Master PySpark,
right in your browser.
Close your spark gap.
Read a short lesson, then solve real PySpark problems graded instantly against a genuine Spark engine. No Hadoop. No containers. No SPARK_HOME. Just you and the code.
Try a real problem in ten seconds — no sign-up required.
PulsePoint — return every visit lasting at least 45 minutes.
from pyspark.sql import functions as F
result = checkins.filter(F.col("minutes") >= 45) \
.select("member", "minutes")| member | minutes |
|---|---|
| Ada | 50 |
| Cara | 45 |
| Dan | 60 |
The old way
Install a JVM. Fight Hadoop winutils. Set SPARK_HOME. Decode a Py4J stack trace…
…and quit before you've written a single transformation. Most people burn out at setup and never reach the actual learning.
Spark Plug
Open a problem. The DataFrame is already loaded. Write PySpark. Hit Run. Done.
We fought the JVM so you don't have to. The only thing between you and Spark is the code you came to write.
How it works
Teach, then test. You're never thrown a problem you weren't equipped for.
Learn
Bite-sized lessons on the concepts that actually show up in interviews and on the job.
Solve
Write real PySpark in a live editor, with the data already in scope. Run whenever you like.
Get graded
Instant, structured feedback. See exactly how your output differs from expected, and why.
Your code runs against a real Spark-Connect engine — lazy evaluation, joins, window functions, all behaving like production. We just took the JVM out of your way.
No Pyodide fakes, no silently-swapped-in query engine pretending to be Spark. When it says you passed, you passed real Spark.
A real difficulty ladder
Approachable on-ramp, genuinely challenging peak — 41 problems across the curriculum spine, from your first filter to interview-grade window functions.
One concept, one transformation. The on-ramp.
Two or three chained ideas — a join and an aggregate.
Windows, multi-step — the FAANG data-engineering loop.
Why Spark, in 2026?
Honest answer: for small data, DuckDB and Polars often win — and we'll tell you when Spark is the wrong tool. That honesty is the point.
But for large, distributed workloads Spark is still the standard, and the new Rust engines (Sail, DataFusion Comet) are keeping its API more alive than ever. Learn the interface that outlives the engine.
What's free today
No card, no setup. Our whole promise is removing friction, so the on-ramp is never held hostage.
- Every lesson and tutorial
- The live editor + instant grading
- The full starter problem set, Easy → Hard
- Streaks and progress tracking
Spark Plug Pro — the full library, solution walkthroughs, and timed mock interviews — is coming. It'll add depth on top; it'll never gate the on-ramp.
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