Big Data

Big data refers to datasets too large, complex, or fast-moving to be processed with traditional database tools and standard analytics approaches. It is characterized by the three Vs: Volume (massive scale), Velocity (high-speed generation and ingestion), and Variety (structured, semi-structured, and unstructured data mixed together).

The big data era drove development of distributed processing frameworks like Apache Hadoop and Spark, which split computation across many machines in parallel. These tools made it economically feasible to process petabyte-scale datasets that would overwhelm any single server.

For most API and dataset use cases - even large ones - true big data infrastructure is unnecessary. A property listing dataset of 10 million records is large but processable with standard tools. Big data infrastructure becomes relevant when dealing with streaming event data, log files, or aggregated datasets from platforms with hundreds of millions of active users.