QueryFlow connects to nine sources: Snowflake, Redshift, PostgreSQL, MySQL, BigQuery, Databricks, Salesforce, Google Sheets, and CSV/Excel files. This page lists every source-to-destination pair we have a dedicated how-to for, grouped by destination, with an honest note on what kind of write each destination actually supports.
Quick answer: BigQuery and Databricks are full Data Sync targets, accepting Insert, Update, or Upsert via MERGE on key columns, and both also work as scheduled job destinations, though only queries sourced from Snowflake, Redshift, BigQuery, or Databricks and delivering to S3, SFTP, a local file, or email run through the background helper with QueryFlow closed. Snowflake, Redshift, PostgreSQL, MySQL, and Salesforce each sync on a schedule too, with the exact write pattern (snapshot, incremental, or upsert) and background-helper support noted per destination below.
Each group below is one destination. Under it are the source-to-destination pages we have written, one per pair, each with a real worked example in that warehouse's dialect (BigQuery uses project.dataset.table backtick syntax, Databricks uses Unity Catalog's catalog.schema.table). The one-line note under each destination name tells you what kind of write it actually supports in QueryFlow today, not what a competitor's marketing page implies a "sync" means.
These are two different things inside QueryFlow, and mixing them up is the most common confusion. A Data Sync target is a table QueryFlow keeps current on a schedule, using Insert, Update, or Upsert logic keyed on columns you choose, the way upsert vs. insert vs. update describes it. A job destination is simpler: a scheduled query's result lands somewhere, full replace or append, with no merge logic at all. BigQuery and Databricks support both patterns. Snowflake, Redshift, PostgreSQL, and MySQL are Data Sync targets on a schedule using the snapshot/incremental/upsert patterns described on the Postgres-to-Snowflake page. Salesforce today is a destination for CSV and Postgres loads specifically, not a general-purpose sync target for every source.
Snowflake, Redshift, BigQuery, and Databricks jobs run through a background helper process that can complete a scheduled job and deliver results to S3, SFTP, a local file, or email even with QueryFlow itself closed. Postgres, MySQL, and Salesforce jobs currently run while the app is open. If you need a pipeline to survive your Mac going to sleep or the app quitting, put the compute-heavy side of it on one of the four background-helper warehouses.
Data Sync target with Insert, Update, or Upsert via MERGE on key columns, plus a scheduled job destination (Append or Replace). A scheduled query sourced from BigQuery and delivering to S3, SFTP, a local file, or email can run through the background helper with QueryFlow closed.
Data Sync target: snapshot, incremental, or upsert on a schedule. A scheduled query sourced from Snowflake and delivering to S3, SFTP, a local file, or email can run through the background helper with QueryFlow closed.
Data Sync target with Insert, Update, or Upsert via MERGE on key columns (Unity Catalog naming), plus a scheduled job destination. A scheduled query sourced from Databricks and delivering to S3, SFTP, a local file, or email can run through the background helper with QueryFlow closed.
Data Sync target on a schedule. Postgres jobs run while QueryFlow is open, not through the background helper.
Data Sync target on a schedule. MySQL jobs run while QueryFlow is open, not through the background helper.
Data Sync target on a schedule, connecting with IAM keys. A scheduled query sourced from Redshift and delivering to S3, SFTP, a local file, or email can run through the background helper with QueryFlow closed.
Job destination via the Salesforce connector. Runs while QueryFlow is open.
If the source isn't a live database at all, just a file someone handed you, the CSV and Excel loader pages above cover a one-time or repeated file load into a warehouse without writing an import script. The CSV-to-CREATE-TABLE tool is a free way to generate the destination table's schema first if you don't already have one.
A Data Sync target accepts Insert, Update, or Upsert writes on a schedule so a destination table stays current. A job destination just receives the output of a scheduled query, typically as a full replace or append, without merge logic.
Scheduled queries sourced from Snowflake, Redshift, BigQuery, or Databricks and delivering to S3, SFTP, a local file, or email run through a background helper and can complete with the app closed. Data Sync jobs, and any job with a different source or destination, run while QueryFlow itself is open.
Yes. Set up a separate pipeline per source, each targeting its own table (or the same table on a schedule that does not overlap), and schedule them independently.
QueryFlow connects to nine sources today: Snowflake, Redshift, PostgreSQL, MySQL, BigQuery, Databricks, Salesforce, Google Sheets, and CSV/Excel files. Requests for new ones go on the public roadmap.
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