Big Data Processing Techniques

How Big Data is Processed

Processing massive datasets requires special techniques that distribute work across many machines.

Batch vs Stream Processing

	Batch	Stream
Data	Stored, processed in chunks	Real-time, continuous
Latency	Minutes-hours	Milliseconds
Tools	Hadoop, Spark	Kafka, Flink

MapReduce Pattern

// Map: break work into key-value pairs
"the cat sat" → (the,1) (cat,1) (sat,1)
// Reduce: combine by key
(the,1) (the,1) → (the, 2)

Key Techniques

• Partitioning — split data across nodes.
• Parallel processing — many machines at once.
• Data lakes — store raw data cheaply (S3, HDFS).
• ETL/ELT — extract, transform, load pipelines.

FAQs

Batch or streaming?

Batch for reports/analytics; streaming for real-time alerts and dashboards. More in our Big Data section.

What is a data lake vs warehouse?

A lake stores raw data; a warehouse stores structured, processed data.