15+ Best Apache Spark Courses & Certifications Online in 2026

Last updated: June 2026. Written by Josh Hutcheson, OnlineCourseing editor. We compare courses on merit, not on who pays the highest commission. See our review methodology.

QUICK VERDICT

Apache Spark is the engine behind a huge share of big-data and data-engineering work, and a good course should get you writing real Spark jobs — DataFrames, Spark SQL, and streaming — not just explaining what a cluster is. We took the most popular Spark courses, verified each was still live and current (a lot of older “Spark” courses are stuck on Spark 2.x), dropped the stale ones, and sorted what’s left by who each one suits. We’ve also been clear about the one credential that actually matters here.

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The best Apache Spark courses at a glance

Ratings and update dates verified live on the providers’ sites in June 2026. Udemy prices reflect the platform’s frequent sales; DataCamp is a subscription.

1. Taming Big Data with Apache Spark and Python (Udemy) — best overall

Frank Kane’s course (Sundog Education) is the one we’d point most people to. It rates 4.4 across 18,061 ratings with over 114,000 students, and the current version was updated in April 2026 to Spark 4 — which matters a lot here, because Spark moves fast and most rivals are still teaching 2.x. Kane spent years at Amazon and IMDb working on large-scale data, and the course is relentlessly practical: you set up Spark, write real jobs with DataFrames and Spark SQL in Python, and run them, rather than wading through theory.

Because it uses Python (PySpark), it’s the most accessible route for the data scientists and analysts who make up most Spark learners. At roughly $15–20 on sale, it’s the best value in Spark learning. It assumes you can already program a little, so it’s not a first-ever coding course.

Best for: Python users who want practical, current Spark skills. Skip if: your team works in Scala.

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2. Apache Spark Essentials in Scala (Udemy, Rock the JVM) — best for Scala

Spark is written in Scala, and many data-engineering teams use it that way. If that’s you, Daniel Ciocîrlan’s Rock the JVM course is the highest-quality option we found — it rates a remarkable 4.8 across 2,299 ratings and was updated in July 2024. Ciocîrlan has a reputation for rigorous, well-structured Scala teaching, and this course goes deeper into how Spark actually works than the Python courses do, which pays off when you’re optimising real jobs.

Best for: developers using or learning Scala for data engineering. Skip if: you only know Python — start with Frank Kane’s course.

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3. Big Data with PySpark (DataCamp) — best interactive option

If you learn best by doing rather than watching, DataCamp’s Big Data with PySpark track is the strongest interactive route. You write and run Spark code directly in the browser, with instant feedback, across a sequence of short courses covering DataFrames, Spark SQL, and machine learning with Spark’s MLlib. It’s a subscription rather than a one-off purchase, which suits people who want a guided, hands-on path and will also use DataCamp’s other data courses.

Best for: data analysts and scientists who prefer learning by coding in-browser. Skip if: you want to own a single course outright.

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The Apache Spark certification that actually counts

If you want a recognised Spark credential, there is one clear answer: the Databricks Certified Associate Developer for Apache Spark. Databricks is the company founded by Spark’s original creators, and their certification is the de-facto industry standard — far more recognised than the various course “certificates of completion” or the older Cloudera CCA-175 (which has been retired). The exam tests your ability to use the Spark DataFrame API in Python or Scala to manipulate and analyse data.

None of the picks above are official prep for that exam, but the Frank Kane and Rock the JVM courses both teach the DataFrame skills it tests, and Databricks publishes its own free preparation material. The practical path is: learn Spark with one of the courses here, then prepare specifically for the Databricks exam if you want the credential. (We don’t earn anything from the Databricks certification — we name it because it’s the honest answer.)

PySpark or Scala: which should you learn?

Spark supports both Python (PySpark) and Scala, and the right choice depends on where you’re headed. PySpark is the more popular entry point and the natural fit if you come from data science or analytics, where Python already dominates — it’s also easier to learn. Scala is Spark’s native language; it can be faster for some workloads and is common on dedicated data-engineering teams, but it’s a steeper climb. For most people starting out, learn PySpark (Frank Kane’s course); reach for Scala (Rock the JVM) if your target job or team uses it. The Spark concepts — DataFrames, transformations, the execution model — are the same in both, so switching later is straightforward.

What you need before you start

• Python or Scala basics — you should be able to program in one of them before starting; the courses teach Spark, not the language.
• SQL — a working knowledge of SQL helps enormously, since Spark SQL and DataFrames borrow its logic.
• A reason to use big data — Spark shines on large datasets across a cluster. You can learn it locally, but the value lands when data outgrows a single machine.

Spark and data-engineering careers

Spark is a core skill for data engineers and a strong asset for data scientists and ML engineers working at scale. It shows up constantly in data-engineering job descriptions, usually alongside SQL, a cloud platform (AWS, Azure, or GCP), and increasingly Databricks. As with most technical skills, what gets you hired is a project you can show — a real pipeline or analysis you’ve built with Spark — more than any certificate. Finish a course, then build something on a real dataset and write up what you did.

What a good Spark course should cover

Spark is more than one tool, so check a course actually covers the parts you’ll use on the job:

• DataFrames and the Spark SQL API — the modern, day-to-day way to work with Spark. This is the core; an up-to-date course leads with it, not with the older RDD API.
• Spark SQL — querying big data with familiar SQL syntax.
• Structured Streaming — processing real-time data, increasingly common in production pipelines.
• MLlib — Spark’s machine-learning library, for running models at scale.
• How Spark runs — partitions, the execution model, and basic tuning, so you can fix slow jobs rather than just write them.

A course that’s still centred on the old RDD API or Spark 2.x is a warning sign — it’s one reason we dropped several older, highly-enrolled courses from this list in favour of the current ones above.

Free ways to learn Apache Spark

• Databricks Community Edition — a free, hosted Spark environment from the company behind Spark. The best place to practise without setting up a cluster yourself.
• The official Spark documentation — the programming guides at spark.apache.org are thorough, current, and free.
• YouTube and the Databricks learning portal — plenty of free walkthroughs, plus Databricks’ own free self-paced material that doubles as certification prep.

We don’t earn anything from the free resources above — they’re genuinely good starting points.

How to choose

• Use Python? Frank Kane’s Taming Big Data is the practical, current pick.
• Use Scala? Rock the JVM’s Spark Essentials is the highest-quality option.
• Prefer learning by doing? DataCamp’s interactive PySpark track.
• Want a credential? Take a course, then prepare for the Databricks certification.

Frequently asked questions

What is the best Apache Spark course?

For most people, Frank Kane’s “Taming Big Data with Apache Spark and Python” on Udemy — it’s practical, Python-based, and was updated to Spark 4 in 2026 (4.4 from 18,000+ ratings). For Scala developers, Rock the JVM’s Spark Essentials (4.8) is the highest-quality pick.

Is there an Apache Spark certification?

Yes — the Databricks Certified Associate Developer for Apache Spark is the recognised industry standard, from the company founded by Spark’s creators. The older Cloudera CCA-175 has been retired. Courses give you certificates of completion, but the Databricks exam is the credential that carries weight.

Should I learn PySpark or Scala?

PySpark (Python) is the more popular and easier entry point, ideal if you come from data science or analytics. Scala is Spark’s native language and common on data-engineering teams but steeper to learn. The Spark concepts are the same in both, so you can switch later.

Do I need to know programming first?

Yes. Spark courses teach Spark, not the underlying language, so you should be comfortable with Python or Scala basics first. Knowing SQL also helps a lot, since Spark’s DataFrame and SQL APIs build on it.

Related guides

• Best PySpark Courses
• Best Data Engineering Courses
• Best Big Data Courses

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