Datalake

A data lake is a centralized repository that stores vast amounts of raw data in native formats at any scale. It supports structured, semi-structured, and unstructured data and is often built on low-cost object storage. Data can be ingested from diverse sources such as databases, logs, sensors, and applications without requiring upfront schema.

The data is typically stored in its original form (schema-on-read) and cataloged with metadata to enable discovery and governance. A data lake architecture usually includes ingestion pipelines, a storage layer, and processing engines or query services that support batch and streaming analytics.

Common formats include Parquet, ORC, JSON, and CSV. Processing may be performed with engines such as Spark, Trino, or Flink, and data can be queried with SQL or programmatic interfaces. Data may be organized into zones or layers such as raw, curated, and trusted to support different governance and consumption needs; or accessed directly as needed.

Security and governance are essential, including access controls, encryption, auditing, and data lineage. Data quality and consistency are managed through metadata, validation rules, and transformations, often in ELT pipelines.

Use cases include data science, exploratory analytics, big data processing, data integration, and machine learning. A related concept is the data lakehouse, which aims to combine the low-cost storage of a data lake with the transactional guarantees and performance of data warehouses.

Challenges include governance, discoverability, and performance tuning. Effective implementation relies on clear data governance, robust metadata, and scalable processing.