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Connector Guide

Extract PostgreSQL Data to Snowflake

A no-code walkthrough for building a production PostgreSQL-to-Snowflake ETL pipeline with DataKnits — no hand-written loader scripts.

Why Move Postgres Data Into Snowflake

PostgreSQL is a strong operational database, but running heavy analytical queries against a production instance competes with transactional workloads for resources. Replicating the relevant tables into Snowflake separates analytics from operations and gives BI tools a warehouse built for large scans and joins.

How DataKnits Builds the Pipeline

  • ✓ Connect to PostgreSQL — DataKnits introspects tables and column types automatically
  • ✓ Select full tables or a custom SQL extract as the source node
  • ✓ Map source columns to a Snowflake destination schema on the visual canvas
  • ✓ Compile to native SQL push-down or PySpark code — inspect it before running
  • ✓ Schedule as a batch job, or configure as a CDC stream for near-real-time sync

Compiled Output (Example)

df_pg = spark.read.format("jdbc")
  .option("url", "jdbc:postgresql://source-db/orders")
  .option("dbtable", "public.orders").load()

df_pg.write.format("snowflake")
  .options(**sf_options)
  .option("dbtable", "ANALYTICS.ORDERS")
  .mode("append").save()

Frequently Asked Questions

How do I extract PostgreSQL data to Snowflake?

Connect DataKnits to your PostgreSQL instance, select the tables or a custom query as the source node, map the schema to a Snowflake destination node on the visual canvas, and DataKnits compiles the pipeline into native PySpark or SQL push-down code that runs the extraction and load.

Does this support incremental or real-time sync, not just a one-time load?

Yes. Pipelines can run on a schedule for incremental batch loads, or be built as Change Data Capture (CDC) flows that synchronize PostgreSQL changes into Snowflake with low latency.

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