Spark Theory for Data Engineers
Get started with the foundational topics of Spark for Data Engineering.
Tutorial #1
⚡Introduction to Apache Spark
Understand what Apache Spark is, why it is used, and how it works at a high level.
Tutorial #2
⚙️Spark Architecture
Learn how Spark’s Driver, Executors, and Cluster Manager work together to execute distributed jobs efficiently.
Tutorial #3
🔄Transformations & Actions
Understand how Spark transformations build lazy execution plans and how actions trigger job execution.
Tutorial #4
📦Resilient Distributed Dataset
Understand what RDDs are, how they work under the hood.
Tutorial #5
📊DataFrames & Datasets
Learn how DataFrames provide a higher-level abstraction with schema enforcement and optimization.
Tutorial #6
⏳Lazy Evaluation
Learn how Spark optimizes performance by delaying execution until an action is triggered.
Tutorial #7
🚀The Catalyst Optimizer
Understand how the query optimizer transforms logical plans into efficient physical execution plans.
Tutorial #8
➕More Topics Coming Soon
Additional Spark concepts, internals, best practices and deep dives will be added soon.