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Dev Patel — Fri, 19 Sep 2025 10:05:30 +0000

Run LLM locally using Ollama: Offline, Private AI with Open-Source LLMs

Learn how to run open-source LLMs like Llama 2 and Mistral locally using Ollama for private, offline, and secure AI deployments—no cloud or API needed.

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Dev Patel — Fri, 19 Sep 2025 10:00:22 +0000

The Future of Digital Transformation: Key Trends for 2025

Explore key digital transformation trends for 2025—AI, automation, cloud, and Microsoft tools like Azure, Power Platform, and Copilot.

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Guide to Hiring a Data Analytics Consultant: Key Considerations and Roles for Business Success

Dev Patel — Fri, 19 Sep 2025 09:58:28 +0000

Navigating the complexities of business intelligence and data analytics can be daunting without expert guidance. This comprehensive guide is designed to help businesses, from startups to established corporations, understand the critical importance of hiring a data analytics consultant. A consultant brings expertise in managing and interpreting data and provides strategic insights that can lead to transformative business decisions. In this blog, we will explore why these professionals are indispensable, what to look for when hiring them, and their roles in ensuring your data initiatives succeed.

What is a Data Analytics Consultant?
A data analytics consultant is a specialized professional who helps businesses interpret and leverage their data to make strategic decisions. They possess deep expertise in data management, statistical analysis, and business intelligence tools, enabling companies to turn raw data into valuable insights. These consultants work across various industries to help businesses improve efficiency, predict trends, and optimize their operations through data-driven strategies.

The Role of a Data Analytics Consultant in Driving Business Success
A data analytics consultant plays a critical role in transforming raw data into actionable insights that inform strategic business decisions. By utilizing advanced analytical techniques, these professionals help organizations navigate the complexities of data management and interpretation. Their responsibilities include:

Strategic Planning: Data analytics consultants assess a company’s data infrastructure and analytical practices to strategize how data can be utilized more effectively. They identify key areas where data-driven insights can optimize business models, enhance marketing strategies, and improve product development. Consultants also assist in setting realistic, data-supported goals that align with the company’s long-term objectives.
Technical Expertise: Consultants bring specialized knowledge of advanced analytics tools and methodologies such as predictive analytics, machine learning, and data modeling. They help businesses choose the right technologies, such as Power BI for Microsoft-centric environments or Tableau for organizations requiring robust visualization capabilities. Their expertise ensures these tools are tailored to extract maximum value from the company’s data.
Hiring the Right Data Analytics Consultant: Key Considerations
Choosing the right data analytics consultant is crucial for the success of your data-driven projects. This section provides a detailed look at what factors to consider when hiring a consultant, including their skill set, experience, and compatibility with your company culture. We will guide you through the process, ensuring you know how to make a well-informed decision that aligns with your strategic goals.

Skill Evaluation: When evaluating the skills of a potential data analytics consultant, it is crucial to assess their proficiency in the specific technologies and methods relevant to your business’s data environment. For instance, if your data infrastructure is built around cloud solutions, a consultant with expertise in cloud analytics platforms like Amazon Web Services or Google Cloud would be beneficial. Additionally, evaluate their experience with analytical methodologies crucial to your needs—such as predictive analytics, statistical modeling, or machine learning—depending on the complexity and nature of the problems you aim to solve. This evaluation should also consider the consultant’s ability to adapt these methodologies to unique business contexts and generate actionable insights that directly contribute to strategic goals.
Cultural Fit: Cultural fit is more than just getting along with the team. It involves ensuring that the consultant’s professional values, work ethic, and approach to data align with your organization’s mission and operational style. This compatibility is crucial for long-term collaboration and success. For instance, if your company prioritizes innovation and rapid iteration, a flexible consultant who is experienced in agile project management might be ideal. Additionally, the consultant’s ability to communicate complex data insights clearly and relatable should align with your organizational emphasis on transparency and education. Finally, consider their previous engagement models with other clients to see how they handle relationships and project management. This ensures these align with how your teams are structured and how projects are typically handled within your company.
Maximizing ROI with Data Analytics Advisory Services
Exploring the realm of data analytics advisory services, this section discusses how these services can enhance your business’s strategic capabilities. By tapping into advisors’ specialized knowledge and expertise, companies can achieve a more profound and more effective utilization of their data, ensuring decisions are not only data-driven but also aligned with the company’s strategic goals.

Leveraging Expertise for Strategic Advantage
Data analytics advisory services can significantly enhance your business’s ability to effectively act on data-driven insights. This section discusses how partnering with the exemplary advisory service provides a competitive edge, offering analysis and strategic recommendations aligned with your business’s long-term goals. Understand the benefits of these services and how they transform data into a strategic asset.

Enhanced Decision-Making: Data analytics advisors help refine organizational decision-making processes by integrating advanced data analysis techniques. This integration enables businesses to predict market trends, customer behaviors, and business outcomes more accurately. Advisors also play a crucial role in quantifying risks and preparing strategies to mitigate them, ensuring that business decisions are proactive and informed.
Innovation and Adaptation: Advisors encourage adopting innovative data practices that support agile responses to market shifts. Staying abreast of the latest data analytics trends and technologies helps businesses adapt to changes more swiftly, whether adopting new data sources like IoT devices or utilizing emerging data analysis software. This constant innovation allows companies to maintain a competitive edge.
Selecting the Best Advisory Service
Selecting an advisory service that matches your needs is as critical as hiring a consultant. This section will guide you through choosing a service that meets your current analytical requirements and supports your future business objectives. From evaluating technical expertise to understanding service scalability, we cover all the essential criteria.

Technical Alignment: When choosing a data analytics advisory service, it’s essential to confirm their expertise and experience with the Business Intelligence (BI) tools and data management platforms your company already uses or plans to implement. For example, suppose your organization relies on Microsoft Power BI for data visualization and analysis. In that case, the advisory service should have proven competencies and certifications in Power BI to ensure they can effectively manage, customize, and optimize the tool to meet your needs. Additionally, their familiarity with your industry’s standard data sources and integration challenges ensures they can troubleshoot issues and streamline data flows efficiently. This alignment minimizes the learning curve and deployment time, enhancing your analytics initiatives’ return on investment.
Service Scalability: As your business grows, so will your data analysis need and the complexity of your data environment. Selecting an advisory service that can meet your current requirements and grow with you is crucial, as well as adapting their services to support larger data sets, more complex analyses, and additional BI tool integrations. This includes their ability to support advanced analytics capabilities such as predictive modeling, machine learning, and real-time data processing as your business begins to require these sophisticated approaches. Assess their track record in handling scalable projects and inquire about their infrastructure and staffing capabilities to support future growth. A service that can scale effectively will help ensure that as your business evolves, your data analytics can continue to provide strategic insights without interruption or the need for frequent provider changes.
Implementing BI Dashboards with Professional Assistance
Understanding the implementation and optimization of BI dashboards is crucial for harnessing the full potential of business intelligence tools. This section covers how data analytics consultants can help design and deploy effective BI dashboards, which are essential for visualizing complex data and extracting actionable insights.

The Power of Dashboards in Data Visualization
BI dashboards are essential for visualizing complex data sets and extracting actionable insights. This section explains how data analytics consultants can help design and implement effective dashboards that provide real-time data insights and drive business decisions. Explore the functionalities of popular BI tools like Power BI and Tableau and how they can be customized to your business needs.

Dashboard Customization: Data analytics consultants tailor BI dashboards to focus on the metrics most critical to an organization’s unique needs. This might include real-time monitoring of sales figures, operational efficiency, or customer engagement metrics. Custom dashboards help managers monitor health metrics directly impacting business outcomes, enabling faster and more targeted responses.
Real-time Analytics: Implementing real-time data analytics within dashboards allows businesses to track operations and market conditions as they happen, enabling immediate decision-making that can capitalize on opportunities or mitigate emerging risks. This capability is vital in industries such as finance and retail, where conditions can change rapidly and require quick, informed responses to maintain competitive advantage.
Choosing the Right BI Tools: Power BI vs. Tableau
Choosing the right BI tool is pivotal for effectively visualizing and analyzing data. This section provides a detailed comparison of Power BI and Tableau, two of the industry’s leading tools. Based on their features, usability, and integration capabilities, it will help you decide which tool best fits your business needs.

Integration and Compatibility: Power BI is highly compatible with other Microsoft products, making it an ideal choice for environments already using tools like Microsoft Excel and Azure. This integration simplifies data management and analysis, allowing for a more streamlined workflow. On the other hand, Tableau offers extensive support for different data sources. It is recognized for its ability to integrate data from a broader range of databases and applications, making it suitable for environments that require flexible data integration capabilities.
User-Friendly Features: Power BI is generally considered more user-friendly for users familiar with the Microsoft suite, with a lower learning curve and better native integration. Tableau, however, is often praised for its superior data visualization capabilities. It offers more advanced options for creating interactive and complex visual representations of data, which can be crucial for businesses that rely heavily on visual data exploration to make decisions.
Hiring a data analytics consultant can redefine how you manage and leverage your business data. As we’ve explored, these professionals play a crucial role in interpreting data and turning it into strategic business opportunities. By choosing the right consultant and advisory services and effectively implementing tools like BI dashboards, your business can enhance its decision-making processes, operational efficiency, and competitive edge in the market.

DEV IT provides tailored data analytics solutions that align perfectly with your company’s specific needs and goals. These solutions ensure you keep pace with your industry’s advancements and stay ahead of the curve. By partnering with DEV IT, you can access top-tier data analytics expertise, dedicated support, and strategic insights that drive decision-making and foster sustainable business growth. Whether you want to refine your data processes, enhance decision-making, or predict future trends, DEV IT’s data analytics consultants provide the guidance and tools to turn data into actionable insights, propelling your business toward success.

Source: Data Analytics Consultant

Why Cloud Security Monitoring is Essential for Protecting Business Data

Dev Patel — Tue, 04 Mar 2025 11:02:52 +0000

Cloud computing has revolutionized the way businesses operate, offering scalability, flexibility, and cost-efficiency. However, as more organizations migrate to the cloud, security threats are evolving at an alarming rate. Cybercriminals constantly target cloud environments, seeking vulnerabilities to exploit sensitive business data. To counter these risks, cloud security monitoring plays a crucial role in ensuring proactive protection and compliance. Implementing the right cybersecurity services and consulting with an experienced cybersecurity consultant can help businesses safeguard their digital assets from modern threats.

Understanding Cloud Security Monitoring
Cloud security monitoring involves continuously tracking, analyzing, and responding to security threats within cloud environments. Unlike traditional security monitoring, which focuses on on-premises networks, cloud security monitoring is designed for dynamic cloud infrastructures, ensuring real-time protection against evolving cyber risks. Key components include:

• Real-Time Threat Detection – Identifies potential security breaches instantly.
• Log Analysis – Monitors system logs for suspicious activities.
• Anomaly Detection – Uses AI and machine learning to detect unusual patterns that may indicate a threat.

Major Security Risks in Cloud Environments
Cloud environments face unique security risks, making continuous monitoring essential. Some of the primary threats include:

• Data Breaches & Unauthorized Access – Weak access controls can expose sensitive business data.
• Insider Threats & Misconfigurations – Employees with excessive privileges can unintentionally or maliciously cause data leaks.
• Malware, Ransomware & DDoS Attacks – Attackers exploit vulnerabilities to compromise cloud resources.
• Compliance Violations – Failing to adhere to security regulations can result in legal and financial penalties.

Benefits of Cloud Security Monitoring for Businesses
A well-implemented cloud security monitoring strategy provides numerous benefits:
• Proactive Threat Detection – Identifies security threats before they escalate.
• Real-Time Visibility – Offers a continuous overview of cloud security status.
• Regulatory Compliance – Helps meet industry standards like GDPR, HIPAA, and ISO 27001.
• Data Integrity & Protection – Ensures sensitive business data remains secure.
• Cost Savings & Operational Efficiency – Reduces downtime and the financial impact of security incidents.

Best Practices for Effective Cloud Security Monitoring
To maximize protection, businesses should follow these best practices:
• Leverage AI-Driven Security Analytics – Automate threat detection and response.
• Use Security Information and Event Management (SIEM) Tools – Centralize security logs for better threat correlation.
• Integrate Identity and Access Management (IAM) – Restrict unauthorized access to critical data.
• Conduct Regular Security Audits – Assess vulnerabilities through penetration testing.
• Automate Incident Responses – Reduce human intervention and response time to threats.

Choosing the Right Cloud Security Monitoring Solution
Selecting the right security monitoring tools and cybersecurity services is crucial for businesses. Consider the following factors:
• Scalability – Can the solution grow with your business?
• Automation Capabilities – Does it reduce manual intervention?
• Integration with Existing Systems – Can it work seamlessly with your cloud environment?

Top cloud security monitoring tools include AWS GuardDuty, Azure Security Center, and Google Chronicle. Consulting with a cybersecurity consultant can help businesses choose the most effective solution tailored to their needs.

Future of Cloud Security Monitoring
As cyber threats evolve, cloud security monitoring continues to advance. Key trends shaping the future include:
• AI and Machine Learning for Threat Detection – Automating real-time anomaly detection.
• Zero Trust Security Models – Ensuring no one is trusted by default, even within the network.
• Continuous Monitoring for Hybrid and Multi-Cloud Setups – Securing complex cloud environments with ongoing assessment.

Conclusion
Dev Information Technology Ltd offers industry-leading Cybersecurity Services to help businesses safeguard their cloud environments and stay ahead of cyber risks. Cloud security monitoring is no longer optional, it is a necessity for businesses aiming to protect their sensitive data from cyber threats. By integrating advanced monitoring tools and engaging with expert cybersecurity consultants, organizations can significantly enhance their security posture. For better understanding and guidance, connect with industry experts to resolve all your cybersecurity concerns.

What is GitOps and How to setup GitOps using CI/CD pipeline?

Dev Patel — Mon, 12 Sep 2022 07:54:53 +0000

Git has always functioned as one of the program’s most robust collaboration tools. It has various tools that boost productivity during each stage of application development.

One such tool is GitOps, which allows the integration of various new features such as auto-deployment, auditing, seamless rollbacks, and efficient troubleshooting. Let’s explore GitOps in detail and learn how it can help boost efficiency in the CI/CD Pipeline.

CI/CD with GitOps

CI/CD comes with its own set of problems that GitOps can help solve. These are:

Problem 1.

Manual processes and provisioning of resources from the on-premises datacentre that has created inefficient practices.

Solution GitLab (SCM,CI) and Terraform
Result Drastically improved lead time to production. Environments can be created in under an hour. Everything can be done using code, and patching has no risks.

Problem 2.

 Lack of control complex in a CI toolchain.

Solution GitLab (SCM,CI,CD) and Terraform
Result It’s easy to deploy something and roll it back if there’s an issue. It’s taken the stress and the fear out of deploying into production. ##What is GitOps? GitOps is a new addition to the versatile tools of Git that allows using features such as cluster management, continuous integration, and application delivery. It works seamlessly with Git and can be your single point of contact for the application delivery process.

Moreover, there are a ton of GitOps Service Providers to choose from, such as GitHub, BitBucket, and GitLab. Many of these providers are already compatible with GitLab, and your switch will be seamless.

Why GitOps?

Achieving automation in the application development process is an incredible feat that’s been made possible thanks to integrating the entire pipeline with DevOps. However, since it uses multiple toolchains for achieving this goal, managing these toolchains has become challenging for developers.

The solution comes from GitOps, which provides the same level of automation via a single toolchain for the entire development and deployment process. As a result, it reduces the points of failure and makes the work easier for developers.

Setup with GitOps

Setting up GitOps is a straightforward procedure. This blog will show how to enable Kubernetes Cluster Creation and Application Deployment Management using GitOps. Here are the steps you need for incorporating GitOps into your pipeline.

1. Repository setup

To start with GitOps, all you need is a Git repository. For this, you must sign up with your preferred Git service provider.
After the repository has been set up, you can begin pushing your code.
While maintaining the repository, ensure that you create branches per your branching strategy.

2. Create Git workflow
You must create a Git workflow to manage all your activities and tasks from a single source. While doing so, define all actions and their execution flow.

3. Define GitOps Actions for our project
Once you set up your Git repository, you must use the git-cicd.yaml file for completing the process. Simply push it to the Git repository.
The file includes instructions that need to be executed during the Git CI/CD setup, and dependencies have to be installed over the Git runner.
Please note that this file is crucial to the functioning of Git CI/CD.

4. Setup infrastructure for GitOps
Your Git service provider would provide space for creating and maintaining source code; however, it will likely not include any processing capability to process the GitOps pipeline.
For this, you will need to provide your pipeline with a separate platform that has computing capabilities. For our purposes, we will create a runtime instance for the same.
Finally, you must set up a runner to execute the Git CI/CD steps in your GitOps pipeline. A runner is simply a machine that functions as a CI server and performs CI/CD functions.

All your dependencies will be installed into the runner using the git-cicd.yaml file. The same steps can be used to define actions for automated application deployment, Kubernetes Cluster Management, and various other features that are provided with GitOps.

*Take away note: * Helps organizations to minimize lead time to production, deploy infrastructure in hours, Foster Collaboration, Compliance and Auditing, Version controlled environments, Test Automation, and Pipeline Configuration Management.

Conclusion

Setting up GitOps to work with your existing project pipelines is easy. By following the above steps, you should be able to have your systems up and running within a day. However, if you encounter any issues along the way, feel free to connect with us and our dev Information Technology ltd. Engineers will be ready to help you.

Original Source : How to setup GitOps using CI/CD pipeline

Azure Data Factory Overview For Beginners

Dev Patel — Mon, 12 Sep 2022 07:49:33 +0000

To complete the Extract, Transform, and Load (ETL) process, engineers can depend on several tools and technologies. One of these tools is Azure Data Factory (ADF). At its core, ADF is a data pipeline orchestrator and ETL tool facilitating easy and streamlined data processing.

With ADF, we can transfer data at scale and with speed while creating bespoke data-driven workflows and schedule pipelines. Besides the flexibility of ADF for processing data, it also has a lower learning curve.

This makes Azure data factory a good solution for beginners in this field and when you need a reliable solution to complete the task quickly.

In this guide, we will go through the steps to begin working with the ADF. Look for the steps to;

Setup
Creating datasets
Creating a pipeline
Debugging the pipeline
Manual triggering of pipeline
Scheduled triggering
Monitoring the pipeline

Let’s get started.

Setting Up Azure Data Factory

Before moving to the setup process, make sure of a few things;

Get a subscription with Azure. You can make an account for free and get started with the basics right away.

Next, identify your role in the Azure account. To set up everything from scratch, take on the role of an administrator.

However, to work on the child resources (datasets, pipeline, triggers, etc.), you can take on the role of a Data Factory Contributor.

Continuing with creating an Azure data factory, here are the steps.

1. Launch Data Factory

You can use Microsoft Edge or Google Chrome to access your Azure account. Once in, navigate to Azure Portal, click on Create a Resource, and select Integration. From the options given, find and click on Data Factory.

2. Add Resource

From the window, you are seeing right now, look for a tab named Basics. Then select your Azure Subscription. This is important because the data set you are about to create will be attached to this subscription.

When prompted to choose Resource, use the drop-down list to select one or Create a new resource.

Follow the “What is Azure Resource Manager” guide to know more about creating a resource.

3. Select Region

These are the geographical regions, and the supported ones are listed on the platform. Basically, these will help you know where the IT Infrastructure Managed Services Azure data factory metadata will be stored. The supported regions are;

West US
East US
North Europe

4. Enter a Name and Version

A basic practice is to give a globally unique name to the data factory. For trial purposes, you can take ADTTutorialDataFactory or anything else you want. If the name is not unique, you will get an error message, which will be easy to resolve. With the name fixed, move to Version and select V2.

It is important to read and understand the Data Factory – naming rules to add the required names to the Data Factory Artifacts.

Check the image below for a better understanding.

Source

5. Git Configuration and Review

In the last step of setting up the ADF, move to the next tab Git Configuration. Here click on Configure Git Later and click on Review & Create. Before hitting create, you will have to pass the validation test.

6. Azure Data Factory Studio:

Once you have created the ADF. Move to the main page, click on Go To Resource and select the name of your Data Factory Page. Towards the bottom, you will see Open Azure Data Factory Studio. This will open the data factory page on a new tab.

When you experience issues with getting authorized, try clearing the browser from third-party cookies and site data.

Next Step – Create a Linked Service

Linked service creation is the next crucial step in ETL processing with the Azure data factory. The purpose of creating this service is to link the data store, from where data will be extracted to Data Factory. The same service also works for when you are working with Synapse Workspace.

Creating this service is like identifying and defining the connection information required to connect the data factory to external sources.

Follow the steps to create a linked service in the Azure data factory.

1. Create New Service

Start by opening the Manage tab located on the left side panel. In this, you will find Linked Service, click on it and create a New linked service.

Source

2.Azure Blob Storage

After the Create Linked Service page opens, find and select Azure Blob Storage, followed by clicking on Continue. On the next page, do the following;

Fill in a name – it can be anything. For tutorial purposes, let’s keep it AzureStorageLinkedService.
Next, select your Subscription and account name from the drop-down list.
In Test Connection, select “To Linked Service” and click on Create.

Moving On → Creating Datasets

Datasets are the data structures stored by businesses in data stores. Simply put, datasets represent the data that you will be using in the **Azure data factory **and put it under processing.

In the ADF, you can create data with a simple procedure. The important thing to remember is that you will need to create two types of datasets, InputDataset and OutputDataset.

InputDataset

The source data in the input folder is represented by this dataset. Here you need to specify three things;

Blob container
Input folder
File

OutputDataset

This is the data that is sent to the destination. Here too, you need to specify three things;

Blob container
Output folder
File

The name of the Output dataset depends on the ID, which in turn, is generated based on the pipeline.

To create datasets, you must specify the details about the source data accurately in source dataset settings;

Blob container
Folder
File This will tell the system where the data resides.

The same is required for in the Sink dataset settings;

Blob container
Folder
File This will tell the system where the data will be copied.

With this done, move forward and complete the following steps;

In basic configuration : click on the Author (Pencil sign) tab and then click on the (+) sign located besides the search bar. From the drop-down menu, select Datasets.
Datasheet Page: On the new datasheet page, select Azure blob storage followed by clicking on Continue. This will take you to the Select Format page, and here you need to select Binary. Click on Continue.
**Set Properties: **In this step, we will configure the properties of the dataset page by working on the following;

source

Fill InputDataset in the Name column.
Under the Linked Service menu, select AzureStorageLinkedService.
Select the file path by clicking on the Browse button on the left-hand side. Once selected, click on OK.

Repeat the same process for configuring OutputDataset. However, in case you are not able to find the output folder, it will be created during the runtime process.

source

Creating a Pipeline in Azure Data Factory Studio

A pipeline is the group of activities that are scheduled to run and perform during the process. All the activities in a pipeline culminate in completing a single task. Hence, creating a pipeline will improve how you complete the process and help you streamline the activities accordingly.

Create a Pipeline: From the Author page, click on the (+) sign and select Pipeline. From the window, you will see five different tabs at the bottom of the page.
Copy Data: Click on the General tab followed by selecting Properties. Name the pipeline; we can keep it CopyPipeline and then close the General panel.
Activities: On the left hand, you will find the Activities panel. Under this, locate Move & transform followed by dragging Copy Data to the white surface area, which is the space given to create the pipeline. Below the tabs, you will find the name column and fill in the name of the CopyfromBlobtoBlob.
Validation: In the next steps, switch to the Source tab and select InputDataset in the column Source Dataset. Next, in the Sink tab, choose OutputDataset for SinkDataset. With this done, you will find the Validate option on the top panel.

Next Up — Debugging the Pipeline

Debugging is simple. You need to ensure that the pipeline is free from errors and issues before deploying it into the Azure Data Factory processing. To run the debugging sequence, follow these steps;

Above the white surface area where you have recently created the pipeline, find Debug. Click on it to start a test run on the created pipeline.
To be sure, confirm the debugging status and results by checking the name of the pipeline you have just created.

Manually Triggering the Pipeline

Besides automatic processing of the pipeline, you can manually trigger the execution. Before manually triggering the pipeline, publish all the entities to the Data Factory.

For this, click on Publish, located on the top main menu. Once published, click on Add Trigger located on the Pipeline toolbar and select** Trigger Now*. When the **Pipeline Run* page opens up, click on OK.

Pipeline Monitoring

Monitoring is important to ensure that the pipeline is running according to the requirements. For this;

Find the Refresh button on the left-hand panel. It’s the button below Author. From the window that appears, find the name of your pipeline, which you want to monitor and check.
Select the name of the pipeline and check its status. To view more information, click on Details (it’s the image of spectacles). To further explore how the properties are configured, check out Copy Activity Overview.
Here you can also confirm whether the system has created a new output folder or not.

Scheduling a Pipeline Trigger

Scheduling a trigger for the pipeline is not always necessary, but it can be an option. You can run a periodic triggering of the pipeline by tweaking the settings. Here’s how it can be done.

Start by going into the Author page and clicking on Add Trigger. Then click on New/Edit and when on the Add Trigger page, click on Choose Trigger followed by clicking on New.
On this page, fill in the required fields. Enter the Start Date, Recurrence, and the End On date. Click on Activated and then OK.

source

You might get a warning message here, click on OK and move forward. Back to the main page, click on Publish All to reflect the changes in the Data Factory.
Once this is done, go to the Monitor and click on Refresh. Here you will notice that the values have changed to Triggered By instead of Trigger Now. This changing of values authenticates that the scheduled trigger has been set.
To further check the progress, click on Trigger Runs and check that an output file is created for every pipeline.

Conclusion

This entire exercise sums up how to operate Azure data factory studio and execute a pipeline function, ensuring data extraction, transformation, and loading into the designated output destination.

Companies that have to transfer loads of data, especially from legacy systems, will find working with the Azure data factory straightforward. As a solution, it supports a wider range of file and data types, making your work faster and easier.

Original Source : Azure Data Factory Overview

ETL vs ELT – Which is Better for a Modern-Day Business?

Dev Patel — Mon, 12 Sep 2022 07:19:02 +0000

Data analysis, capturing, and interpretation has become key factor in business success. ETL and ELT are two types of data management practices that help organizations make data-driven decisions.

To deliver these decisions and ensure that they are interpreted into business intelligence solutions, ETL and ELT are practiced, depending on the organizational requirements.

Basically, with the increasing volume of data and its extraction from a progressively increasing number of sources, making business-appropriate decisions has become complex. With ETL and ELT, we can make this entire process a lot more efficient and rewarding.

What is ELT and ETL?

ETL and ELT are data integration processes. With these, we can shift or move raw data to a database. These databases are usually called data lakes or data warehouses. To send the data to the desired location, either ETL or ELT is implemented. ETL and ELT processes work with cloud-managed services, which helps provide universal access to interpreted data based on access control settings.

1.Extract, Transform, Load (ETL)

One of the ways organizations can use to store and manage data includes collecting, reformatting, and storing it on the desired server. After extraction, the data is formatted based on predefined parameters.

This is the staging area, where data is transformed into understandable bits, visualizations, patterns, and trends. In the load phase or stage, the formatted data is moved to a data warehouse or data lake.

From here, anyone with access to the storage server can access the data and make business decisions.

ETL origins go back to the decade of 1970s when companies were starting to collect large amounts of data from multiple sources. To process, they started to arrange this data into different datasets.

This led to a significant issue of disjointed and cluttered databases. And as these complex databases began to increase, collecting data was quickly becoming a redundant exercise with no beneficial outcome.

Then ETL arrived and provided businesses with an effective way to manage large datasets with ease. For the next three decades, ETL was the mainstay for organizations to convert raw data into business intelligence.

*For Whom ETL is an Ideal Data Integration Method? *

Disperse Data Sources

Businesses that have diverse and spread-out data sources will benefit the most from ETL. These are companies that have customers, suppliers, partners, and stakeholders in different regions addressed via multiple ventures.

ETL helps these businesses collect data from different repositories and formats in unison, then load everything onto a target location.

Shift from Legacy Systems

A particular use case of the ETL system is when organizations working with legacy systems want to implement a collective data shift to a modern system. In this case, as well, the ETL process can extract, transform the data into an understandable format, and load it to the target location. So, we can say that ETL is an important part of digital transformation solutions.

So, ETL is best suited for situations where you have multiple environments and have to process data collectively before viewing it on a separate medium.

2.Extract, Load, Transform (ELT)

The second and relatively modern data integration process, ELT, has a different approach. Here, the data is extracted, loaded onto the target location, and transformed. The major benefit of loading before transforming is to faster processing.

The speed of data transfer increases because there won’t be any coding-based errors, which may occur in the migration process. However, the transfer or migration comes before transformation, which eventually leads to a faster implementation system.

Essentially, ELT is decoupling the loading and transformation process. Here, they are independent of each other, which leads to better performance.

Because the transformation is done after data is loaded, it also helps decrease the computing power required to transform data before loading.

The transformation is now the responsibility of the service provided you have chosen to implement the ELT integration process. Since the ELT system generally works with cloud-managed services, businesses can process structured, unstructured, raw, and semi-structured data with the same efficiency.

For Whom ELT is an Ideal Data Integration Method?

1.Large Volume of Data

A key area where ELT implementation bears fruit is when the datasets are huge. We are talking about terabytes of data extracted or collected from different sources. For instance, in a weather forecasting system that has multiple types of data coming from a wide array of locations. In this case, the ELT system allows businesses to cover more ground with huge volumes of data and process it with speed.

2.Real-Time Data Requirement

Organizations that rely on correct data analysis and that too, in real-time will certainly want to use the ELT implementation system. A trading company requires access to accurate data insights and that too, in real-time.

Similarly, large-scale distributors and suppliers also require real-time access to accurate data insights. These types of businesses will find access to the insights they need with ETL helping them improve business performance with intelligence.

Today we have smart solutions that help further improve the ELT data implementation. These solutions help complete the data transformation cycle irrespective of the data type and extension.

Going Through Each Stage Separately

Both ETL vs. ETL has three core stages. While their order has been rearranged, the functionality and purpose remain the same. Let’s discuss them separately.

1. Extract

In both types of processing, extract comes first. The first extraction stage means collecting and copying data from a pool of sources. The data can come from simple and dynamic ERP solutions, CRM systems, SQL, NoSQL databases, SaaS systems, emails, mobile applications, websites, web pages, excel databases, and so on.

Because there are multiple and disparate sources to collect data, the extraction is time-consuming and complicated. This is intricate work and must be done carefully.

There are three types of Extraction processes;

Full Extraction

Full extraction means when systems cannot differentiate between new and old records. Due to this, the only way is to pull out all the data irrespective of the time, type, and extension.

Partial Extraction

This extraction is the most convenient and easy method for pulling out data from different source systems. Partial extraction has notifications that will send alerts whenever records change.

Incremental Extraction

This one is similar to partial extraction but with a difference. No notifications are sent in this process. With this, we can extract only that data that has been modified.

However, with the changing order of Transformation and Load, we will have to work on the extraction part differently. In ETL, you must plan ahead about the type of data that has to be extracted because it will go for transformation in the next stage.

In ELT, we can extract data without any filters because the data will be sent to the data warehouse immediately. Once loaded, we can decide on the transformation.

2.Transform

Transform is the second stage in ETL and the third stage in ELT. This is the stage where extracted data is transformed into meaningful insights. With specially built solutions and technologies, we can give the command to sort, filter, duplicate, cleanse, convert, translate, remove, encrypt, join, and split data.

Transformation makes data readable and makes room for an effective analysis. Here again, the shift of Transform and Load can determine the speed and efficiency of the entire process.

In ETL, the transformation occurs outside the data warehouse in a separate and independent staging area. Dedicated engineers and specialists work together to implement the transformation processes.

Any type of conversions and processing happening here can only be done once. It is a complicated procedure.

To change the type of analysis midway means modifying the entire pipeline and working on it from scratch.

On the other hand, the ELT transformation process is a bit more flexible and business-friendly. This is because the data for transformation is taken from the data warehouse. Here it can be changed, transformed, or modified, any number of times.

3.Load

Loading is the second stage in ELT and the third stage in ETL. The task here is to load or add data to the data warehouse. From the warehouse, any user can access and view it depending on the access control settings and permissions.

In ETL, the data is prepared and then sent to the warehouse. Mostly, the engineers do this with SQL and by arranging it in a tabular form.

With ELT, the entire data set is first loaded into the warehouse. This reduces the time required to process the raw data by a huge margin.

Head-to-Head Comparison of ELT vs. ETL

Until now, you must have understood that the ELT vs. ETL is not only about changing the position of two stages, replacing Load with Transform. There are several fundamental differences, and they arise due to the swapping of the stages.

Conclusion

ETL and ELT processing help businesses with precision and high-value data integration to improve business performance and analysis. Both the methods lead to offering business intelligence solutions allowing organizations to leverage actionable insights.

While choosing a service provider catering to ELT and ETL implementation, prefer one with automation and scheduling systems along with standardized query support. Moreover, a service provider with a faster query response will bring results with speed and scalability.

It is important to identify the type of data you have, the storage capabilities of the service provider, and your business needs while choosing the best solution. With data analysis becoming an integral part of business growth and success, it is essential to ensure process simplification while focusing on the results.

Original Source : ETL vs ELT

Cloud Application Modernization Services are Critical for Business Success

Dev Patel — Fri, 17 Jun 2022 08:18:59 +0000

Application modernization has become a necessity today, especially for businesses lacking in capturing the market because of technological immaturity. Companies that are still relying on on-site solutions and legacy systems must update, upgrade, change, reformat, and re-platform their digital solutions.

Doing so will empower them to work at par with the existing market trends, experience operational flexibility, reduce costs, and streamline operations while making them more productive than before.

The process of repurposing the legacy systems is called application modernization. Cloud application modernization is one part of this process.

Explaining Application Modernization and Optimization

The current and upcoming market structure necessitates that businesses become fluid enough to adapt, react, and perform according to the market. Plus, they need to execute the required changes with speed and efficiency or lose market share for being obsolete.

This poses a challenge for the Infrastructure and Operations leaders, CTOs, CIOs, and technical leaders. They must build a system that enables companies to support rapid and pertinent innovation plus growth.

Application Modernization and Optimization

Companies repurpose and reprogram their existing infrastructure and software to become modern. Sometimes the CTOs decide to consolidate the legacy systems or rewrite their code to function like a modern application.

This is done while considering the company needs and the market trends. In the end, cloud app modernization services provider a plethora of benefits.

Monolithic to Granular Structure:

Legacy software and solutions have a monolithic architecture. They are built on a single-tier, which also includes a database and interface into a single program.

Modernizing them means giving these solutions a granular structure where every segment works independently of others, but when combined, they all work together to run a single application. This type of architecture makes it easier to maintain, update, and alter the solutions.

Cost-Efficient

Compared to a monolithic application structure, the granular or microservices format is also cost-efficient. This is because to update a feature in the legacy software means changing or editing the entire codebase so that every aspect conforms to the updated version. With application modernization, the architecture changes to take a new form, which is cost-efficient.

Enhances Performance

When you compare a legacy application with one built with contemporary technologies, there will be starking differences. The latter will perform better in terms of performance, and speed, have a better interface and provide an impressive customer experience.

The commiserate result of all these aspects is that the user experience will be better, leading to lead conversion and revenue generation.

Application Modernization Patterns

We can modernize a solution with three patterns;

Rehosting:

Rehosting is the simplest form of modernization where we simply pick up the application code and move it to another form of infrastructure. Mostly, this is moved to the cloud services like AWS, Azure, etc. In most cases, there is no alteration done to the code.

However, in cases where changes are required or done to the application code, then we can call it Replat forming. With cloud application migration, the application’s security is considered to be the primary concern. Hence, it is critical to pick the best cloud service provider with higher security and efficiency.

Refactoring

In refactoring, the entire code base is restructured or rewritten to suit the modern-day application development architecture. The developers work on the code to make it compatible with the cloud architecture. They can split the monolithic application structure
into a microservices one.

Key Technologies Used for Cloud Application Modernization Services

Organizations working on the modernization and optimization of an application use different types of technologies and tools. These are;

Cloud Computing

At its very core, cloud application modernization leverages private, hybrid, and multi-cloud solutions to modernize strategically. In this, while security is the critical factor considered for the migration part, the developers also look at latency and the overall architecture.

Containerization

With technologies like Kubernetes and Docker, developers deploy parts or complete applications in a separate environment. This dedicated environment is self-sufficient to host some features or full-fledged applications. With containerization, the greatest benefit we can leverage is scalability.

Plus, a well-executed application optimization and modernization process will ensure scalability, resilience, application security, compliance, and adaptability.

Cloud Wars: AWS Vs. Azure Vs. Google Cloud

Dev Patel — Fri, 03 Jun 2022 07:53:38 +0000

AWS, Azure, and Google Cloud are three cloud services that are the creations of three large worldwide corporations with a global customer base and a constant commitment to improving their services and products.

You must know the differences between AWS, Azure, and Google Cloud solutions when purchasing Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) solutions. And this discussion is necessary since the question is not if you should use cloud computing but rather the cloud service you require.

Where AWS provides global cloud services via its large repository of data centers spread across the globe, Azure provides seamless integration functions with Microsoft’s tools. Google Cloud Platform or GCP rules the market in the price section, which is one of the most prominent concerns for users.

In the sections ahead, we will explore the differences between these three cloud services in a bit more detail.

Introducing AWS, Azure, and Google Cloud Platform

AWS or Amazon Web Services
AWS, a paid-subscription type of on-demand Cloud Computing platform for single users (people), businesses, and governments, was founded and maintained by the online marketing behemoth.

Amazon Web Services is the cloud market's oldest and most experienced service provider, having pioneered this type of service model in the industry to some extent. AWS has an advantage over competitors in terms of the user base (which is greater), trust, and reliability due to its experience and early market debut.

Google Cloud Platform
Google Cloud services include a suite of different cloud-based computing services running on the same infrastructure that Google uses for its services like Google Search, YouTube, etc.

Begun in 2011, GCP has been able to maintain a solid position in the cloud business since then. Google Cloud was built to help Google's own products, such as the Google Search engine and YouTube, perform better.

They have, however, already released their enterprise services, allowing anyone to use the Google Cloud Platform.

Microsoft Azure
Microsoft's Azure cloud computing platform was launched in 2010 to provide users and companies with a secure cloud computing platform. Azure was renamed 'Microsoft Azure' in 2014; however, the term 'Azure' is still frequently used.

Since its inception, Microsoft Azure has achieved great development in comparison to its competitors. One of the most essential features of Microsoft's Azure cloud solutions is the opportunity to use a wide range of cloud-based services without having to invest in new hardware. Due to this, the cloud computing system's installation and management costs see a substantial decrease.

Major Points of Differentiation Between Three Major Cloud Services

Availability Zones
Availability zones are data center areas or locations from where a company provides and runs its cloud services. More availability zones means the coverage of cloud services will also be higher. Technically, it corresponds to

1.Higher uptime

Fewer lags
Lesser downtime
Better availability of services.

Compute Capabilities
Software computation is the mix of computing factors, including processing power, memory, storage capacity, etc. Cloud services with better compute capabilities can deliver better performance
and solutions.

AWS: The Elastic Compute Cloud, or EC2, is used by Amazon Web
Services to supply safe and resizable compute capacity to the cloud architecture. Besides having complete compatibility with Windows and Linux-based systems, EC2 supports a larger number of instances while showing evidence of handling a larger number of GPI instances.

High GPI instances lead to high-performance computation and auto-scaling capabilities. AWS has been expanding its containerization offerings in addition to the computational services. It has its own Fargate services and supports Docker and Kubernetes

Azure: With Microsoft Azure, not EC2 but Virtual Machines represent the compute services. VMs are compatible with Linux, Windows, Oracle, IBM, SAP, and SQL Server. With all of this interoperability, Azure virtual machines are also more secure and have hybrid cloud capabilities.

Integrations such as artificial intelligence and machine learning are also possible with Azure. Azure offers two container services: Azure Container, which is built on Kubernetes, and Azure Container Registry, which manages Docker Hub and Azure Container Registry.

Google Cloud Compute: GCP’s computational engine includes both predefined and custom functionality, such as predefined machine kinds, pre-second billing, and Linux and Windows support.

You can also include automated discounts and a carbon-neutral infrastructure, which is beneficial because it decreases energy consumption significantly. By linking to Kubernetes, Google
Compute Engine is also ready for containerization and microservices systems.

Storage Capacity
Amazon Web Services: AWS has three storage systems meant
for cloud computing;

Simple Storage Service (S3) - Meant for object storage
Elastic Block Storage (EBS) - Used for persistent block storage
Elastic File System (ELS) - Used for file storage

AWS interacts with a SQL-compatible database called Aurora, and it also uses relational database services, including Dynamo DBL, NoSQL database, and ElastiCache.

Google Cloud Platform: GCP has a hyper-active and unified object storage system, making the data storage functions easier. It includes both predefined and custom functionality, such as predefined machine kinds, pre-second billing, and Linux and Windows support. In terms of database, GCP provides SQL-based Cloud SQL and an RDBMS, Cloud Spanner. For a NoSQL database system, GCP uses Cloud Bigtable and Cloud Datastore.

Conclusion

After considering the key components of a cloud services provider, we can deduce that every system has its own way of execution and leverages different components. Hence, it is important to identify your requirements and match them with the offerings. With careful planning and analysis of your needs, you can identify the cloud services that can satisfy them while considering the costs.

DEV Community: Dev Patel

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What is GitOps and How to setup GitOps using CI/CD pipeline?

CI/CD with GitOps

Problem 1.

Problem 2.

Why GitOps?

Setup with GitOps

1. Repository setup

2. Create Git workflow

3. Define GitOps Actions for our project

4. Setup infrastructure for GitOps

Conclusion

Azure Data Factory Overview For Beginners

Setting Up Azure Data Factory

1. Launch Data Factory

2. Add Resource

3. Select Region

4. Enter a Name and Version

5. Git Configuration and Review

6. Azure Data Factory Studio:

Next Step – Create a Linked Service

1. Create New Service

2.Azure Blob Storage

Moving On → Creating Datasets

InputDataset

OutputDataset

Creating a Pipeline in Azure Data Factory Studio

Next Up — Debugging the Pipeline

Manually Triggering the Pipeline

Pipeline Monitoring

Scheduling a Pipeline Trigger

Conclusion

ETL vs ELT – Which is Better for a Modern-Day Business?

What is ELT and ETL?

1.Extract, Transform, Load (ETL)

Disperse Data Sources

Shift from Legacy Systems

2.Extract, Load, Transform (ELT)

1.Large Volume of Data

2.Real-Time Data Requirement

Going Through Each Stage Separately

1. Extract

Full Extraction

Partial Extraction

Incremental Extraction

2.Transform

3.Load

Head-to-Head Comparison of ELT vs. ETL

Conclusion

Cloud Application Modernization Services are Critical for Business Success

Explaining Application Modernization and Optimization

Application Modernization and Optimization

Monolithic to Granular Structure:

Cost-Efficient

Enhances Performance

Application Modernization Patterns

Rehosting:

Refactoring

Key Technologies Used for Cloud Application Modernization Services

Cloud Computing

Containerization

Cloud Wars: AWS Vs. Azure Vs. Google Cloud

Introducing AWS, Azure, and Google Cloud Platform

Major Points of Differentiation Between Three Major Cloud Services

Conclusion

Problem 1.