Data Lakes Enable Flexible Big Data Analytics

Organizations today adopt data lakes to store, process, and analyze vast structured and unstructured datasets using a schema-on-read approach. The article outlines core architecture layers (ingestion, storage, processing, analytics, governance), common technologies (e.g., Kafka, Spark, S3), benefits like scalability and ML support, and challenges such as governance and security. Proper metadata and governance are essential to prevent data swamps and enable analytics.