8 Online Courses to Master Data Engineering for High-Demand Data Jobs

Hey there! With data becoming the most valuable asset for businesses today, data engineering skills are highly sought after. As someone passionate about technology, you may be wondering – what exactly do data engineers do? And how can you break into this fast-growing field?

In this comprehensive guide, I‘ll explain everything you need to know about data engineering – the responsibilities, skills required, learning resources and career growth opportunities. I‘ll also share my top recommendations on online courses, so you can gain data superpowers too!

What is Data Engineering and Why is it Important?

Data engineering is like the plumbing of the data world – not the most glamorous work, but absolutely critical to make everything flow.

Data engineers are the ones who build the robust pipelines to collect, store, process and serve data at scale. Without quality data infrastructure, companies can‘t extract any value from data to drive decisions.

Some key responsibilities of data engineers include:

Building efficient data warehouses and data lakes for analytical workloads
Developing data pipelines using orchestration tools like Apache Airflow to move data
Transforming raw data into analysis-ready formats
Setting up streaming data systems to process real-time data
Automating data infrastructure using infrastructure as code techniques
Designing and optimizing data models for SQL and NoSQL databases
Collaborating with data scientists and analysts on data projects

In short, data engineers focus on making data usable. The systems they architect power everything from business intelligence dashboards to machine learning models to customer-facing analytics.

The Explosive Growth of Data Engineering

With data growing exponentially across industries, companies urgently need skilled data engineering talent. Positions for data engineers are expected to grow by over 16% between 2019 and 2029 according to the U.S. Bureau of Labor Statistics.

LinkedIn‘s 2020 Emerging Jobs Report found that data engineering was one of the top 5 emerging professions in the US based on huge demand and salary growth.

The average data engineer salary in the US is $117,345 – much higher than most IT roles. Demand is high even beyond tech hubs, with many remote opportunities.

For technologists looking for challenging and future-proof work, data engineering is a promising path to pursue. The Kaggle 2020 Machine Learning and Data Science Survey found it to be one of the top 3 most in-demand skillsets.

Learning data engineering opens up exciting career opportunities and equips you with valuable technical skills like cloud computing, SQL/NoSQL databases, ETL processes, data modeling, containerization and more. These skills can help you advance in your current role or switch into high-paying data roles.

Since data engineering is an emerging field, it‘s helpful to understand how it differs from related roles:

Data analysts focus on deriving insights from data using reports, visualizations and statistical analysis. Data engineering builds the foundation for analysts.
Data scientists apply advanced statistical and machine learning techniques to data to build models. This isn‘t a core focus of data engineering.
Database administrators manage database systems and infrastructure. Data engineering covers a much wider stack including pipelines, storage, cloud, etc.
Data warehouse developers design and develop data warehouse schemas and ETL processes. Data engineering expands beyond just warehousing.
Big data engineers specifically focus on building big data infrastructure leveraging tools like Hadoop and Spark. Data engineering applies across both big data and smaller data use cases.
DevOps engineers handle code releases, infrastructure, CI/CD automation for software applications which data engineers integrate with.

The boundaries between roles are fluid, and some overlap is common. But the key focus of data engineers is building robust data infrastructure.

Skills You Need at Different Career Stages

The prerequisites and skills needed evolve as you progress from entry level to senior data engineering roles.

Entry Level Data Engineers

Fundamentals of Python and SQL – the core languages
Basic statistical and mathematical knowledge
Understanding of relational databases like Postgres, MySQL
Familiarity with cloud platforms like AWS, GCP or Azure
ETL process experience with tools like Airflow, dbt, Kafka
Ability to work collaboratively in an agile environment

Mid-Level Data Engineers

Production experience with distributed data systems and pipelines
Extensive SQL tuning, optimization and modeling skills
Knowledge of data warehousing patterns and architectures
Containerization skills with Docker and Kubernetes
Experience with big data systems like Spark, Hadoop, Hive, etc
Release and test automation using CI/CD platforms like Jenkins

Senior Data Engineers

Deep expertise in distributed cloud architecture and infrastructure as code
Ability to design complex enterprise data platforms
Master complex data problems through creative solutions
Mentoring and coaching skills for junior engineers
Deep knowledge of data systems scalability, security and governance
Ability to drive technical direction and architectural decisions

Of course, these vary across companies and specific needs of teams. But focusing on building these skills can help accelerate your data engineering career.

Prerequisites for Getting Started

While a computer science degree is not required, having some technical background will help kickstart your journey. Here are some prerequisites:

Core Programming

Python and Scala are popular languages for data engineering, but Java and C# are also useful
SQL – strong grasp of both relational databases like PostgreSQL, MySQL and NoSQL like MongoDB
Command line interfaces of Linux/Unix systems

Data and Infrastructure:

Data modeling – designing schemas, entities, relationships
Data warehousing – star schema, snowflake schema, incremental ETL
Infrastructure as code tools like Terraform, CloudFormation, Ansible

Foundational Theory:

Statistics – distributions, statistical testing, regression modeling
Algorithms and data structures – arrays, trees, maps, hash tables
Distributed systems – consensus protocols, CAP theorem

Cloud Platform Experience:

AWS – S3, Redshift, EMR, Glue, Kinesis, Athena
GCP – BigQuery, Cloud SQL, Dataflow, Dataproc, PubSub
Azure – Synapse Analytics, HDInsight, Data Factory

You don‘t need to master all these before getting started. The key is cultivating a lifelong learning mindset as technology progresses rapidly.

Online Courses for Learning Data Engineering

Let‘s look at the best online course options to learn data engineering concepts – from basics to advanced topics.

Structure of Data Engineering Courses

Data engineering courses usually follow a modular structure covering:

Core concepts – data warehouse, data lake, ETL/ELT, data modeling
Databases – relational databases like PostgreSQL, cloud databases like BigQuery
Ingestion – batch and streaming data collection using REST, APIs, web scraping
Orchestration – workflow tools like Apache Airflow, Kafka
Processing – distributed processing with Spark, data warehousing, transformation
Storage – S3, Redshift, Snowflake, Hive, HBase, cloud object storage
Visualization – BI tools like Tableau, Looker, Power BI to see pipeline results
Infrastructure – infrastructure as code with Terraform, Docker, Kubernetes
Monitoring – metrics, logging, dashboards
Security – encryption, access control, SSO, VPNs

This provides well-rounded training covering the full data engineering workflow.

Beginner Data Engineering Courses

Here are some top courses to get started:

Data Engineering for Everyone by Datacamp – Free 4-hour course explaining basics without coding
Data Engineering Basics for Everyone by IBM on edX – 2-hour overview of key concepts
Modern Big Data Analysis with SQL by Cloudera on Coursera – Hands-on introduction to big data SQL

These provide a high-level understanding of data engineering to set the context before diving deeper.

Comprehensive Data Engineering Courses

For more advanced training, comprehensive courses teach end-to-end skills with hands-on practice:

Data Engineering Nanodegree by Udacity – 5-month program with real-world projects
Data Engineering Career Track by Datacamp – 300+ hours of interactive coding challenges
Data Engineer Masters Program by Simplilearn – Blended course with lab access and projects
Data Engineering Essentials on Udemy – Hands-on focus with interactive code exercises

These courses teach more advanced skills like distributed cloud data systems, data modeling, optimization, security and more through practical experience.

Specialized Data Engineering Courses

Specialized courses help you dive deeper into specific technologies:

Apache Spark: Databricks Data Engineering with Databricks
Google Cloud: Coursera Building Batch Data Pipelines on GCP, Building Resilient Streaming Systems on GCP
AWS: ACloudGuru Database Design and ETL on AWS
Apache Kafka: Udemy Kafka Connect, Streams and KSQL for Data Engineers
dbt: Codecademy Learn dbt

These advanced courses help build specialized expertise after getting well-rounded foundations.

Learning Formats

The main online course formats are:

Self-paced – Flexible on-demand video content you can learn anytime
Cohort-based – Progress on a schedule with a group of learners
Bootcamps – Intensive multi-week immersive programs
University courses – Semester-long academic programs

Self-paced courses allow setting your own schedule. Cohorts provide structure and peer learning. Bootcamps accelerate learning in a short period. Academic courses offer in-depth foundational theory.

You can mix and match formats based on your learning preferences.

Tips for Learning Data Engineering

Here are some tips to guide your learning:

Start with core concepts before tools
Focus on hands-on coding, not just theory
Build portfolio projects to demonstrate skills
Use GitHub to showcase code and collaborate
Join data communities to stay updated
Keep learning as technologies evolve rapidly
Take notes and document your learning journey

Learning data engineering requires patience and perseverance. But it opens up lots of exciting career opportunities!

Data Engineering Career Growth Paths

Once you gain some experience, data engineering offers many potential career progression opportunities:

Data Engineer – Focus on building data pipelines, warehouses, lakes and databases
Analytics Engineer – Implement analytics and BI use cases working with business teams
Data Platform Engineer – Architect foundational data infrastructure and tools
Data Solutions Architect – Design enterprise-wide data platforms and governance
Principal Data Engineer – Lead complex deliverables and mentor junior engineers
Data Engineering Manager – Manage data teams, processes and technology strategy

Many senior data engineers also start their own data consulting firms. The analytics needs across industries provide great entrepreneurship potential too.

Learn In-Demand Data Skills Now

I hope this guide gave you a comprehensive overview of the rewarding world of data engineering!

With the exponential growth in data, having specialists who can build the data highways and plumbing needed to harness insights is incredibly valuable for any organization today.

Whether you want to advance in your current role or switch into this high-paying field, data engineering skills give you that coveted technical superpower.

The online courses above are a great starting point to future-proof your skills. Let the data be with you!