP?

OLAP tools like Apache Pinot and Apache Druid can be used in the following scenarios:

To quickly execute complex analytical and ad hoc queries, without affecting your OLTP system negatively,
To offer aggregations that allow users to get consistent results fastly, and
To help business users in generating reports from large data sources.

OLAP is being used for a wide range of organizations for:

Sales and Marketing: For market research analysis, promotion analysis, sales analysis and forecasting, customer analysis, and market and customer segmentation.
Finance and Accounting: For financial modeling, financial performance analysis, budgeting, and activity-based costing.
Production: For production planning and defect analysis.

Adopt OLAP Systems for Your Business

With OLAP companies can maximize the potential of their data as OLAP converts it into the most practical format for multidimensional analysis. Needless to say, it makes it easier for organizations to achieve intelligent insights into their business.

OLAP technology adoption depends on the underlying data sources, software types, and the objective a business wants to achieve. Each business area is specific and should have a bit of customization to create multidimensional “cube” models for data mining and analysis.

Source: OLAP Analysis

Front End Development Tools

Priyanshi Sharma — Wed, 02 Mar 2022 06:03:49 +0000

In a matter of a couple of years, front end development tools have come a long way. Thanks to these progressive tools, developers can build the layout and interface of the website hassle-free and efficiently.

As a front end developer, you will be developing web designs using programming languages like HTML, CSS and JavaScript. It gets more difficult to choose the right set of front end development tools as the number of tools in the market increases.

Top Frontend Development Tools

To make things easier for you we have compiled a list of the top 15 front end development tools in 2022, including:

Atom
Sublime Text
Visual Code Studio
Sass
LESS
Git
Chrome DevTools
HTML5 Boilerplate
CSS Reset
Bootstrap
Codepen
ESLint
Jasmine
Jira
BaseCamp

In the next section, we will divide these tools into seven different categories so that you know which tool is best for what kind of use cases.

Our All-Time Favorite Front End Development Tools

There is a wide range of front-end development tools for different requirements for HTML, CSS and JavaScript including code editors, version control systems, preprocessors, testing, project manager, and front end boilerplates.

Here is a curated list of top front end development tools that can be beneficial for your next project.

Code Editors

As a developer, you spend most of your time writing codes, so, one tool that you need the most is the code editor. There are numerous code editors in the market but the best ones of them are Atom, Sublime Text and Visual Code Studio.

Atom

It is a free and open-source text and source code editor, written in JavaScript and embedded in Git Control. Atom is a feature-rich text editor that supports many programming languages like JavaScript, Python, C/C++, Haskel, and Go. It comes with adaptable plugins, themes, functions for APIs and languages you use.

Key Benefits of Atom:

Git and GitHub version control
Built-in package manager
Smart auto-completion
Multiple panes
Cross-platform editing
Free of cost

Sublime Text

Sublime Text is a sophisticated, versatile, commercial, and fast text and source code editor. It supports several programming and markup languages. Sublime texts help with the automation of repetitive tasks so a developer can focus on the important stuff. Its interface is user-friendly and fully customizable and is supported on Windows, macOS, and Linux.

Key Benefits of Sublime Text:

Multiple encoding support
Smart auto-completion
Easy change of syntax, indentation, and sort setting with Command Palette
Multiple selection support
Unlimited operation canceling
Fully customizable display
Python API allows plugins to augment built-in functionality

Visual Code Studio

VS Code is an open-source and free code editor developed by Microsoft that is suitable for cross-platform development. It has been redefined and optimized for building and debugging web and cloud applications. VS Code has a unique solution - IntelliSense, enabling smart completion according to the function definition, variable types, and imported modules.

Key Benefits of VS Code:

Easy debugging
Syntax highlighting
Embedded GIT commands
Code refactoring
Snippets

Version Control Systems

Version control systems help developers to manage and keep track of all the changes in the source code over time. It enables developers to check and revert to the earlier versions of code if required.

GIT

Git is undoubtedly the preferred version control system for most developers. It is a version control system, capable of handling small to large projects efficiently and quickly. It allows the development team to work with the same code file of a project simultaneously without worrying about overwriting others' work. Git enables developers to create multiple autonomous code parts and make the creation, integration, and deletion of code smooth and easy.

Key benefits of Git:

Easy testing and debugging
High-speed work on large projects
Cryptographic user data protection
Branching model to easily create, link and delete independent code lines

CSS Preprocessors

CSS is a style sheet language for styling web pages, and CSS preprocessors make the creation of attractive sites easier. Due to this, CSS Preprocessors have become the mainstream of front end development workflow. These preprocessors extend the CSS functionality by adding a wide range of logical syntax. CSS preprocessors also help developers to generate CSS from the unique syntax of the preprocessor itself.

Sass

Sass is one of the most used CSS preprocessors. Sass compiles style sheets into CSS. With Sass, you can use nested rules, variables, functions, and mixins in a CSS compatible syntax. Sass organizes large stylesheets and makes it easy to share designs across projects. Sass has two different syntaxes, i.e., SCSS and Sass, where each one can load the other.

The SCSS syntax is a superset of CSS, which means that all the valid CSS is a valid SCSS.

The Sass or indented syntax supports all the same features as SCSS but uses indentation instead of curly braces and semicolons.

LESS

Leaner Style Sheets or LESS is a backward compatible CSS language extension. If you have learnt CSS, learning Less becomes easier as it looks similar to CSS. It has made a few additions to CSS like variables and loops to make working with CSS simple.

Key Benefits of LESS:

Clean code structure, thanks to nesting
Cross-browser compatibility
Faster updates
Fast and easy coding

Developer Tools

While building a website or web application, you might want to make modifications and test these changes' looks in your browser before committing those changes. Developer tools help you with that.

Dev tools are the functions in-built on modern web browsers that enable developers to see code behind every section of the website with just a few clicks.

Chrome DevTools

We recommend Chrome DevTools for this purpose because, in most scenarios, developers use Google Chrome as the browser to check the view of the site. Chrome DevTools is a set of web developer tools directly built into the Google Chrome browser. Chrome DevTools help in editing the page on the fly and diagnose errors quickly to help developers in swiftly building a more efficient website.

Key Benefits of Chrome DevTools:

Advanced breakpoint support for debugging Graphics
Easily explore local HTML5 databases
Functions execution time analysis to improve the site speed
While reloading the application, the trace is redrawn and relinked
Dynamic CSS and DOM to control web page appearance

Front end Boilerplate

Boilerplate or boilerplate code refers to the code section that needs to be included in different places with little to no changes. Boilerplates save developers from repetitive tasks and speed up the development process. HTML5 Boilerplate and Reset CSS are the top picks when it comes to front end boilerplate.

HTML5 Boilerplate

HTML5 boilerplate is a professional template for HTML, CSS, and JavaScript that helps in developing fast, adaptable and robust web apps or sites.

Key Benefits of HTML5 Boilerplate:

Mobile browser optimized
Javascript libraries
Cross-browser compatibility
Caching static content with .htaccess

Reset CSS

Every browser has its cascading style sheets that are used to display non-styled sites that create difficulties for CSS coding. So, Reset CSS aims to minimize the inconsistencies in the browser like margins, font sizes, line heights, etc. To reduce the risk of cross-browser compatibility issues, developers can reset the browser's CSS settings to zero with Reset CSS.

Online Coding Environment

Online coding environments are the sites that allow you to write code and view the results next to the code immediately. These coding environments are the fastest way for developers to learn and experiment with different coding techniques from developers in the same environment.

CodePen

CodePen is an online development environment for front end developers that enables them to test and showcase HTML, CSS and JavaScript code snippets (pens). A developer can build a website, deploy it, show off their work, build test cases to learn and debug. With the large and active community of developers, one can also find inspiration from their work and support when they need it.

Key Benefits of Codepen:

Customizable interface
Private pens
Allows you to customize your site's code demos
Easy assets (images) holding
Online IDE
Live code collaboration

CSS Framework

CSS frameworks are a must-have for front end development. CSS frameworks are packages of multiple CSS stylesheets that are ready to use for standard web design functions, such as layouts, fonts, navigation bars, colors, and so on.

Bootstrap

Bootstrap is the most used, free, and open-source CSS framework built by the Twitter development team and offers a detailed UI package. Bootstrap contains HTML, CSS and JavaScript extension templates for labels, typography, boxes, forms, navigation, and other interface components.

Key Benefits of Bootstrap:

Flexible and easy to use
Remarkable advancement speed
Lightweight and customizable
Fewer cross-browser bugs
Responsive structure
Strong community support

Testing

Testing and reviewing your code for a potential bug or error in the web app or site is a critical part of the application development. It is the only way through which one can ensure the proper functionality and performance of the web application or website.

ESLint

ESLint is a pluggable and configurable static code analysis tool for detecting and reporting on problems and errors in ECMAScript/JavaScript code. Some of the problems identified by ESLint are automatically fixed. It helps in minimizing bugs and making code more consistent.

Key Benefits of ESLint:

Strong documentation
Easy migration
Autofixation of errors
Allows developers to create linting rules
Pluggable rules

Jasmine

Jasmine is a behaviour-driven open-source testing tool for JavaScript that aims to run on any JavaScript platform. It doesn’t depend on the frameworks of JavaScript and doesn’t require DOM. Jasmine comes with a clean syntax that allows developers to easily write tests.

Key Benefits of Jasmine:

No external dependencies
No DOM requirement
Easy code testing

Project Management Tools

Project management tools help managers to control the workflow of the team that is collaborating on the same project and deliver the project on time they have committed to. Put simply, project management tools facilitate project planning, task management, bug and error management, resource allocations and time tracking.

Jira

Jira is part of the agile network management solutions suite that empowers team collaboration. Jira helps development teams to plan, assign, manage, track, and report work while bringing teams together for agile application development.

Key Benefits of Jira:

Improve agility of the organization
Align teams at a large scale
Improve operational efficiency
Deliver better results

Basecamp

Basecamp is an online collaboration tool that helps in managing teams and their work together while communicating with each other. Basecamp allows a manager to divide teams into separate projects where each project includes people involved, discussions, documents, files, tasks, important dates, and everything else related to the work at hand.

Key Benefits of Basecamp:

Intuitive and easy to use
Collaborative Project Management
Hassle-free third-party tools integration for synchronization and backup
Built-in communication features
Cross-device compatibility
Excellent customer support

Ending Note

So these were the leading front end development tools that are essential for modern applications’ front end development. These tools will improve your workflow when it comes to complex technical aspects of the front end development of an application or website. As a developer, you must choose the tools based on the requirements of your project.

Source: Front End Development Tools