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.
Key Features
RDD/DataFrame API: Distributed data abstractions for parallel processing
Spark SQL: SQL queries on structured data with optimizations
Structured Streaming: Micro-batch streaming on DataFrames
MLlib: Distributed machine learning library
Catalyst optimizer: Query optimization and code generation
Pricing
Open Source: Free, Apache 2.0 license
Databricks: $0.40-0.75/DBU-hour (most popular managed option)
AWS EMR: EC2 costs + $0.27/hour EMR charge per node
GCP Dataproc: Similar to EMR pricing model
When to Use It
✅ Large-scale ETL and data transformation
✅ Machine learning pipelines
✅ Streaming data processing
✅ Complex data processing logic
✅ Need unified batch and streaming
When NOT to Use It
❌ Small datasets (overhead not justified)
❌ Simple SQL queries (use query engine instead)
❌ Real-time sub-second latency (use Flink)
❌ Ad-hoc queries (use Trino/Presto)
❌ Simple data pipelines (simpler tools exist)
Common Use Cases
ETL at scale: Transform TB/PB of data daily
Machine learning: Distributed training and feature engineering
Stream processing: Real-time data pipelines (micro-batching)
Graph analytics: Social network analysis, recommendation systems
Log processing: Parse and analyze massive log files
Spark vs Alternatives
vs Flink: Flink true streaming and lower latency, Spark easier and more mature
vs Hadoop MapReduce: Spark 100x faster, standard for big data processing
vs Trino/Presto: Spark for transformations, Trino for queries
Unique Strengths
Unified platform: Batch, streaming, ML, graph in one engine
In-memory speed: 100x faster than MapReduce for iterative workloads
Ecosystem maturity: Huge community, extensive libraries
Language support: Python, Scala, Java, R, SQL all first-class
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.