DEV Community: aayushi94

7 Important Characteristics Of An Effective Online IT Training

aayushi94 — Mon, 24 Jun 2019 06:52:35 +0000

The goal of education is not to increase the amount of knowledge but to create the possibilities for a child to invent and discover, to create men who are capable of doing new things.

Learning has been the cornerstone of human evolution and we very quickly realized the benefits of passing knowledge to the following generations. Even though one of the most ancient activities, much of humanity today is still dedicated to perfecting the art of Training and imparting knowledge, for faster development and progress of this Human race. In last 50 years, we have seen another renaissance, fueled by IT and today we can believe 50 years from now the World will be a better place and we will be crossing new frontiers with the help of Elon Musks’s of our generation.

The leaps in Communication Technology fueled by the internet has given us better and better models of imparting knowledge and online IT training has become the de-facto way to spread it. While dismantling the challenges of Location-Access-Ability; Online IT training companies are continuously innovating to tackle these 2 major challenges of learning: Engagement and Motivation.

To solve these, the online IT learning ecosystem has developed some essential characteristics. Let’s have a look at them to understand their contribution and possibilities.

Latest Content: Curriculum

The more I know, the more I know how less I know.

We like fresh food, latest news then why not the most updated when it comes to Learning?

This applies to your professional life as well, because, with quicker release cycles, Technology is changing, so is the syllabus.
World class training companies ensure that content is not just updated but is also easy to consume by learners.

The questions they frequently asked themselves are:

                -> Is content Latest and up to the mark?

               -> Is it aligned with the Industry requirements?

               -> What will be my learning objectives and Key Takeaways?

               -> How will it benefit my Career?

Besides Content, the frequency and recency of updates to the curriculum becomes important. It should be relevant to the demands of the industry and subject matter experts should be hands-on to quickly give shape to the latest developments for up to date learning.

To sum it up, the latest content makes your learning more engaging and a great team keeps you up to the date.

Quality Instructors

Technology is just a tool. In terms of getting the kids working together and motivating them, the teacher is most important. – Bill Gates

The goal of learning or any online course is to gain in-depth knowledge of the related subject and an instructor is the master director of this orchestra. Learning is sticky and effective when explained by analogies and simple examples. Our ancestors understood this and hence our ancient texts are filled with lessons, told to us via stories.

Similarly, the instructors should have relevant hands-on skills and should be able to articulate different concepts in simplest manners possible.

Just like any world-famous performer, an experienced Instructor is able to effectively assess the different needs of learners in his batch and fine-tune his delivery.

The best Instructors bring not just their subject matter expertise to the table, but also a brilliant understanding of human psychology and patterns, solving an important part of engagement and motivation piece of this puzzle.

Learner’s Platform

So, what is it? Is it a Software, Classroom, Library or an University?

Honestly, IT learning is changing fast. Our understanding of learning ecosystems is evolving rapidly and experts are debating should we even try to find analogies with older learning systems. While this debate might take a few more years to come up with an outcome, we can for now define Learning Management System as a platform where you will find everything related to your e-learning.
You can expect to find your class content, curriculum, projects, assignments or any kind of support for doubt resolutions.

For example, on Edureka’s LMS you will find more than 100 up to date courses which helps learners find the best courses and certifications for them. And yes, Lifetime access to the Learning Management System (LMS) is essential so that you can refer to your class presentations, quizzes, installation guides, data sets & Instructor’s recordings.

An effective LMS is measured by how well it can engage the learner and help him structure his learning.

Real Life Projects & Case Studies

The more you sweat in peace, the less you bleed in war.

Real life projects conceptualized by industry experts prepare you for industry exposure and test of your learning in the corporate world. If your course pedagogy has less emphasis on hands-on/practicals, then you should think twice about this theoretical training.
You should look around at interesting problems the world is facing, and can they be solved with the skills you are learning. The projects in your training should be contemporary and solving the problems which the world is facing now.

What is the point of creating a Sudoku app when real-life challenges demand building world-class credit card fraud detection systems?

To begin, is job half well done. Most of us face problems in setting up environments to execute a simple piece of code, and that’s where we give up. To overcome this problem, Cloud Lab was born. It is a pre-configured environment which can be accessed by anyone via a browser with minimal system configurations. In simple terms, you can use Cloud lab to execute all in-class demos/ assignments and real-life projects like a breeze without worrying about setup & hardware frustrations.

So while real-life projects keep you engaged, Cloud Lab ensures there is no demotivation!

Personal Learning Manager (PLM)

Tell me and I forget, teach me and I may remember, involve me and I learn. – Benjamin Franklin

The best Curriculum and the most able Instructors can help us move forward in our learning journey but all of us would admit that our best learning happened beyond classrooms. The discussions with peers, colleagues, friends added those extra perspectives which were essential to our education. Few online training companies understood its importance and the concept of a Personal Learning Manager (PLM) was born, and it has given splendid results. With regular interactions and interventions, a PLM ensures that you don’t get stuck anywhere in your training and your progress is as per the timelines.

For example, in Edureka, in-house PLMs resolve all queries of the learners assigned to them. They cover broad scope and are champions of ensuring learning happens. One can ask anything from batch timings to class reminders to course content to understand more about the online way of learning.

Dedicated PLMs not just ensure that the learning happens seamlessly up to certification but provide lifetime learning assistance to any learner. Now, do you think anything else contributes more to the engagement and motivation than PLM in online training? They are not going away soon, that’s for sure!

Technical Experts

Great things are done by a series of small things brought together.

Anyone who has achieved something in life will agree that success did not come with one big action.
It was a series of small tasks which were completed with dedication and sincerity.

Hidden behind these small tasks are hundreds of queries. Learning systems the world over struggle with this challenge of capturing and solving unexpected queries and bottlenecks for the learner, to ensure his learning path is in right direction and on schedule.

The online medium gives IT training companies the ability to provide help even for smallest of the queries, anytime, anywhere.

Technical experts have become an important part of these learning systems, and they do specialized jobs of solving queries, even if it means using tools like screen sharing to resolve the silliest of the bugs and thus ensuring the learner continues on his fabulous journey of learning!

These technical experts ensure that you spend your maximum time in engaging with learning and not in struggling with minor but detrimental setbacks.

Learner’s Engagement

The need for connection and community is primal, as fundamental as the need for air, water, and food.

As per the critics of e-learning, the lack of face to face human interaction with Instructors and batch mates reduces the efficacy of online learning.
The online training companies realized this and that is why we have seen some new kinds of characteristics developed by them. Online may not provide face to face human involvement but it compensates by connecting us to people across the world, who are making the same journey.

Learning portals today have a discussion forum for lively interaction among learners across the batches. These student forums helps the learners interact with the fellow learners, get lifetime access to the Learner community and even make friends for life!

Being a part of such a community gives you access to the global community forum that further facilitates learning through peer interaction and knowledge sharing.

Now since you are more equipped to identify the characteristics, there should be no more excuses for lack of confidence in online learning.

Now, what are you waiting for? Go, learn!

Source: Edureka

What Is Java? A Beginner’s Guide to Java and Its Evolution

aayushi94 — Thu, 13 Jun 2019 12:24:02 +0000

Java is a general-purpose, concurrent, object-oriented, class-based, and the runtime environment(JRE) which consists of JVM which is the cornerstone of the Java platform. This blog on What is Java will clear all your doubts about why to learn java, features and how it works.

What is Java used for?

Before I answer the question, what is Java used for, let me brief you about why you should choose Java. Java is highly popular and has dominated this field from early 2000’s till the present 2018.

Java has been used in different domains. Some of them are listed below:

Banking: To deal with transaction management.
Retail: Billing applications that you see in a store/restaurant are completely written in Java.
Information Technology: Java is designed to solve implementation dependencies.
Android: Applications are either written in Java or use Java API.
Financial services: It is used in server-side applications.
Stock market: To write algorithms as to which company they should invest in.

Big Data: Hadoop MapReduce framework is written using Java.
Scientific and Research Community: To deal with huge amount of data.
Wait! Java can do more.

Let’s see how some of the technologies make use of Java as an essential core of their functionalities.

You can see in the above image, Java is an ocean of opportunities.

Let us see a brief history of Java.

History of Java

Java is a programming language developed by James Gosling with other team members named Mike Sheridan and Patrick Naughton also called as Green Team in 1995 for Sun Microsystems for digital devices such as set-top boxes, televisions etc. Now, let us see in detail what is Java.

What is Java?

It is an object-oriented language similar to C++, but with advanced and simplified features. Java is free to access and can run on all platforms.

Java is: –

Concurrent where you can execute many statements instead of sequentially executing it.

Class-based and an object-oriented programming language.
Independent programming language that follows the logic of “Write once, Run anywhere” i.e. the compiled code can run on all platforms which supports java.

In simple words, it is a computing platform where you can develop applications.

Features of Java

Simple: Java has made life easier by removing all the complexities such as pointers, operator overloading as you see in C++ or any other programming language.

Portable: Java is platform independent which means that any application written on one platform can be easily ported to another platform.

Object-oriented: Everything is considered to be an “object” which possess some state, behavior and all the operations are performed using these objects.

Secured: All the code is converted in bytecode after compilation, which is not readable by a human. and java does not use an explicit pointer and run the programs inside the sandbox to prevent any activities from untrusted sources. It enables to develop virus-free, tamper-free systems/applications.

Dynamic: It has the ability to adapt to an evolving environment which supports dynamic memory allocation due to which memory wastage is reduced and performance of the application is increased.

Distributed: Java provides a feature which helps to create distributed applications. Using Remote Method Invocation (RMI), a program can invoke a method of another program across a network and get the output. You can access files by calling the methods from any machine on the internet.

Robust: Java has a strong memory management system. It helps in eliminating error as it checks the code during compile and runtime.

High Performance: Java achieves high performance through the use of bytecode which can be easily translated into native machine code. With the use of JIT (Just-In-Time) compilers, Java enables high performance.

Interpreted: Java is compiled to bytecodes, which are interpreted by a Java run-time environment.

Multithreaded: Java supports multiple threads of execution (a.k.a., lightweight processes), including a set of synchronization primitives. This makes programming with threads much easier.

Components in Java

JVM (Java Virtual Machine)

It is an abstract machine. It is a specification that provides a run-time environment in which Java bytecode can be executed. It follows three notations:

Specification: It is a document that describes the implementation of the Java virtual machine. It is provided by Sun and other companies.
Implementation: It is a program that meets the requirements of JVM specification.
Runtime Instance: An instance of JVM is created whenever you write a java command on the command prompt and run the class.

JRE (Java Runtime Environment)

JRE refers to a runtime environment in which Java bytecode can be executed. It implements the JVM (Java Virtual Machine) and provides all the class libraries and other support files that JVM uses at runtime. So JRE is a software package that contains what is required to run a Java program. Basically, it’s an implementation of the JVM which physically exists.

JDK(Java Development Kit)

It is the tool necessary to:-

Compile
Document
Package Java programs.

The JDK completely includes JRE which contains tools for Java programmers. The Java Development Kit is provided free of charge. Along with JRE, it includes an interpreter/loader, a compiler (javac), an archiver (jar), a documentation generator (Javadoc) and other tools needed in Java development. In short, it contains JRE + development tools.

Source : Edureka

Python: Interesting Facts You Need To Know

aayushi94 — Tue, 11 Jun 2019 11:25:03 +0000

What is the first thing that comes to your mind when you hear the word “Python”? Is it the ferocious snake or an interesting programming language?

Though both these terms sound similar, they end up having contrasting effects!!

While Python(snake) is the dangerous hostile creature, Python(programming language) on the other hand, is the friendlier one. So let us know more about the friendlier one.

Do you know who invented Python?

Guido Van Rossum: A Dutch programmer, popularly known as the author of Python programming language. He has created Python in 1989 and has worked for Google and Dropbox!

Ever wondered why is it named Python?

There’s an interesting story about it. While implementing Python, Van Rossum was also reading the published scripts from “Monty Python’s Flying Circus”, a BBC comedy series from the 1970s. Since he wanted a short, unique and slightly mysterious name for his invention, he got inspired by the series and named it Python!

What are some real-life applications?

Over 75% of what you watch is recommended by Netflix.

Recommendations are made by Machine Learning!

Facebook is using Machine Learning to tag in posts and images.

Alexa is Amazon’s Virtual Personal Assistance used for speech recognition, weather detection etc.

Do you know different Domains catered by Python?

All the above technologies make use of Python as an essential core of their functionalities. So to sum up, Python is an ocean of opportunities!!

Python has grown to become a popular programming language. Let me introduce you to the BiG players:

The simplicity of Python is what makes it so popular!
Let’s look at some FEATURES:

Python is not that complex! It is a high-level dynamic programming language and is quite easy to learn. Python code has a very ‘natural’ style to it, which makes it easy to read and understand.

It’s really a great language for beginners, all the way up to seasoned professionals!

So, for those of you familiar with Java or C++, Python will break the mold you have built for a typical programming language. The above screenshot displays the trends of these programming languages as per Google.

Python has a strong spike in popularity over the last 1 year!!

In this constantly evolving and adapting world, Python brings in peace with its simplicity and ease that supports the creation and maintenance of plethora of applications.

Source : Edureka

Data Analyst vs Data Engineer vs Data Scientist: Skills, Responsibilities, Salary

aayushi94 — Mon, 03 Jun 2019 08:34:19 +0000

Data Analyst vs Data Engineer vs Data Scientist

Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer and data scientist.

Who is a Data Analyst, Data Engineer and Data Scientist?

Data Analyst - Data Analyst analyzes numeric data and uses it to help companies make better decisions.

Data Engineer - Data Engineer involves in preparing data. They develop, constructs, tests & maintain complete architecture.

Data Scientist - A data scientist analyzes and interpret complex data. They are data wranglers who organize (big) data.

Data Analyst vs Data Engineer vs Data Scientist

Data Analyst
Most entry-level professionals interested in getting into a data-related job start off as Data analysts. Qualifying for this role is as simple as it gets. All you need is a bachelor’s degree and good statistical knowledge. Strong technical skills would be a plus and can give you an edge over most other applicants. Other than this, companies expect you to understand data handling, modeling and reporting techniques along with a strong understanding of the business.

Data Engineer
Data Engineer either acquires a master’s degree in a data-related field or gather a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

Data Scientist
Data Scientist is the one who analyses and interpret complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc.

Skill-Sets

Data Analyst -
Data Warehousing
Adobe & Google Analytics
Programming knowledge
Scripting & Statistical skills
Reporting & data visualization
SQL/ database knowledge
Spread-Sheet knowledge

Data Engineer -
Data Warehousing & ETL Statistical & Analytical skills
Advanced programming knowledge
Hadoop-based Analytics
In-depth knowledge of SQL/ database
Data architecture & pipelining
Machine learning concept knowledge
Scripting, reporting & data visualization

Data Scientist -
Statistical & Analytical skills
Data Mining
Machine Learning & Deep learning principles
In-depth programming knowledge (SAS/R/ Python coding)
Hadoop-based analytics
Data optimization
Decision making and soft skills

Data Analyst vs Data Engineer vs Data Scientist Skill Sets

As mentioned above, a data analyst’s primary skill set revolves around data acquisition, handling, and processing. A data engineer, on the other hand, requires an intermediate level understanding of programming to build thorough algorithms along with a mastery of statistics and math! And finally, a data scientist needs to be a master of both worlds. Data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.

Now that we have a complete understanding of what skill sets you need to become a data analyst, data engineer or data scientist, let’s look at what the typical roles and responsibilities of these professionals.

Next, let us compare the different roles and responsibilities of a data analyst, data engineer and data scientist in their day to day life.

Roles And Responsibilities

Data Analyst -
Pre-processing and data gathering
Emphasis on representing data via reporting and visualization
Responsible for statistical analysis & data interpretation
Ensures data acquisition & maintenance
Optimize Statistical Efficiency & Quality

Data Engineer -
Develop, test & maintain architectures
Understand programming and its complexity
Deploy ML & statistical models
Building pipelines for various ETL operations
Ensures data accuracy and flexibility

Data Scientist -
Responsible for developing Operational Models
Carry out data analytics and optimization using machine learning & deep learning
Involved in strategic planning for data analytics
Integrate data & perform ad-hoc analysis
Fill in the gap between the stakeholders and customer

Data Analyst vs Data Engineer vs Data Scientist Roles

Now data scientist and data engineers job roles are quite similar, but a data scientist is the one who has the upper hand on all the data related activities. When it comes to business-related decision making, data scientist have higher proficiency.

After these two interesting topics, let’s now look at how much you can earn by getting into a career in data analytics, data engineering or data science.

Data Analyst vs Data Engineer vs Data Scientist: Salary

Data Analyst - $59000 /year
Data Engineer - $90,8390 /year
Data Scientist - $91,470 /year

Data Analyst vs Data Engineer vs Data Scientist Average Salary

The typical salary of a data analyst is just under $59000 /year. A data engineer can earn up to $90,8390 /year whereas a data scientist can earn $91,470 /year.

Looking at these figures of a data engineer and data scientist, you might not see much difference at first. But, delving deeper into the numbers, a data scientist can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.

If you wish to know more about Data scientist salary, job openings, years of experience, geography, etc., here’s a full-fledged article on Data Scientist Salary for your reference.

If you are someone looking to get into an interesting career, now would be the right time to up-skill and take advantage of the Data Science career opportunities that come your way.

Who is a Data Scientist?

aayushi94 — Mon, 20 May 2019 06:38:46 +0000

Who is a Data Scientist?

The other day, I read an article on venturebeat.com that revealed how advanced data analytics helped Obama win the 2012 presidential elections! This and more stories like Bank of America benefiting from its data-intensive technologies or Wipro putting in $30 million in a US-based data science firm or Paypal hiring data scientists, give a clear reflection that Data Scientist is the sexiest job of the 21st century as quoted by Harvard.

After hearing so much about Data Science, let’s get into some basics!

What is Data Science all about?

Some call it as Civil Engineering of data, and others call it a Discipline in itself; after all, what is Data Science all about?

Data Science is a term which came into popularity by EMC2. It is a process of extracting valuable insights from “data”.

As we are living in the Big Data Era, Data Science is becoming a very promising field to harness and process huge volumes of data generated from various sources. Data Science is a vast discipline in itself, consisting of specialized skill-sets such as statistics, mathematics, programming, computer science and so on. Data science consists of several elements, techniques and theories including math, statistics, predictive analysis, data modelling, data engineering, data miming, and visualization.

The discipline of data science hasn’t evolved overnight. In fact, it has been there for years in the form of business analytics or competitive intelligence, but it is now only that its true potential has been realized. The main purpose of Data Science is to extract and interpret data effectively and present it in a simple, non-technical language to the end users.

Thus, Data Science is all about constructing useful information, thereby, converting it into data-driven products!

Who is a Data Scientist?

Is he/she someone struggling with data all day and night or experimenting in his/her laboratory with complex mathematics? After all, ‘Who is a Data Scientist’?

There are several definitions available on Data Scientists. In simple words, a Data Scientist is one who practices the art of Data Science. The highly popular term of ‘Data Scientist’ was coined by DJ Patil and Jeff Hammerbacher. Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines. They work with several elements related to mathematics, statistics, computer science, etc (though they may not be an expert in all these fields).

Data Scientists are Business Analysts or Data Analysts, with a difference!

Though the initial training or basic requirements are similar for all these disciplines, Data Scientists require:

Whether an agricultural scientist wants to know the percentage increase in the yield of wheat this year as compared to last year’s (and the reasons associated with it) or if a financial company wants to classify its customers based on their creditworthiness (before granting loans) or whether a retail organization wants to rewards extra points to its loyal customers, all need data scientists to process large volume of both structured and unstructured data in order to take crucial business decisions.

The main challenge that today’s Data Scientists face is not to find solutions to the existing business problems but to identify the problems that are most crucial to the organization and its success.

Why Data Scientists are called ‘Data Scientists’?

The term “Data Scientist” has been coined after considering the fact that a Data Scientist draws a lot of information from the scientific fields and applications whether it is statistics or mathematics. They make a lot of use of the latest technologies in finding solutions and reaching conclusions that are crucial for an organization’s growth and development. Data Scientists present the data in a much more useful form as compared to the raw data available to them from structured as well as unstructured forms.

Just like any other scientific discipline, data scientists always need to ask and find answers of What, How, Who and Why of the data available to them. They are required to make a clear defined plan and work towards achieving the results within limited time, effort and money.

Three components of Data Science:

Data science consists of three components, that is, organizing, packaging and delivering data (OPD of data). Let’s have a brief look into these:

Organizing the data:
Organizing is where the planning and execution of the physical storage and structure of the data takes place after applying the best practices in data handling.
Packaging the data:
Packaging is where the prototypes are created, the statistics is applied and the visualisation is developed. It involves logically as well as aesthetically modifying and combining the data in a presentable form.
Delivering the data:
Delivering is where the story is narrated and the value is received. It makes sure that the final outcome has been delivered to the concerned people.

What skills does a Data Scientist possess?

Role of a Data Scientist is indeed a challenging one! Though the skill-sets and competencies that Data Scientists employ differ extensively, to be an efficient Data scientist, he should:

1.Be very innovative and distinctive in his approach in applying various techniques intelligently to extract data and get useful insights in solving business problems and challenges.

2.Have the ability to locate and construe rich data sources.

3.Have a hands-on experience in Data mining techniques such as graph analysis, pattern detection, decision trees, clustering or statistical analysis.

4.Develop operational models, systems and tools by applying experimental and iterative methods and techniques.

5.Analyze data from a variety of sources and perspectives and find out hidden insights.

6.Perform Data Conditioning – that is, converting data into a useful form by applying statistical, mathematical tools and predictive analysis.

7.Research, analyze, execute, and present statistical methods to gain practical insights.

8.Manage large amounts of data even during hardware, software and bandwidth limitations.

9.Create visualizations that will help anyone understand the trends in data analysis with ease.

10.Be a team leader and communicate effectively with other business analysts, product Managers and Engineers.

In brief, a Data Scientist should be very strong in any of these skills (Programming, Statistics, Mathematics, Business skills) and at the same time have a working-knowledge of the related skill-sets. For instance, a person with strong statistics background can become a data scientist while acquiring substantial skills in coding and business.

A Data Scientist is like a webmaster, who not only needs to be a jack of all trades but also a master of atleast one of the above fields.

So, what does a Data Scientist do?

A data scientist has a dual role – that of an “Analyst” as well as that of an “Artist”! Data scientists are very curious, who love a large amount of data, and more than that, they love to play with such huge data to reach important inferences and spot trends! This is what distinguishes a Data Scientist from a traditional Data Analyst. A Data scientist not only refers one particular source such as a social media site or a log file but various other sources with the aim to find out a hidden insight that can prove to be very significant for the organization. They perform “what if” analysis, ask questions and look at the data from different angles and transform the big data into the next Big idea!

Conway Diagram:

This is the Conway Venn Diagram on Data Science illustrated by the famous Data Scientist Drew Conway. This diagram presents Data science as a combination of much-in demand skills such as hacking skills, math skills and knowledge of statistics including substantive expertise.

Data Science is also an Art!

Data science is not only a science or a technique, it is also an ‘Art’. Data Science is an art of listening to your intuitions while facing huge amount of data, classifying it, evaluating it and reaching conclusions. Not everyone is blessed with this art! Data scientists need to be really creative in visualizing the data in various graphical forms and present the highly complex data in a very simple and friendly way! If a Data scientist is able to convert terrifying Petabytes of structured as well as unstructured data (images, videos, log files, etc) into very easy and simple format, he is an – ‘Artist’!

After all only a skilful Data Scientist can manage McDonald’s Database or videos uploaded on Youtube, or Tesco’s huge volume of data or GE’s Healthcare data or managing the data related to thousands blood samples of patients at Apollo or unstructured data generated from X-rays!

Data Scientist Jobs

“US faces shortage of 140,000 to 190,000 people “with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”
– Mckinsey Global Institute

As Data Science is an emerging field, there is a plethora of opportunities available world across.
Just browse through any of the job portals; you will be taken aback by the number of job openings available for Data scientists in different industries, whether it is IT or healthcare, retail or Government offices or academics, life sciences, oceanography, etc. Venture Capitalists have never showed such an excitement in investing money as in the case of data driven start-ups.

Data Scientist Salary:

Below you can find very lucrative pay-packets offered to Data scientists!

Whether you call them Data scientists or Data Gurus or by some other fancy name, the fundamentals remain the same! The world is in acute need of smart and creative people who can dive deep inside the ocean of Big Data and save the world from ignorance and provide valuable insights into businesses and help the World Economy grow!

Splunk Tutorial For Beginners: Explore Machine Data With Splunk

aayushi94 — Tue, 14 May 2019 08:25:55 +0000

In this blog, I am going to talk about one of the most trending analytical tool Splunk, which is winning hearts in the fields of big data and operational intelligence. It is a horizontal technology used for application management, security and compliance, as well as business and Web analytics, with tremendous market demand for professionals with Splunk Certification Training. Splunk is a complete solution which helps in searching, analyzing and visualizing the log generated from different machines. Through this Splunk tutorial, I will introduce you to each aspect of Splunk and help you understand how everything fits together to gain insights from it.

But before I start, let me list down the topics that I will be discussing:

Splunk & it’s advantages
Architecture
Splunk Pricing
Configuration Files

Splunk Introduction

Before getting started with Splunk, have you ever realized the challenges with unstructured data and the logs coming in real-time? For example- live customers queries, increased number of logs through which the size of the dataset keeps on fluctuating every minute. How can all of these problems be tacked? Here, Splunk comes to the rescue.

Splunk is a one-stop solution as it automatically pulls data from various sources and accepts data in any format such as .csv, json, config files, etc. Also, Splunk is the easiest tool to install and allows functionality like: searching, analyzing, reporting as well as visualizing machine data. It has a huge market in the IT infrastructure and business. Many big players in the industry are using Splunk such as Dominos, Adobe, Bosch, Vodafone, Coca-Cola etc.

As you can see in the above image, Splunk has some really cool advantages:

-Splunk collects data in real-time from multiple systems
-It accepts data in any form, example- log file, .csv, json, config etc.
-Splunk can pull data from database, cloud and any other OS
-It analyze and visualize the data for better performance
-Splunk give alerts/ event notifications
-Provides real-time visibility
-It satisfies industry needs like horizontal scalability (using many systems in parallel)

Splunk Architecture

Splunk’s architecture comprises of various components and its functionalities. Refer to the below image which gives a consolidated view of the components involved in the process:

As you can see in the above image, splunk CLI/ splunk web interface or any other interface interacts with the search head. This communication happens via Rest API. You can then use search head to make distributed searches, setup knowledge objects for operational intelligence, perform scheduling/ alerting and create reports or dashboards for visualization. You can also run scripts for automating data forwarding from remote Splunk forwarders to pre-defined network ports. After that you can monitor the files that are coming at real time and analyze if there are any anomalies and set alert/ reminders accordingly. You can also perform routing, cloning and load balancing of the data that is coming in from the forwarder, before they are stored in an indexer. You can also create multiple users to perform various operations on the indexed data.

Splunk Pricing

While indexing the data, the first question that will arise is “How much will it cost?”. Well, it all depends on the volume that you are indexing. So, in a nutshell:

“More you index the data, the less you pay”

There are major two Splunk editions:

-In Splunk free edition, you can collect and index data upto 500 MB per day. It can be used only by one user where you can search, analyze and visualize the data.
-Splunk Enterprise edition starts from $225 per month. There is no limit for users and you can scale unlimited amount of data per day. You are provided by Enterprise-Grade Support and you can also deploy on-premises in your own cloud, or use Splunk Cloud service. You can perform unlimited searches and monitor them accordingly. There are many such advantages with the enterprise edition.

We have different types of licenses, refer to the below screenshot.

-Enterprise Trial license: You can index 500MB per day but this license is valid only for 60 days. You have all the features enabled such as alerts, multiple user access, distributed search, clustering etc. Now after 60 days, it will convert this license into free license.
-Free License: In free license, you will not have any user access control, it will be only available for one user(Admin). There won’t be any user accounts available, clustering, distributed search and even alerts will be disabled. Therefore, it is only used for performance of log collection & log analyses.
-Forwarder License: Whenever you are setting up heavy forwarder, you should install a forwarder license on it, then only the Splunk instance will turn into a heavy forwarder.

Splunk Tutorial: Configuration Files

Configuration files play a very important role in the functioning of your Splunk environment. These configuration files contain Splunk system settings, configuration settings and app configuration settings. You can edit these files and accordingly changes will be reflected in your Splunk environment. However, the changes made to configuration files will be taken into effect only if the Splunk instance is restarted.

These configuration files can be found in the below places:

$SPLUNK_HOME/etc/system/default
$SPLUNK_HOME/etc/system/local
$SPLUNK_HOME/etc/apps/

Path where these configuration files are stored is consistent in all operating systems. They are always stored in $SPLUNK_HOME, the directory where Splunk is installed.There is another path where configuration files are stored: $SPLUNK_HOME/etc/users. In this folder, user specific settings in UI, user specific configurations and preference will be stored. As an administrator you can also store user specific settings for multiple Splunk users.

Everything you see in the UI is configurable/ modifiable via the configuration file. In fact there are a lot of options that cannot be edited via UI, but it is possible via CLI or by directly editing a configuration file. Moving ahead in splunk tutorial,let us know discuss the structure of these conf files.

Configuration File Structure

Each configuration file consists of one or more stanzas. Each stanza begins with a stanza header enclosed in square brackets followed by file settings. Each setting is an attribute value pair, where attributes are case-sensitive. Below is the basic pattern of a configuration file containing more than one stanza.

[stanza1]
=
=

For example:

[SSL]
serverCert =
password =

In the last section of this Splunk tutorial blog, I will talk about the most common configuration files in Splunk:

inputs.conf
outputs.conf
props.conf
savedsearches.conf
indexes.conf
authentication.conf
authorize.conf

That brings us to the end of this Splunk tutorial blog. I am pretty sure that by now, most of you have understood the fundamentals of Splunk, so you can start indexing data and gain insights from it.

Source: Edureka