Best Data Engineering Courses Online (2026)

Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Every course on this list was loaded and re-checked in September 2026. See our review methodology.

Why Data Engineering Matters in 2026

Before you sign up for another data science course, read this.

I've taken DataCamp, Dataquest, Coursera ML, and the Udacity nanodegrees. Get my Tuesday picks — plus reader-only codes when they drop.

No spam. Unsubscribe anytime.

Pursuing certification? See our dedicated guides: AWS Certified Data Engineer (DEA-C01) and Azure Data Engineer (DP-700). Both walk you through exam domains, prep paths, salary impact, and the best courses to use.

Not sure which cloud to pick? Read our AWS vs Azure comparison first.

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.

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. The role sits close to production systems, which is why hiring tends to favor people who can show working pipelines over people who can only show coursework.

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

Course	Platform	Level	Duration	Price
IBM Data Engineering Professional Certificate	Coursera	Beginner	4-6 months	$49/mo
Data Engineering for Beginners: Learn SQL, Python & Spark	Udemy	Beginner-Intermediate	56 hours	$14-$85
Data Engineer in Python	DataCamp	Intermediate	40 hours	$14/mo (annual)
Preparing for Google Cloud Certification: Cloud Data Engr	Coursera	Intermediate	4 wks @ 10 h/wk	Coursera Plus / sub
DP-203 (retired — see DP-700)	—	—	—	—
Data Engineering Foundations Specialization	Coursera	Beginner	3 months	$49/mo
Mastering Databricks & Apache Spark — ETL Pipeline	Udemy	Intermediate	16 hours	$14-$85
Build and Run ETL Pipelines with Azure Databricks & Data Factory	Pluralsight	Intermediate	2h 16m	$21–$39/mo (yearly)
Machine Learning with Apache Spark	Coursera	Intermediate	2 weeks	$49/mo
Data Engineering on AWS — Serverless ETL & BI	Udemy	Intermediate	12 hours	$14-$85
Data Warehousing and BI Analytics	edX	Beginner	6 weeks	Free / $99 cert
Azure Databricks & Spark Core for Data Engineers	Udemy	Intermediate-Advanced	20 hours	$14-$85
Data Engineer Path	Dataquest	Beginner	6 months	$33/mo
Introduction to Big Data with Spark and Hadoop	Coursera	Beginner	4 weeks	$49/mo

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 for Beginners: Learn SQL, Python & Spark [Udemy]

The Data Engineering for Beginners: Learn SQL, Python & Spark course by Durga Viswanatha Raju Gadiraju is Udemy’s largest data engineering course by enrollment — 426,589 students, rated 4.3 from 8,896 ratings, last updated March 2026. 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.

Set your expectations on scale: this is the largest course on the list at 53 sections, 623 lectures and 55 hours 57 minutes of video. That is a months-long commitment rather than a weekend, and the trade-off is breadth over brevity. Udemy courses regularly go on sale for $14-$20, which makes this the cheapest route to that much material. You also get lifetime access and a 30-day money-back guarantee. Checked September 2026.

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 in Python [DataCamp]

DataCamp’s Data Engineer in Python track is a 14-course path of roughly 40 hours. DataCamp now lists its Associate Data Engineer track as a prerequisite, so this is no longer the true beginner entry point it once was — it assumes you already have the SQL foundation from that track. You start with cloud computing concepts, work through Python from the basics to advanced use, and finish on pipelines and workflow automation.

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 current syllabus centers on data manipulation and cleaning with pandas, importing and exporting data from CSV, Excel, SQL, JSON and APIs, designing ETL and ELT pipelines, automating workflows with Apache Airflow, and version control with Git. A DataCamp certification is available on completion. The track holds 4.3 stars from 51 reviews — a small sample, so weigh it lightly. DataCamp Premium costs $14/month billed annually — about $168 a year, a standing “special price” against a $28 list — or $35/month month-to-month, and it includes all projects. Priced September 2026.

Best for: Self-paced learners who already have working SQL and want an interactive, code-along route into pipelines and Airflow.

Key topics: Python, pandas, ETL and ELT pipeline design, Apache Airflow, Git, cloud computing concepts, software engineering practice

4. Preparing for Google Cloud Certification: Cloud Data Engr Professional Certificate [Coursera]

The Preparing for Google Cloud Certification: Cloud Data Engr Professional Certificate is built by Google Cloud Training and prepares you for the Google Cloud Professional Data Engineer certification exam — one of the most valued cloud credentials in the industry. (The clipped “Engr” is Google’s own abbreviation in the official title, not our typo.) It carries 108,994 enrollments and scores 4.6 from 4,951 reviews across the program.

This five-course series covers Google Cloud’s data ecosystem: BigQuery for interactive analysis and warehousing, Dataflow for stream and batch processing, Dataproc and Cloud SQL for migrating existing Hadoop, Pig, Spark, Hive and MySQL workloads, and Cloud Storage for data lakes. You work through hands-on labs in live Google Cloud environments rather than simulations.

Coursera rates it intermediate and schedules it at 4 weeks at 10 hours a week, on a flexible schedule that most people stretch out further. It assumes some familiarity with SQL and Python, so complete beginners should start with the IBM certificate first. Enrollment runs through a Coursera subscription and it is included with Coursera Plus — see our Coursera pricing guide for what that costs today.

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

Key topics: BigQuery, Dataflow, Dataproc, Cloud SQL, Cloud Storage, data lakes, data governance, real-time data, PySpark, Apache Hadoop

5. Azure data engineering — DP-203 is retired, aim at DP-700

We no longer recommend a DP-203 course. Microsoft retired the DP-203 exam and the Azure Data Engineer Associate certification in March 2025, and replaced it with DP-700: Fabric Data Engineer Associate. Preparing for DP-203 today buys you a credential you can no longer earn.

The Udemy course previously listed in this slot was built for the 2022 version of the DP-203 exam, so it is doubly out of date. We have removed it rather than leave a paid recommendation pointing at a dead exam.

If Azure is your platform, go to our Azure Data Engineer certification guide, which covers DP-700, the Fabric-based syllabus that replaced DP-203, and the current prep paths. The underlying skills — Data Lake, Synapse, Data Factory, Databricks — still transfer; it is the exam and the tooling emphasis that moved to Microsoft Fabric.

Best for: Anyone who came here looking for DP-203 prep and needs the current path instead.

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. Build and Run ETL Pipelines with Azure Databricks and Azure Data Factory [Pluralsight]

Build and Run ETL Pipelines with Azure Databricks and Azure Data Factory on Pluralsight is a focused, hands-on course by Mohit Batra, who holds a 4.7 author rating across 18 courses. It was last updated in June 2025. Note that it is specifically an Azure course: Databricks does the large-scale processing, Data Factory handles ingestion and orchestration.

Its six modules walk through the complete ETL process: setting up the environment and Databricks Unity Catalog, transforming data in Azure Databricks, loading it with Delta Lake, performance optimization, then automating pipelines with Databricks Workflows and orchestrating them from Azure Data Factory.

At 2 hours 16 minutes, this is a deep-dive workshop rather than a comprehensive course. It’s ideal as a complement to a longer foundational course. Pluralsight costs $21/month for Core Tech or $39/month for Complete, both billed yearly (about $252 and $468 a year) — the old Standard and Premium tiers were retired — and you can try it free for 10 days. Priced September 2026. 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: Azure Databricks, Azure Data Factory, ETL pipelines, Delta Lake, Unity Catalog, Apache Spark, performance optimization, Databricks Workflows

9. Machine Learning with Apache Spark [Coursera]

The Machine Learning with Apache Spark course on Coursera is IBM’s answer to a problem most data engineers eventually hit: a pipeline is only useful if a model can consume what comes out of it. It teaches you to run machine learning workloads on the same Spark cluster you already use for processing.

The syllabus moves through Spark SQL for data analysis and then into regression, classification, and clustering with SparkML. It runs to four modules, is taught by the IBM Skills Network team, and is rated 4.5 from 115 reviews with just over 21,000 enrolments.

It is short — roughly two weeks at 10 hours a week — and Coursera lists it at intermediate level, so you want working Python and SQL before you start. It counts toward several IBM programs on Coursera rather than one single certificate, which makes it a reasonable way to sample that curriculum before committing to a full professional certificate.

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, Spark SQL, regression, classification, clustering, 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.

Explore the Udacity Data Engineer Nanodegree →

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 for Beginners on Udemy or the DataCamp Data Engineer in Python 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, on Azure, the current DP-700 (Fabric Data Engineer) path — DP-203 was retired in March 2025. 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: Azure data engineering prep now targets DP-700 (Fabric), since DP-203 retired in March 2025 — that guide 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 offer a free preview of the first module.

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 or, on Azure, DP-700 (Fabric Data Engineer), which replaced the retired DP-203. 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

Correction — September 2026

An earlier version of this page said demand for data engineers had “outpaced data scientists for three consecutive years” and quoted an unsourced $120,000–$160,000 average. Neither claim was supported. We have replaced both with official BLS figures, including a growth projection that is markedly more modest than the way this role is usually marketed.

Data engineering pays well, but treat any single national average you see with caution: the US Bureau of Labor Statistics does not track “data engineer” as its own occupation, so those figures are job-board aggregates, not official statistics. The nearest official category is Database Administrators and Architects, which the BLS puts at a 2025 median of $126,760 a year ($60.94 an hour) across 144,500 jobs. Database administrators on their own sat lower, at a $104,620 median in May 2025 — the architect half of the category pulls the average up. For the roles on either side, the BLS 2025 medians are $134,040 for software developers and $120,230 for data scientists.

The part worth knowing before you commit: the BLS projects that database administrator and architect category to grow just 4% from 2025 to 2035 — about as fast as the average for all occupations — against 35% for data scientists over the same period. The pay is genuinely strong. The “fastest-growing role in tech” framing is not something the official projections support, and you should discount marketing that leans on it.

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.

Related: Best Apache Spark Courses

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 for Beginners course on Udemy delivers the most material 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.

Best Data Engineering Courses & Certifications Online (2026)