Join MySQL, PostgreSQL, files, and S3-backed data from one workspace to validate, compare, and shape datasets before migration or automation.
Most traditional tools execute queries against a single active connection. Cross-connection joins typically require database-level federation or external pipelines.
DBeaver, DataGrip, and similar tools run queries against a single active connection per SQL editor tab. Cross-database joins depend on database-level federation such as FDW or linked servers.
BigQuery, Snowflake, and Redshift typically require data to be ingested or staged before cross-source joins. External querying is possible but requires additional configuration and managed integrations.
Airbyte, Fivetran, and dbt focus on extracting and loading data into a destination before it can be joined or analyzed. Ad-hoc cross-source queries are not their primary workflow.
Traditional database IDEs do not include a built-in federated SQL engine for ad-hoc joins across separate connections. Cross-connection joins typically require database-level configuration or manual export/import.
DBConvert Streams removes all of this overhead with a built-in federated SQL engine.
Architecture
An embedded DuckDB engine that attaches to your live sources and executes queries in place.
Attach
Each source is attached as a virtual schema
Plan
Query planned centrally by DuckDB optimizer
Execute
Results returned instantly from attached sources
No Staging
No persistent staging or replication required
Copy-paste queries across databases and files.
Note: pg1 and my1 are connection aliases defined in DBConvert Streams. They represent configured PostgreSQL and MySQL connections.
SELECT
a.first_name || ' ' || a.last_name AS actor_name,
f.title AS film_title
FROM pg1.public.actor a
JOIN pg1.public.film_actor fa ON a.actor_id = fa.actor_id
JOIN my1.sakila.film f ON fa.film_id = f.film_id
LIMIT 20;Joins tables across PostgreSQL and MySQL in a single query — no export, no import, no staging.
(SELECT 'PostgreSQL' as source, film_id, title, release_year
FROM pg1.public.film
WHERE title LIKE 'A%'
LIMIT 5)
UNION ALL
(SELECT 'MySQL' as source, film_id, title, release_year
FROM my1.sakila.film
WHERE title LIKE 'A%'
LIMIT 5);Combines results from PostgreSQL and MySQL into one dataset.
Compare data between source and target before cutover. Verify row counts, checksums, and content match across migration runs.
Use SQL to inspect and filter source data before starting a migration. Identify schema mismatches or data quality issues upfront.
Combine live database records with archived Parquet or CSV exports in S3 for historical analysis without re-importing.
Investigate production vs staging mismatches. Run comparison queries directly without building replication streams first.
Combine operational data from MySQL and PostgreSQL into unified reports without a data warehouse.
Analyze Parquet or CSV exports together with live databases using full DuckDB SQL - aggregations, window functions, and transformations.
Convert Mode
The SQL you write to explore is the same SQL that powers a stream. Point it at a target — another database, a file, S3 — and the data moves. No hand-off between tools. No rebuild.
Related workflows
Use the surrounding pages when you want to move from ad-hoc query work into explorer workflows, migration execution, or API-driven automation.
Use the explorer workspace when you want schema browsing, inline inspection, and SQL in the same UI.
Move from validation into snapshot migration when the query output is ready to become the transfer source.
Use the API when you want to script query-driven workflows, stream setup, or operational checks.
Use the explorer for hands-on query work and keep this page for patterns you can copy. Move into migration or API automation only after the SQL is doing the right job.