Best Data Engineering Courses & Certifications Online (2026)

Why Data Engineering Matters in 2026

Data engineering is the backbone of every modern data-driven organization. While data scientists build models and analysts create dashboards, data engineers build the infrastructure that makes all of it possible. They design, build, and maintain the pipelines that move data from raw sources into usable formats.

The demand for data engineers has outpaced data scientists for three consecutive years. Companies need professionals who can handle ETL processes, build data warehouses, manage streaming pipelines, and work with cloud platforms like AWS, Azure, and Google Cloud.

Whether you’re a software developer looking to specialize, a data analyst ready to level up, or a career changer entering the field, the courses below will get you there. I’ve researched and compared courses across every major platform — Udemy, Coursera, DataCamp, Pluralsight, and more — to rank the best options by content quality, practical value, and career outcomes.

Top Data Engineering Courses at a Glance

Best Data Engineering Courses (Ranked)

1. IBM Data Engineering Professional Certificate [Coursera]

The IBM Data Engineering Professional Certificate is the most comprehensive entry point into data engineering available online. This 13-course program covers the full data engineering stack: relational databases with SQL, Python scripting, Linux shell commands, ETL and data pipeline construction, NoSQL and big data technologies, Apache Spark, and data warehousing.

What makes this certificate stand out is its breadth. You don’t just learn one tool — you work through hands-on projects involving PostgreSQL, MongoDB, Cassandra, Cloudant, IBM Db2, Apache Kafka, and Apache Airflow. By the end, you’ll have built a complete data platform capstone project that demonstrates real-world skills to employers.

The program takes 4-6 months at about 10 hours per week. At $49/month through Coursera Plus, the total cost depends on your pace. IBM’s name carries weight on a resume, and the certificate is recognized by employers hiring for junior data engineering roles. Financial aid is available if cost is a concern.

Not sure if a Coursera certificate is worth it? Read our in-depth Coursera certificate review to find out.

Best for: Career changers and beginners who want a structured, end-to-end learning path. No prior experience required beyond basic computer literacy.

Key topics: SQL, Python, ETL, data warehousing, Apache Spark, NoSQL, Apache Airflow, Kafka, IBM Cloud

2. Data Engineering Essentials — SQL, Python, and Spark [Udemy]

The Data Engineering Essentials course by Durga Viswanatha Raju Gadiraju is Udemy’s top-rated data engineering course for good reason. It covers the three pillars every data engineer needs: SQL for querying, Python for scripting, and Apache Spark for large-scale data processing.

The course is highly practical. You’ll build data pipelines from scratch, work with PostgreSQL and MySQL relational databases, and learn how to process data using PySpark. The instructor walks through real ETL scenarios rather than abstract theory, which means you can apply what you learn directly at work.

At 18 hours of content, this is a focused course that doesn’t waste time on filler. Udemy courses regularly go on sale for $14-$20, making this one of the most affordable options on the list. You also get lifetime access and a 30-day money-back guarantee.

Best for: Developers and analysts who already know some Python or SQL and want to build practical data engineering skills quickly.

Key topics: SQL, Python, Apache Spark, PySpark, PostgreSQL, data pipelines, ETL

3. Data Engineer with Python [DataCamp]

DataCamp’s Data Engineer with Python career track is a 25-course learning path that takes roughly 95 hours to complete. It’s structured as a skill tree: you start with Python fundamentals and SQL, then progress through data pipeline construction, big data tools like PySpark, and cloud-based data management.

DataCamp’s interactive browser-based coding environment is its biggest advantage. Every lesson includes hands-on coding exercises where you write and run real code — no setup required on your machine. This makes it particularly good for learners who struggle with environment configuration.

The track covers database design, ETL pipelines, PySpark for big data, AWS Boto for cloud automation, and MongoDB for NoSQL. Each module includes assessments and real-world projects. DataCamp costs $25/month (billed annually) or $33/month on the premium plan, which includes all projects.

Best for: Self-paced learners who want an interactive, code-along experience with clear progression from beginner to intermediate.

Key topics: Python, SQL, PySpark, AWS Boto, MongoDB, data pipeline design, Shell scripting

4. Google Cloud Data Engineer Professional Certificate [Coursera]

The Google Cloud Data Engineer Professional Certificate is built by Google Cloud itself and directly prepares you for the Google Cloud Professional Data Engineer certification exam — one of the most valued cloud credentials in the industry.

This 6-course program covers Google Cloud’s data ecosystem in depth: BigQuery for data warehousing, Dataflow for stream and batch processing, Pub/Sub for messaging, Dataproc for managed Hadoop and Spark, and Cloud Storage for data lakes. You’ll work through hands-on labs in Qwiklabs using real GCP environments.

The program takes about 4 months at 4 hours per week. It assumes some familiarity with SQL and Python, so complete beginners should start with the IBM certificate first. At $49/month through Coursera, the total cost is roughly $200 — significantly cheaper than Google’s own training courses.

Best for: Engineers who want to specialize in Google Cloud data services or prepare for the GCP Data Engineer certification.

Key topics: BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, Apache Beam, data lakes, streaming analytics

5. DP-203: Data Engineering on Microsoft Azure [Udemy]

The DP-203 exam preparation course on Udemy is the go-to resource for anyone pursuing the Microsoft Azure Data Engineer Associate certification. It covers every exam objective: designing and implementing data storage, data processing, and data security on Azure.

You’ll learn to work with Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory, and Azure Databricks. The course includes practice tests that mirror the actual DP-203 exam format, so you’ll know exactly what to expect on test day.

At 24 hours of video content plus practice exams, this is a dense course aimed at professionals with some Azure experience. If you’re starting from scratch with Azure, pair this with the Azure fundamentals course first. The DP-203 certification is particularly valuable for organizations running Microsoft technology stacks.

Best for: IT professionals and developers already working with Microsoft technologies who want to certify their Azure data engineering skills.

Key topics: Azure Data Lake, Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure Stream Analytics, DP-203 exam prep

6. Data Engineering Foundations Specialization [Coursera]

The Data Engineering Foundations Specialization on Coursera by IBM is a shorter alternative to the full IBM Professional Certificate. This 5-course program focuses on the core concepts without the deeper hands-on capstone work.

It covers data engineering lifecycle concepts, relational database fundamentals, SQL querying, ETL and data pipeline development, and an introduction to NoSQL databases. The instruction is clear and assumes no prior data engineering experience.

At roughly 3 months to complete, this is a good option for people who want a solid foundation without committing to the full 6-month professional certificate. It works well as a stepping stone — you can always continue to the full certificate later. The specialization includes a shareable certificate on completion.

Best for: Complete beginners who want to understand data engineering fundamentals before diving into specialized tools or cloud platforms.

Key topics: Data engineering concepts, SQL, relational databases, ETL, NoSQL basics, data pipeline design

7. Mastering Databricks & Apache Spark — Build ETL Data Pipeline [Udemy]

The Mastering Databricks & Apache Spark course focuses on what many employers actually need: building production-grade ETL pipelines using Databricks and Spark. Databricks has become the platform of choice for enterprise data engineering, and this course teaches it from the ground up.

You’ll learn Spark fundamentals, Delta Lake for reliable data lakes, Databricks notebooks, structured streaming, and how to build complete data pipelines that ingest, transform, and serve data. The hands-on projects are production-realistic, not toy examples.

At 16 hours, the course is concise and focused. It assumes you know Python basics and have some SQL experience. This is an excellent course for data engineers who are already working but need to learn the Databricks ecosystem — a skill that’s increasingly in demand as companies migrate from legacy Hadoop systems.

Best for: Working professionals who need to learn Databricks and modern Spark-based data engineering for their current or next role.

Key topics: Databricks, Apache Spark, Delta Lake, ETL pipelines, structured streaming, PySpark

8. Building ETL Pipelines with Databricks [Pluralsight]

The ETL Pipelines with Databricks course on Pluralsight is a focused, hands-on course that teaches you to build robust ETL pipelines using the Databricks platform. Pluralsight’s data engineering content is consistently high-quality, and this course is no exception.

The course walks through the complete ETL process: extracting data from various sources, transforming it using Spark and Databricks, and loading it into a data warehouse or data lake. You’ll work with Delta Lake, handle schema evolution, and implement data quality checks.

At about 3 hours, this is more of a deep-dive workshop than a comprehensive course. It’s ideal as a complement to a longer foundational course. Pluralsight costs $29/month for the standard plan, and you can try it free for 10 days. The platform includes skill assessments so you can measure your progress.

Best for: Developers who already understand data engineering basics and want focused, practical Databricks ETL training.

Key topics: Databricks, ETL pipelines, Delta Lake, Spark, data quality, schema evolution

9. Data Engineering and Machine Learning Using Spark [Coursera]

The Data Engineering and ML Using Spark course on Coursera bridges the gap between data engineering and machine learning — a combination that’s increasingly valuable. You’ll learn how to build Spark-based data pipelines that feed directly into ML workflows.

The course covers Spark architecture, RDDs, DataFrames, Spark SQL, and SparkML. What makes it unique is the dual focus: you build data engineering infrastructure and then use that same infrastructure for machine learning. This gives you perspective on how data engineers and data scientists collaborate in practice.

At about 5 weeks, the course is a manageable time commitment. It’s part of IBM’s broader data engineering curriculum on Coursera and can count toward the full professional certificate. You’ll need basic Python and SQL knowledge before starting.

Best for: Data engineers who want to understand the ML pipeline, or data scientists who want to build better data infrastructure for their models.

For the machine learning and AI side of data work, check out our guide to the best deep learning courses online.

Key topics: Apache Spark, SparkML, RDDs, DataFrames, Spark SQL, data pipelines for ML

10. Data Engineering on AWS — Serverless ETL & BI [Udemy]

The Data Engineering on AWS course teaches you to build serverless data pipelines on Amazon Web Services. If your organization runs on AWS (and about 32% of cloud infrastructure does), this course will show you how to build data engineering solutions using native AWS services.

You’ll work with AWS Glue for serverless ETL, Amazon Redshift for data warehousing, Amazon Athena for querying data lakes, AWS Lambda for event-driven processing, and Amazon QuickSight for business intelligence. The course builds a complete project from ingestion to dashboarding.

At 12 hours, the course is tightly focused on AWS-specific tools. Some AWS experience is helpful but not strictly required. This is a practical choice for engineers preparing for the AWS Data Engineer Associate certification or building AWS data infrastructure at work.

Best for: Engineers working with or planning to work with AWS who need to build cloud-native data pipelines.

Key topics: AWS Glue, Amazon Redshift, Athena, Lambda, QuickSight, serverless ETL, S3 data lakes

11. Data Warehousing and BI Analytics [edX]

The Data Warehousing and BI Analytics course on edX by IBM focuses specifically on the data warehousing side of data engineering. Data warehousing is a core data engineering competency, and this course teaches it properly: dimensional modeling, star and snowflake schemas, OLAP cubes, and materialized views.

You’ll work with real databases and build a data warehouse from scratch. The course also covers BI tools like IBM Cognos Analytics, giving you the full picture from data warehouse design to end-user reporting. This context helps you understand why data engineers build what they build.

The course runs for 6 weeks and is available for free in audit mode. A verified certificate costs $99. This is one of the best free resources for learning data warehousing concepts that apply regardless of which cloud platform you use.

Best for: Anyone who wants to understand data warehousing fundamentals without committing to a full certificate program or monthly subscription.

Key topics: Data warehouse design, dimensional modeling, star schema, OLAP, ETL, IBM Cognos Analytics

12. Azure Databricks & Spark Core for Data Engineers [Udemy]

The Azure Databricks & Spark Core course is a comprehensive deep-dive into using Databricks on the Azure cloud platform. It’s designed specifically for data engineers rather than data scientists, focusing on infrastructure, pipeline construction, and data processing at scale.

The course covers Spark architecture, Databricks workspace management, cluster configuration, Delta Lake, Azure Data Factory integration, and building complete data engineering solutions. You’ll learn both the Spark programming model and the Azure-specific tooling around it.

At 20 hours, this is a substantial course that goes deep enough for production work. It’s particularly useful if your organization uses Azure and Databricks together — a combination that’s extremely common in enterprise environments. The instructor provides real-world scenarios based on actual enterprise data engineering challenges.

Best for: Data engineers working in Azure environments who need deep Databricks and Spark expertise for production pipelines.

Key topics: Azure Databricks, Apache Spark, Delta Lake, Azure Data Factory, cluster management, data lake architecture

13. Dataquest Data Engineer Path

The Dataquest Data Engineer path is a structured, project-based curriculum that takes you from Python basics to building production data pipelines. Dataquest’s approach is entirely code-first — there are no video lectures. Instead, you read explanations and immediately write code in the browser.

The path covers Python programming, SQL and PostgreSQL, data pipeline construction, algorithms and data structures, and handling large datasets. Each module ends with a guided project that builds on what you’ve learned. You’ll build a complete portfolio of data engineering projects by the end.

The learning path takes about 6 months and costs $33/month on the premium plan. Dataquest works well for people who learn better by doing than by watching. The lack of video content is either a feature or a drawback depending on your learning style — but the code-first approach means you’ll write significantly more code than in video-based courses.

Best for: Self-directed learners who prefer reading and coding over watching videos, and who want a structured path with portfolio projects.

Key topics: Python, SQL, PostgreSQL, data pipelines, algorithms, data structures, project-based learning

14. Introduction to Big Data with Spark and Hadoop [Coursera]

The Introduction to Big Data with Spark and Hadoop course on Coursera is a solid starting point for understanding the big data ecosystem. It covers both Apache Hadoop (the original big data framework) and Apache Spark (the modern successor), giving you context on how the field has evolved.

You’ll learn about HDFS, MapReduce, Spark DataFrames, Spark SQL, and how these technologies fit into the modern data engineering stack. The course includes hands-on labs using IBM Cloud, so you can practice without setting up infrastructure on your own machine.

At 4 weeks (roughly 8-12 hours), this is a short course aimed at building foundational knowledge. It’s a good complement to more hands-on courses like the Udemy Databricks courses listed above. Part of IBM’s data engineering curriculum on Coursera.

Best for: Beginners who want to understand the big data landscape before specializing in a specific tool or cloud platform.

Key topics: Apache Hadoop, HDFS, MapReduce, Apache Spark, Spark SQL, DataFrames, big data fundamentals

15. Data Engineering Using Databricks on AWS and Azure [Udemy]

The Data Engineering Using Databricks on AWS and Azure course covers Databricks deployment and usage across both major cloud platforms. This multi-cloud perspective is valuable because many enterprises operate in hybrid or multi-cloud environments.

You’ll learn to set up Databricks workspaces on both AWS and Azure, build data pipelines, work with Delta Lake, and understand the differences in cloud-specific integrations. The course covers both the commonalities (Spark, Delta Lake) and the platform-specific pieces (S3 vs ADLS, IAM vs Azure AD).

This is a practical course for engineers who need flexibility across cloud platforms. At current Udemy pricing, it’s an affordable way to gain multi-cloud Databricks experience. The dual-cloud approach also helps you understand which platform-specific features matter and which are just surface-level differences.

Best for: Data engineers who work in multi-cloud environments or want to keep their options open across AWS and Azure.

Key topics: Databricks, AWS, Azure, multi-cloud data engineering, Delta Lake, PySpark, cloud integration

Best Data Engineering Bootcamps

Springboard Data Engineering Bootcamp

Springboard’s Data Engineering Career Track is a mentor-led bootcamp that takes about 6 months part-time. You get a dedicated industry mentor who meets with you weekly, reviews your work, and helps guide your job search. The curriculum covers Python, SQL, data modeling, ETL pipeline construction, and cloud data infrastructure on AWS.

The bootcamp costs around $9,900 (or monthly payments), and Springboard offers a job guarantee — if you don’t land a data engineering job within 6 months of graduating, you get a full refund. The program includes career coaching, resume review, and mock interviews. It’s a significant investment, but the mentorship and job guarantee reduce the risk.

Udacity Data Engineer Nanodegree

Udacity’s Data Engineer Nanodegree is a 5-month program with a focus on hands-on projects. You’ll build a data warehouse on Amazon Redshift, create data pipelines with Apache Airflow, and work with data lakes using Spark. Each project is reviewed by Udacity’s technical mentors.

Pricing is around $249/month. The program includes technical mentor support and personal career coaching. Udacity’s projects are among the most realistic of any online learning platform, which makes the Nanodegree particularly valuable for building a portfolio.

How to Choose a Data Engineering Course

By Experience Level

Beginner (no technical background): Start with the IBM Data Engineering Professional Certificate on Coursera. It assumes no prior experience and builds up methodically from SQL and Python basics.

Intermediate (know Python/SQL, some data work): Jump into hands-on courses like Data Engineering Essentials on Udemy or the DataCamp Data Engineer track. These skip the basics and focus on building pipelines.

Advanced (working developer/engineer): Go straight to cloud-specific certifications like the GCP Data Engineer or Azure DP-203 courses. These assume technical proficiency and focus on platform-specific skills.

By Cloud Platform

AWS: The Data Engineering on AWS Udemy course covers Glue, Redshift, Athena, and Lambda for serverless data engineering.

Azure: The DP-203 exam prep or Azure Databricks & Spark Core courses are the best options for Microsoft-centric organizations.

GCP: The Google Cloud Data Engineer Certificate on Coursera is built by Google and prepares you directly for their certification exam.

By Budget

Free: The edX Data Warehousing course is available for free in audit mode, and Coursera courses can be audited for free (without the certificate).

Under $100: Udemy courses regularly go on sale for $14-$20 each. You could complete three or four Udemy data engineering courses for less than one month of Coursera.

Subscription ($25-$50/mo): Coursera Plus ($49/mo) or DataCamp ($25-$33/mo) give you access to entire libraries of content, which is cost-effective if you plan to take multiple courses.

By Certification Goal

If you want a recognized certification to put on your resume, prioritize the IBM certificate, the Google Cloud certificate, or the Azure DP-203 certification. These are vendor-backed credentials that hiring managers recognize.

What Does a Data Engineer Do?

A data engineer designs, builds, and maintains the data infrastructure that organizations use for analytics, reporting, and machine learning. Think of it this way: if a data scientist is a chef, the data engineer builds the kitchen.

Day-to-day, data engineers build ETL/ELT pipelines that extract data from sources (APIs, databases, files), transform it into usable formats, and load it into data warehouses or data lakes. They work with tools like Apache Spark, Apache Airflow, Apache Kafka, dbt, and cloud-native services on AWS, Azure, or GCP.

Data engineers also handle data quality, monitoring, schema management, and access control. They collaborate closely with data scientists, analysts, and product teams to ensure everyone has access to reliable, well-structured data. The role requires strong programming skills (Python, SQL), understanding of distributed systems, and familiarity with cloud infrastructure.

The role sits between software engineering and data science, borrowing skills from both. Senior data engineers often specialize in areas like streaming data, data platform architecture, or ML infrastructure (MLOps).

Data Engineer Salary & Career Outlook

Data engineering is one of the highest-paying technical roles you can enter without a traditional computer science degree. In the United States, data engineers earn between $120,000 and $160,000 on average, with senior and staff-level engineers at top companies earning $180,000-$250,000+.

Demand continues to grow. LinkedIn’s 2025 Jobs on the Rise report listed data engineering in the top 10 fastest-growing roles globally. The supply of qualified data engineers hasn’t kept up with demand, which keeps salaries high and job searching relatively straightforward for those with the right skills.

The most in-demand skills for data engineers in 2026 are: Python, SQL, Apache Spark, cloud platforms (AWS/Azure/GCP), Apache Airflow, Kubernetes, and dbt. If you build proficiency in these through the courses listed above, you’ll be well-positioned for the job market.

Frequently Asked Questions

What does a data engineer do?

A data engineer builds and maintains the data infrastructure that powers analytics and machine learning. They create ETL pipelines, manage data warehouses and data lakes, ensure data quality, and work with tools like Apache Spark, Airflow, and cloud services. For a deeper look at how data engineering fits alongside related roles, see our data scientist vs data engineer comparison.

Is data engineering hard to learn?

Data engineering has a moderate learning curve. If you already know Python and SQL, you can pick up core data engineering concepts in 2-3 months. The harder part is learning distributed systems, cloud platforms, and production-grade pipeline design — that takes closer to 6-12 months of focused study and practice. It’s not harder than software engineering, but it’s a different skill set.

How long does it take to become a data engineer?

Plan for 3-6 months to learn the fundamentals (SQL, Python, basic ETL, one cloud platform). Getting job-ready typically takes 9-12 months of consistent study, including building portfolio projects. Career changers from software engineering can transition faster — often in 3-4 months — since they already have the programming foundation.

Do I need a degree for data engineering?

No. While some job listings mention a degree preference, most employers prioritize practical skills and portfolio projects. Professional certificates from IBM, Google Cloud, or Microsoft carry real weight. The data engineering field is more skills-focused than many technical roles, and bootcamp graduates regularly land jobs at top companies.

What’s the difference between data engineering and data science?

Data engineers build the infrastructure; data scientists use it. Data engineers focus on pipelines, databases, data quality, and scalability. Data scientists focus on statistical modeling, machine learning, and generating insights. In practice, the roles overlap — but data engineering is more software engineering-oriented while data science is more math-oriented. We cover this in detail in our data roles comparison guide.

What programming languages do data engineers use?

Python and SQL are the two essential languages. Python handles scripting, automation, and working with tools like Apache Spark (via PySpark). SQL is used for querying databases, building transformations, and working with data warehouses. Some data engineers also use Scala (for Spark), Java, or Go, but Python and SQL cover 90% of what you need.

Conclusion

For beginners, the IBM Data Engineering Professional Certificate on Coursera gives you the most complete foundation. It covers everything from SQL basics to Apache Spark, and the IBM credential is widely recognized by employers.

For intermediate learners who already know Python and SQL, the Data Engineering Essentials course on Udemy delivers the most practical value for the lowest price. Pair it with a cloud-specific course (GCP, Azure, or AWS) to round out your skill set.

Whichever course you choose, the most important thing is to build real projects. Complete the hands-on labs, build your own pipelines using public datasets, and push your code to GitHub. Employers care far more about what you can demonstrate than which course name is on your resume.

Related Data Science Content

• Best Data Science Programs
• Machine Learning Courses
• Python Courses
• SQL Courses
• Tableau Courses
• Power BI Courses