Data Platform services
Seven services, documented in the order data moves through a project.
How the services fit together
Five of the seven services form the path data takes through a project: in, stored, transformed, queried, then exposed. The other two are conditions on all of it rather than steps in it.
Connectors is the door. It registers every external system the project reads from, decides what structure incoming data arrives with, and exposes results back out to external BI and SQL tools. Lakehouse Manager is what the project knows about its data: datasets, tables, attributes, buckets, lineage, and the policy tags that govern access down to a single column. Data Processing Engine does the work, through actions, workflows and notebooks, and through the jobs that execute them. Analytics Manager turns tables into queries, charts and dashboards. Application Services ships the result, either as an authenticated custom API or as a deployed web application.
The two cross-cutting services come last. Control Center watches every job and deployment, holds the logs and the audit trail, raises alerts and scales resources. Identity Access Manager decides who can read and change what, at organization and project level.
The boundaries are deliberate. Registering a connection, modelling the data behind it and running the pipeline that fills it are three different jobs, and they can belong to three different teams.
Connectors→
Register sources, control what comes in, expose what goes out.
Lakehouse Manager→
Serverless Iceberg storage for datasets, tables and buckets.
Data Processing Engine→
Actions, workflows and the jobs that run them.
Analytics Manager→
Query the lakehouse and build dashboards on it.
Application Services→
Expose data as an authenticated API or a deployed app.
Control Center→
Metrics, logs, alerts and resource scaling.
Identity Access Manager→
Who can do what, at organization and project level.
Go further
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