How to Learn Big Data Step by Step?- [Resources Included]

Do you want to know How to Learn Big Data Step by Step?… If yes, this article is for you. In this article, you will find a step-by-step roadmap for Big Data. Along with that, at each step, you will find resources to learn Big Data topics.

So without any further ado, let’s get started-

How to Learn Big Data Step by Step?

Before moving to the step-by-step roadmap for Big Data, I would like to discuss the What is “Big Data”?

What is “Big Data”?

As the name sounds big data is a huge amount of data that is generated daily by everyone. This data may be anything, one Facebook post is also a kind of data.

Data is increasing very fast. According to one report, By 2025, it’s estimated that 463 exabytes of data will be created each day globally – that’s the equivalent of 212,765,957 DVDs per day!

Here Big data analytics come into place, to manage and process such a huge amount of data. This generated data is not in proper form, which means it is unstructured data. It may be image data, text data, audio data, and other kinds of data.

Big data analytics is basically the process of finding useful patterns from large amounts of unstructured data. It involves lots of steps starting from data cleaning to finding patterns. It is a concept to store and process huge amounts of data. Big data is defined by the 3 V’s-

1. Volume- It refers to the size of data, which means how much data is generated.
2. Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
3. Velocity- It refers to the speed of data, which means at what speed data is generated.

Now let’s move to the step-by-step roadmap for Big Data.

How to Learn Big Data Step by Step?

Step 1- Learn Unix/Linux Operating System and Shell Scripting

You should have good practice in shell scripting because many tools got the command line interface where the commands are based on the shell scripting and Unix commands.

With the help of Shell Scripting, you can build data pipelines. A shell script is a text file that contains a sequence of commands for a UNIX-based operating system.

You can learn Unix/Linux Operating System and Shell Scripting with these resources-

Resources

• Linux Command Line Basics (FREE Course)
• Shell Workshop (FREE Course)
• Configuring Linux Web Servers (FREE Course)
• Linux Fundamentals (Coursera)
• Introduction to Bash Shell Scripting (Coursera Project)

Step 2- Learn Programming Language (Python/Java)

Some major core modules of popular Big data tools are written in Java. That’s why Java is still the backbone for many big data frameworks.

Python can also do Big Data processing. But Java is kind of direct and you do not need to do it with the help of third-party aid.

You can learn Java or Python. It’s up to you.

Java has Hadoop, a framework with which you can create big data applications while Python has many tools and open-source libraries, a lot of them. If you’re a beginner, take up Python as it is relatively easy to grasp and execute. Otherwise, go for Java.

Now, let’s see the resources to learn Java and Python.

Python Resources

• The Python Tutorial (PYTHON.ORG)
• Python for Absolute Beginners! (Udemy)
• Python for Everybody  (Coursera)
• Python 3 Tutorial (SOLOLEARN)
• CS DOJO (YouTube)
• Programming with Mosh (YouTube)
• Corey Schafer (YouTube)
• Python Crash Course (Book)

Java Resources

• Java Programming Basics (Free Course)
• Become a Java Programmer (Udacity)
• Become a Java Web Developer (Udacity)
• Core Java Specialization (Coursera)
• Introduction to Java (Coursera)
• Java Programming Masterclass covering Java 11 & Java 17 (Udemy)

Step 3-  Learn SQL

SQL is the most demanding skill for Big Data. That’s why you should have a strong understanding of SQL. Knowledge of NoSQL is also required because sometimes you have to deal with unstructured data.

Playing around with SQL in relational databases helps us understand the querying process of large data sets.

You can learn SQL and NoSQL from these courses-

Resources

•  Learn SQL Basics for Data Science Specialization– Coursera– This specialization program is dedicated to those who have no previous coding experience and want to develop SQL query fluency. In this program, you will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.

• Excel to MySQL: Analytic Techniques for Business Specialization– This Specialization program is offered by Duke University. This is one of the best SQL online course certificate programs. In this program, you’ll learn to frame business challenges as data questions. You will work with tools like Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.

• W3Schools– You can learn DBMS and its concepts from the Free Tutorial of W3Schools.

• NoSQL systems– In this course, you will learn how to identify what type of NoSQL database to implement based on business requirements. You will also apply NoSQL data modeling from application-specific queries.

Step 4- Learn Big Data Tools

Once you master Python, Java, and SQL, the next step is to learn Big Data tools. Knowledge of Big Data tools like- Hadoop and MapReduce., Apache Spark, Apache Hive, Kafka, Apache Pig, and Sqoop is required.

You should have at least basic knowledge of all these tools. You can learn Big Data from these courses-

Resources

• Intro to Hadoop and MapReduce(Udacity)- This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.

• Spark (Udacity)- This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.

• Hadoop Developer In Real World (Udemy)- This course will cover all the important topics like HDFS, MapReduce, YARN, Apache Pig, Hive, Apache Sqoop, Apache Flume, Kafka, etc. The best part about this course is that this course not only gives basic knowledge of concepts but also explores concepts in deep.

• Big Data Specialization (Coursera)– In this specialization program, you will get a good understanding of what insights big data can provide via hands-on experience with the tools and systems used by big data scientists and engineers.

Step 5- Start Practicing with Real-World Projects

First of all Congratulation! You are now well versed in Big Data Skills. It’s time to start working on some Real-World projects. Projects are most important to getting a job as a Big Data Engineer.

The more projects you will do, the more in-depth understanding of Data you will grasp. Projects will also provide more privilege to your Resume.

For learning purposes, you can start with real-time streaming data from social media platforms where APIs are available like Twitter.

That’s all!. If you follow these steps and gain these required skills, then no one can stop you to land in Big Data Field.

Now, let’s see how to learn Big Data for free?

How to Learn Big Data for FREE?

You can check these FREE Big Data Online Courses-

1. Intro to Hadoop and MapReduce– Udacity

Time to Complete- 1 Month

This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.

This course will also help you to discover how HDFS distributes data over multiple computers and how MapReduce enables analyzing datasets in parallel across multiple machines.

In this course, you will also learn how to write your own MapReduce code and how to use common patterns for MapReduce programs to analyze Udacity forum data.

You Should Enroll if-

• You have basic programming skills in Python.

Interested to Enroll?

If yes, then start learning- Intro to Hadoop and MapReduce

2. Spark– Udacity

Time to Complete- 10 hours

This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.

Throughout this course, you will understand the big data ecosystem and learn when to use Spark and when not to use it. This course will also teach Data Wrangling with Spark, Debugging, and Optimization.

You will also use Spark’s Machine Learning Library to train machine learning models at scale.

You Should Enroll if-

• You are a student with programming and data analysis experience.

Interested to Enroll?

If yes, then check out all details here-Spark

3. Introduction to Big Data– Coursera

Rating- 4.6/5

Time to Complete- 17 hours

This is a Free to Audit course on Coursera. That means you can access the course material free of cost but for the certificate, you have to pay.

In this course, you will understand the Big Data landscape including examples of real-world big data problems and the V’s of Big Data (volume, velocity, variety, veracity, valence, and value), and why each impacts data collection, monitoring, storage, analysis, and reporting.

You will also identify what are and what are not big data problems and be able to recast big data problems as data science questions.

This course will summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system, and the MapReduce programming model.

You Should Enroll if-

• You are new to data science and want to learn Big Data.

Interested to Enroll?

If yes, then check out all details here- Introduction to Big Data

4. Introduction to Big Data with Spark and Hadoop– Coursera

Rating- 4.4/5

Time to Complete- 11 hours

This is a Free to Audit course on Coursera. That means you can access the course material free of cost but for the certificate, you have to pay.

In this course, you will learn the characteristics of Big Data and its application in Big Data Analytics. This course will provide an understanding of the features, benefits, limitations, and applications of some of the Big Data processing tools.

Then you will explore how Hadoop and Hive help leverage the benefits of Big Data while overcoming some of the challenges it poses.

You will also learn about the functions, parts, and benefits of Spark SQL and DataFrame queries, and discover how DataFrames work with SparkSQL.

At the end of this course, you will learn Resilient Distributed Datasets (RDDs), their uses in Apache Spark, and RDD transformations and actions.

You Should Enroll if-

• You have programming background in languages such as Python and SQL.

Interested to Enroll?

If yes, then check out all details here-Introduction to Big Data with Spark and Hadoop

So that’s all, only these skills are required to become a Big Data Engineer. Congratulations, it’s your first step toward Big Data.

But the most important thing is to keep enhancing your skills by working on more and more challenges.

The more you practice, the more knowledge of Big Data you will gain. So after completing these steps, don’t stop, just find new challenges and try to solve them.

Now it’s time to wrap up!

Conclusion

In this article, I have discussed How to Learn Big Data Step by Step? If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

Thank YOU!

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden