Data tooling landscape

Decides what runs, when and how. Orchestration, infrastructure as code and CI/CD for data.

Where data is stored and processed. Warehouses, lakehouses, open table formats, engines, storage and streaming.

Moves and shapes data. Ingestion connectors, change data capture, transformation and reverse ETL.

Proves data is fit for use. Tests, validation rules, data contracts and profiling.

Tells you when data or pipelines break, and what they cost. Data observability, monitoring and FinOps.

Describes the data. Catalogues, technical catalogues, lineage and semantic layers.

Decides who may use which data, and for what. Access policies, stewardship, privacy and master data.

Protects data and access. Identity, secrets and keys, data security posture, masking and tokenisation.

Turns data into decisions. BI and dashboards, notebooks and data apps.

Builds on governed data. ML platforms, feature stores and vector search.