Learn Spark & Databricks by seeing what happens under the hood.
From your first DataFrame to Catalyst, shuffle, AQE, Delta Lake and production architecture — explained through a simulator you can step through.
No account needed. Your progress is saved automatically.
What you will actually see
Educational simulation · not live cluster data
Now showing: Driver
How it works
Visualization first, text second — so the mental model comes before the jargon.
Step 1
Watch it execute
Step through Driver → Job → Stages → Tasks → Shuffle at your own pace. Every step says what Spark is doing and why.
Step 2
Inspect any piece
Tap a task to see its partition, executor, and host. The three ideas people most often confuse, kept clearly separate.
Step 3
Then go deeper
Switch to Learn or Interview when you want internals, production failure cases, and the questions you will be asked.
Partitions, tasks, executors
See why one partition means one task, and how executors pick that work up.
What groupBy really costs
Watch records fly across the cluster so each key lands on one reducer.
What happens when you run a Spark job?
groupBy + show() — 11 guided steps, from your code to the result.
Curriculum
23 lessons · 5 simulators · interview tracks
- Fundamentals
Spark Overview
What Spark is, why it exists, and the mental model of driver, executors, and lazy plans.
- Fundamentals
Spark Architecture
Cluster manager, driver JVM, executor JVMs, and how a SparkSession talks to a cluster.
- Fundamentals
Execution Flow
Trace groupBy + show() from lazy code through jobs, stages, tasks, shuffle, and the result.
- Fundamentals
RDD
Resilient Distributed Datasets: partitions, lineage, and when you still need the RDD API.
- Fundamentals
DataFrame
Structured data with a schema. Catalyst can optimize DataFrames in ways RDDs cannot.
- Fundamentals
Transformations
Narrow vs wide transformations, and why nothing runs until an action arrives.
- Fundamentals
Actions
show, count, collect, write — the calls that force Spark to produce a result.
- Fundamentals
Lazy Evaluation
Spark builds a DAG of transformations and waits. That wait is a feature, not a bug.
- Core Concepts
Partitions
A partition is a chunk of data. One task processes one partition. Size them for healthy runtime.