A data lake is a centralized repository that stores all types of enterprise data, including structured, semi-structured, and unstructured data, at any scale. Teams keep data in its raw form and apply structure only when reading it, which makes lakes ideal for fast ingestion and flexible analysis across many workloads.

Key ways organizations use a data lake

• Analytics and BI on diverse, large datasets. Lakes aggregate clickstream logs, app events, and transactional extracts in one place so analysts can join them for dashboards and ad-hoc SQL. Because storage is decoupled from compute, you can analyze the same files with multiple engines (SQL, Spark, notebooks) without moving data.
• Data science and machine learning. Data scientists prefer lakes for feature engineering and model training because they can retain long histories and high-granularity data (text, images, sensor feeds). This breadth fuels recommendation, churn, fraud, and NLP models.
• Real-time and streaming analytics. From IoT telemetry to application logs, lakes are used to land high-volume streams for near-real-time monitoring, alerting, and personalization.

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