Apache Spark

Unified analytics engine for large-scale data processing

Overview

Apache Spark is the de facto standard for large-scale data processing. It provides APIs in Python, Scala, Java, and R for batch processing, streaming, machine learning, and graph processing. Created at UC Berkeley, it powers data infrastructure at thousands of companies.

Key Features

✓ Unified Engine: Batch, streaming, ML, graph in one
✓ Spark SQL: SQL interface for structured data
✓ MLlib: Distributed machine learning library
✓ Structured Streaming: Stream processing on DataFrames
✓ Multi-language: Python, Scala, Java, R, SQL
✓ In-memory: Fast iterative processing

Pros

👍 Industry standard for big data
👍 Massive ecosystem
👍 Scales to petabytes
👍 Multiple managed offerings (Databricks, EMR, Dataproc)
👍 Strong community

Cons

👎 Complex to tune and optimize
👎 Resource-intensive
👎 Steep learning curve
👎 Overkill for smaller datasets
👎 JVM overhead

Best For

Organizations processing large-scale data that needs distributed computing. Essential for big data ETL, ML pipelines, and data lake processing.

Founded: 2014 HQ: Apache Software Foundation