Apache Spark
What it is: Unified analytics engine for large-scale data processing. In-memory computation, 100x faster than MapReduce. Batch, streaming, ML, graph processing.
What It Does Best
In-memory processing. Cache data in RAM for iterative algorithms. Machine learning, graph algorithms fly.
Unified API. Spark SQL, DataFrames, Streaming, MLlib, GraphX. One engine for all data workloads.
Language flexibility. Python (PySpark), Scala, Java, R, SQL. Data engineers and scientists both productive.
Pricing
Free: Open source, Apache 2.0. Managed Spark: Databricks, AWS EMR, Azure Synapse, GCP Dataproc (compute-based).
When to Use It
✅ Large-scale ETL and data transformation
✅ Machine learning pipelines
✅ Streaming data processing
✅ Complex data processing logic
When NOT to Use It
❌ Small datasets (overhead not justified)
❌ Simple SQL queries (use query engine instead)
❌ Real-time sub-second latency (use Flink)
Bottom line: Industry standard for big data processing. Replaced MapReduce/Hive for most workloads. Use Databricks if you can afford it, self-managed on EMR/Dataproc if not. Essential skill for data engineers.