Datalakeet

Datalakeet is a term used to describe a modular, vendor-agnostic framework for building and operating data lake environments. It combines storage, ingestion, metadata management, governance, and data consumption components into an integrated platform designed for scalability and governance.

Key features include modular ingestion pipelines, schema-on-read capabilities, a metadata catalog, data lineage, access controls, and data quality checks. It emphasizes interoperability with cloud object stores (such as S3, Azure Blob, Google Cloud Storage) and common data formats (Parquet, Avro, JSON, ORC).

Architecture typically comprises a storage layer, where raw and processed data reside; a processing/ingestion layer for ELT pipelines and transformation; and a governance/catalog layer that tracks metadata, lineage, quality, and access policy. It often supports table formats like Iceberg, Delta Lake, or Hudi.

Common use cases include analytics, business intelligence, data science, and data sharing across teams or organizations. Datalakeets aim to reduce setup complexity, improve discoverability via catalogs, and enable governed data access without duplicating data.

In practice, "datalakeet" is not a formal standard; it appears as a descriptive term for a set of best practices and reusable components. Implementations vary and may be provided by open-source projects or cloud services; adopters should evaluate compatibility with existing data pipelines and governance requirements.