# Data Engineering
All Data Engineering notes →1 — Distributed Computing Fundamentals
The distributed-systems fundamentals underneath every big-data engine — parallel processing, distributed execution, cluster computing, scheduling, and resource management.
data-engineering distributed-processing book
2 — Apache Spark
Apache Spark end to end — architecture, RDDs, DataFrames and Datasets, the Catalyst optimizer and Tungsten execution engine, shuffle and partitioning behavior, broadcast joins, and adaptive query execution.
data-engineering distributed-processing book
3 — Batch Processing Frameworks
The batch processing framework landscape beyond Spark — Hadoop MapReduce, Tez, Flink's batch mode, and Apache Beam's unified batch/stream model.
data-engineering distributed-processing book
4 — Stream Processing
Stream processing engines — Spark Streaming, Structured Streaming, Flink, Kafka Streams, Beam — and the watermarks, windowing, state management, and event-time-vs-processing-time semantics they all have to solve.
data-engineering distributed-processing book