Apiome Data Federate
Run one SQL query across PostgreSQL, Snowflake, BigQuery, S3, and apiome-db — without moving a single row.
Apiome Data Federate is a cross-source virtual query engine built on Apache DataFusion. Write SQL that joins PostgreSQL, MySQL, Snowflake, BigQuery, S3 Parquet, and apiome-db simultaneously; predicates are rewritten and pushed down to each source's native dialect while DataFusion handles cross-source joins locally. Materialize the results straight into apiome-db Classes.
What ships with Data Federate
Every Data Federate surface is wired into the rest of the Apiome platform — no glue code, no separate identity, no bolt-on integrations.
Federation SQL editor
Write cross-source JOINs against a live catalog explorer, see pushdown decisions inline, and view results side-by-side — powered by DataFusion.
Visual query planner
Inspect the logical and physical plan tree with estimated row counts, pushdown annotations, and data-volume-per-step breakdowns.
Source catalog
Register and manage PostgreSQL, MySQL, Snowflake, BigQuery, S3 Parquet, and apiome-db — each becomes a virtual catalog in the editor.
Unified virtual schemas
Browse every source schema and table in one catalog tree, compare column definitions, and see which externals map to apiome Classes.
Per-source dialect config
Tune the pushdown capability matrix — which predicates, aggregations, and window functions each source engine can execute natively.
Scheduled materialization
Promote saved queries to materialization jobs that write into apiome-db Classes with incremental watermarks and Temporal orchestration.
A look inside Apiome Data Federate
Live design previews from the Data Federate mockup pack — 9 surfaces in total.
Visual DataFusion plan tree with per-step row estimates, pushdown coverage, and a breakdown of local hash-join work versus data read from each source.
Link external table columns to apiome Class properties one-by-one, defining type coercions, rename rules, and null-handling so results materialize cleanly.
Materialize federation results into apiome-db Classes on a schedule or on demand with append vs. replace semantics, incremental watermarks, and Temporal workflows.
Use cases
Data Federate is designed around the way real teams actually work — not the way a tool wants them to work.
Join Snowflake revenue to a PostgreSQL user table and apiome Classes in one query without building an ETL pipeline first.
Prototype a cross-warehouse rollup in the SQL editor, then save it as a shared virtual view for the whole team.
Materialize a federated customer-360 view into apiome-db Classes nightly with incremental watermarks.
- Pushdown governance with per-source capability policies
- Full federation query audit log with data-volume accounting per source
- Credentialed source connections with secret management and rotation
- Row- and column-level access controls across federated catalogs
- Temporal-backed materialization SLAs with retry and backfill
- Generate cross-source JOIN SQL from a natural-language ask
- Suggest rewrites that push more predicates down to each source
- Explain any query plan step and its pushdown decision
- Recommend column mappings and type coercions for materialization
AI copilot for federated SQL
Data Federate pairs the editor with an assistant that understands every registered catalog — it drafts cross-source queries from plain English and rewrites them to maximize pushdown.
Every Data FederateAI feature is grounded in your tenant's data, runs under your data-residency policy, and respects every role and ACL the platform enforces.
Every surface in Apiome Data Federate
A look at the 9 screens designed for this suite — covering everything from day-1 onboarding to day-100 operations.
