Data Lake
Definition
A data lake is a centralized storage repository that holds raw data in its native format -- structured, semi-structured, and unstructured -- at any scale. Unlike traditional data warehouses that require data to be transformed and loaded into a predefined schema, data lakes store data as-is, enabling flexible analysis and processing after storage.
Data lakes are typically built on object storage (AWS S3, Azure Data Lake Storage, Google Cloud Storage) and use open table formats (Apache Iceberg, Delta Lake, Apache Hudi) to add transactional capabilities, schema enforcement, and time-travel queries on top of raw file storage.
Why It Matters
Organizations generate data faster than they can process it. Log files, IoT sensor data, application events, documents, images, and API responses all contain valuable information, but they do not fit neatly into relational database schemas.
Data lakes solve this by separating storage from processing. You store everything affordably in object storage and decide how to process it later. This enables use cases that traditional warehouses cannot support: training ML models on unstructured data, running ad-hoc analytics on raw event streams, and combining structured business data with unstructured documents.
The risk is the "data swamp": a data lake without governance, catalog, or quality controls becomes an expensive storage bucket that nobody trusts. The difference between a data lake and a data swamp is governance.
Key Components
- Ingestion -- Batch and streaming data capture from diverse sources
- Storage -- Object storage with open table formats for transactional capabilities
- Catalog -- Metadata management for data discovery and lineage tracking
- Processing -- Compute engines (Spark, Presto, Trino) for transformation and analysis
- Governance -- Access controls, data quality monitoring, PII detection, and compliance
How BAG Uses It
We architect data lakes and lakehouses for clients in financial services, insurance, and manufacturing. Our approach emphasizes governance from day one: data catalogs, quality monitoring, lineage tracking, and access controls are not afterthoughts. We have built platforms processing billions of records across multiple industries, using Apache Iceberg for transactional data lake capabilities.