Do you want to know How to Become a Data Engineer?.. If yes, this article is for you. In this article, you will find a step-by-step roadmap for a Data Engineer. Along with that, at each step, you will find resources to learn data engineering topics. So without any further ado, let’s get started-How to Become a Data Engineer?
- Convert inaccurate data into a usable form for further analysis.
- Develop large data warehouses using ETL Tools.
- Develop, test, and maintain architectures.
- Develop dataset processes.
- Deploy Machine Learning (ML) and statistical methods.
So, these are some main roles and responsibilities of a data engineer. But most roles and responsibilities differ upon the companies.
Skills Required for Data Engineer:
Before deep dive to the skills, Like to share one analysis regarding Data Engineering Skills-
According to analysis, the most demanding skills or technologies for Data engineers are SQL, Python, Spark, AWS, and so on.
Now, let’s discuss skills required for Data Engineer:-
- Programming Language
- Depth Database Knowledge
- Big Data Tools
- Data Warehousing and ETL Tools
- Cloud Platforms
- Knowledge of Operating System
- Machine Learning
- Data Visualization Tools
These are some mandatory skills required for Data engineers. let’s see, in what order you have to learn these skills.
How to Learn Data Engineering
Step 1: Programming Languages
To become a Data Engineer, One should have a good understanding of Programming languages and Software Engineering concepts. The industry standard mostly revolves around two technologies: Python and Scala.
Start with Python and after having a good understanding of Python, learn the basics of Scala. You can learn these languages with these resources-
Resources
- Python for Everybody – This is one of the most popular and highly enrolled Specialization Programs. 1.7 M students have enrolled in this specialization program. This specialization program will teach you fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language.
- Functional Programming in Scala Specialization– This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. This specialization is a 5 Course Series. You will learn how to Manipulate data with Spark and Scala, write purely functional programs using recursion, pattern matching, higher-order functions, and much more.
- The Python Tutorial (PYTHON.ORG)
- Python for Absolute Beginners! (Udemy)
- Python 3 Tutorial (SOLOLEARN)
- CS DOJO (YouTube)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
- Python Crash Course (Book)
Step 2: Depth Knowledge of SQL and NoSQL
Start with learning SQL. SQL is the most demanding skill for Data Engineer. You should have a strong understanding of SQL. Knowledge of NoSQL is also required because sometimes you have to deal with unstructured data.
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– One 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 3: Big Data Tools
Once you master Python 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 mandatory.
One 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)- 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 4: ETL Tools
Data Engineers have to perform ETL operations. That’s why one should be expertise with ETL tools like- Informatica & Talend. You can learn these tools through online courses. I have found some resources for learning these tools-
Resources
- INFORMATICA TUTORIAL (Guru99)– This tutorial is completely free. In this tutorial, you will learn how Informatica does various activities like data cleansing, data profiling, transforming, and scheduling the workflows from source to target in simple steps, etc.
- Informatica Training & Certification (Edureka)– This training will make you expertise in Advanced Transformations, Informatica Architecture, Data Migration, Performance Tuning, Installation & Configuration of Informatica PowerCenter.
- Data integration (ETL) with Talend Open Studio ( Udemy)– In this course, you will learn to install Talend, how to navigate, and use the interface efficiently. Along with that, you will learn how to import data into Talend and then perform the various transformation of data, cleansing, filtering, lookups, concatenations, and much more.
Step 5: Cloud Computing (GCP, AWS, AZURE)
More and more application workloads are moving to the different cloud platforms. That’s why the data science/engineering community must have a good understanding of these clouds. You can learn about Google Cloud Platform or AWS or AZURE.
One can learn Cloud Computing with these courses-
Resources
- Data Engineering, Big Data, and Machine Learning on GCP Specialization (Coursera)- This specialization program offered by Google Cloud will provide you with a hands-on introduction to designing and building data pipelines on the Google Cloud Platform. In this program, you will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and derive insights via presentations, demos, and hands-on labs.
Step 7: Machine Learning & Data Visualization Tools
As a Data Engineer, it’s not mandatory to have Machine Learning knowledge, but having a basic knowledge of ML Algorithms is a plus for you. You can learn Machine Learning Basics with the “Machine Learning by Andrew Ng” FREE Course.
One should have a basic understanding of Data Visualization tools. You can learn either Tableau or PowerBI. One can grab knowledge of Data Visualization from below courses:-
Resources
- Data Visualization in Tableau– Udacity– This free course will give knowledge of data visualization using Tableau. The course begins with the fundamentals of data visualization such as why visualization is so important in analytics, exploratory versus explanatory visualizations, and data types and ways to encode data.
- Data Visualization with Tableau Specialization– This specialization program is intended for newcomers to data visualization with no prior experience using Tableau. At the end of this program, you will be able to generate powerful reports and dashboards that will help people make decisions and take action based on their business data.
- Data Visualization with Python– This course will teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. This course will use several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
Step 8: Practicing with Real-World Projects
Well Congratulation! 😊 You are now well versed in Data Engineering Skills. It’s time to start working on some Real-World projects. Projects are most important to get a job as a Data Engineer.
The more projects you will do, the more deep understanding of Data you will grasp. Projects will also provide more privilege to your CV.
For learning purposes, you can start with real-time streaming data from social media platforms where APIs are available like Twitter.
Step 9: Take your First Step as Data Engineer
Now you have all the data engineering skills and projects, it’s time to take your first step as Data Engineer. And that is Make a Strong Resume.
Your Resume is the first impression for any recruiters. No matter how skilled you are, if your resume is not attractive, sorry you will not get an interview call. That’s why you shouldn’t ignore your CV.
If you want that your resume will get more privilege than others, then you should keep these things in mind-
- Read the job profile and check what skills they require, then see how many skills you have. Suppose in the job description they mentioned Knowledge of Python, and you have Python Knowledge, then definitely write “Knowledge of Python as the first skill. You can repeat the same for other skills too, just compare your skills and the skills written in the Job Description. This tip will definitely help you.
- The template of your resume should be classic.
- Avoid templates with so many graphics. It gives a bad impression to the recruiter.
- Don’t hesitate about white spaces. That means don’t try to fill the full page with text. Leave some white space that looks clean.
- Don’t write a long text like a story. It should be precise and simple.
- Mention only the most important Data Engineering Projects. Don’t mention very basic projects.
- After finalizing your resume, you need to check for grammar and spelling mistakes. Because of any grammar or spelling mistakes, your full work will be wasted. So thoroughly check for grammar and spelling before sending it to the company. You can check it on Grammarly.
That’s all!. If you follow these steps and gain these required skills, then no one can stop you to land in Data Engineering Field.
Conclusion
In this article, I have discussed How to Become a Data Engineer? 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! 😊
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