Data Engineering Roadmap

Data Engineering is the new trending profession. Due to its nascency, industry leaders are developing many ways to build the skills for Data Engineering which may seem overwhelming to you. We came across this interesting roadmap on Kaggle today and thought that we should share it with you. We’ve compiled our take on the Data Engineering Roadmap with end-to-end information about how you can kick start your careers in Data Engineering!

Why has Data Engineering become so significant?

The main target of every company is to improve their sales and generate more leads. In today’s Data-focused world, only data can help you reach this world. Therefore, Data Engineering is progressing rapidly, and there’s more and more demand for Data Engineering today. 

Wondering, why?

Well, mainly because the main aim of Data Engineering is to simplify the lives of Data scientists. They are the builders of Data. Without them, the massive amount of data generated each day would be of little value to the organization..

What is the role of a Data Engineer? What does their pay look like?

A Data Engineer designs and implements the infrastructure to collect and store data. They also pre-process the data and transform it into a usable format. To summarize, a Data Engineer builds Data pipelines and ensures a smooth flow of the data.

The average Data Engineer salary is anything between $64,000 and $132,000 and depends on your skills, role, and experience. On average, a Data Engineer makes around $129,001 annually in the United States with a $5,000 cash bonus per year.

If you are looking to start your journey in the field of Data Engineering, the MLAcademy’s Live Courses is the way to go.

What is the career-wise scope of Data Engineering?

The best part of Data Engineering is its versatility. When you choose a Data Engineering career, you gain skills that overlap numerous professions.

Hence, when you enter the field of Data Engineering, you can work as a Hadoop Developer, ETL Developer, BI Developer, Technical Architect, Data Warehouse Engineer, etc.

Data Engineering is a vast field and encompasses several domains. Thus, this gives you a lot of flexibility to choose the sector and the applications as per your preference. 

What skills are required to be a data engineer?

A Data Engineer needs to be a good developer. They need command over Database Management concepts and Operating Systems and be handy with using different technologies. To sum it up for you, the following skills are a must-have:

  1. Excellent Programming.
  2. Hand-on with Database concepts
  3. Understanding of Operating systems
  4. Cloud Computing knowledge
  5. Scheduling Workflows
  6. Mastery in Data Processing Techniques

Some of the soft skills required for a Data Engineer are as follows:

  1. Analytical thinking
  2. Problem Solving ability
  3. Attention to detail
  4. Ability to make decisions and take responsibility
  5. Multitasking
  6. Adaptability

Woah! That’s a lot.

Are you confused about where to begin your journey? Here’s the answer for which you’ve been waiting for so long. Keep reading. 

How do you become a Data Engineer?

Fasten your belts and hold tight. This roadmap is going to be a long journey that’ll depict your Data Engineer career path.  

[Step 1] Improve your Computer Science Fundamentals.

As a beginner, you need to know basic concepts. Understand the working of your computer and the Internet.

Familiarize yourself with version control tools like Git. They are immensely beneficial when you work in groups and help in tracking your project progress. 

Start brushing up foundational concepts like basic terminal commands and their usage, Data Structures and Algorithms, the functioning of APIs, and the difference between structured and unstructured data. 

Move on and grasp the concepts of Linux Operating Systems and Serialization. A strong base of Maths and Statistics will help you along the way.

[Step 2] Master a programming language.

The actual Data Engineering Roadmap begins with enhancing your programming proficiency. 

To become a Data Engineer, build yourself up to become a Software Engineer first. The most dominant programming languages in this field are as follows:

  1. Python
  2. Scala
  3. Java
  4. Go

Generally, most companies prefer Python and Scala.

[Step 3] Testing

It is crucial to understand the concepts of Software testing that include Unit Testing, Integration Testing, and Functional Testing. They will help you in improving your software and figuring out the defects. 

[Step 4] Understand Databases

SQL powers anything and everything associated with Data. Mastering SQL is a must. Understanding the Database is one of the most significant steps in the Data Engineering Roadmap.

Make sure you learn SQL basics and understand database management fundamentals like Normalization, ER diagrams, and ACID properties. 

Other concepts you should spend time on are as follows, OLTP and OLAP, Horizontal and vertical scaling, CAP theorem, and Dimensional modeling.

[Step 5] Knowledge of Relational and Non-Relational Databases

You need to know how to write queries for any Relational Database Management system. You also need to be familiar with the concept of Nom-Relational Databases.

It would prove beneficial if you master one database from each category. 

[Step 6] Data Warehousing

One of the primary responsibilities of a Data Engineer is to carry out ETL operations. Thus, you need to know how to design, construct and manage a Data Warehouse.

For Data Warehousing, the best tools available are Snowflake, Amazon Redshift, and Google BigQuery. 

[Step 7] Understanding Cloud Computing and Cluster Computing Fundamentals

Voila! We are halfway through the Data Engineering Roadmap.

All the data goes into the cloud. Thus, as a Data Engineer, you need to be comfortable with at least one cloud provider. We recommend you use either Amazon Web services, Microsoft Azure, and Google Cloud Platform. 

Moreover, data is generally independent and sourced in clusters. Thus, Knowledge of these clusters and distributed systems is essential. 

You will find tools like big data tools like Apache Hadoop, HDFS, MapReduce, etc., as a requirement in most data engineering career descriptions.

[Step 8] Data Pre-processing

In-depth knowledge of Data Pre-processing will go a long way in your Data Engineer career path. You will have tons of data to process. 

Get acquainted with frameworks that can process batch, hybrid, and streaming data. For example: – Apache Pig, Apache Spark, Apache Kafka. 

[Step 9] Workflow Scheduling

Once you’ve built the workflow for your data, you will need to schedule it regularly. For this, you can use Apache Airflow, Google Composer, and Apache Oozie. 

[Step 10] Learn to Monitor Pipelines

As a data engineer, you’ll spend loads of time building and managing data pipelines. 

You will also need to monitor the working of these pipelines. Thus, Prometheus, Datadog, Sentry, and StatsD will come in handy for this purpose. 

[Step 11] Basics of Networking

Understand the working of VPN, Firewalls, IP address, DNS servers, and various protocols like TCP/UDP, http/https, etc. 

[Step 12] Learn to work with Infrastructure and Internalize it.

Learn how to manage and provision your data center using machine-readable configuration files.

Learn about tools like Docker, Kubernetes, and AWS CloudFormation to use in the case of Containers, Container Orchestration, and Infrastructure Provisioning, respectively. 

[Step 13] Cybersecurity and IAM basics

This is the final step, and here we come to the end of our Data Engineering Roadmap. 

Security, Privacy, and Authencity are crucial aspects when we deal with data. Thus, basic knowledge of legal compliance, encryption, key management, and data governance and integrity will help in securing data. 

Familiarize yourself with Azure Active Directory or Active Directory for Identity and Access Management. 

Are you ready to become a Data Engineer?

A lot goes into being a Data Engineer. However, practice and consistency is the way to succeed. Keep applying the concepts you learn. 

This Data Engineering Roadmap gives you a brief overview of the tools and skills you need to develop to become a Data Engineer.

Data Engineering is an ever-changing field. You need to be adaptable and follow the trends to stay at the top in the competition.