Apache Spark
A unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, ML, and graph computation. Processes data in-memory for speed.
What is Apache Spark?
A unified analytics engine for large-scale data processing with built-in modules for SQL, streaming, ML, and graph computation. Processes data in-memory for speed.
Apache Spark is a advanced concept that sits in the Stream & Batch Processing area of system design. Engineers reach for it whenever they need to reason about real-world trade-offs in that space — not just for textbook correctness, but because real production systems at companies like Netflix, Amazon, and Google make these decisions every day.
If you want to go deeper than this definition — with diagrams, code, and a quiz to lock it in — work through the "Apache Spark" lesson linked below. It walks through the why, the mechanism, the trade-offs, and how the giants actually use it in production.
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Full interactive lesson with diagrams, code examples, real-world references, and a quiz.
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Related glossary terms you might want to look up next.
Apache Flink
A distributed stream processing framework that handles both real-time streams and batch data with exactly-once guarantees. Used by Alibaba, Netflix, and Uber at massive scale.
Batch Processing
Processing large volumes of data in scheduled chunks rather than in real time. Think nightly reports, ETL jobs, and data warehouse loads.
MapReduce
A programming model for processing massive datasets in parallel across a cluster. Map splits data into key-value pairs; Reduce aggregates them. Pioneered by Google, implemented by Hadoop.