Sync PostgreSQL with Snowflake for Enterprise-Scale Analytics

On a configurable schedule, this template queries specified PostgreSQL tables for new or updated records since the last run, transforms the data to match the target Snowflake schema, and bulk-loads the results into Snowflake using efficient COPY or INSERT operations.

Steps:

• Trigger on a time-based schedule (e.g., every hour or every 15 minutes)
• Query PostgreSQL for rows where updated_at or created_at exceeds the last watermark timestamp
• Map and transform column names, data types, and null handling between systems
• Bulk insert or upsert transformed records into the target Snowflake table
• Update the watermark value to the latest processed timestamp for the next run

Connectors Used: PostgreSQL, Snowflake

Whenever a new row is inserted into a specified PostgreSQL table, this template captures that record and inserts it into a corresponding Snowflake table — enabling near-real-time data availability in the warehouse for latency-sensitive use cases.

Steps:

• Detect new row insertion in a monitored PostgreSQL table via polling or trigger
• Retrieve the full record payload including all relevant columns
• Transform and map fields to match the Snowflake target table schema
• Insert the record into the designated Snowflake table

Connectors Used: PostgreSQL, Snowflake

This template orchestrates parallel syncs across multiple PostgreSQL tables in a single workflow run, loading each table's data into its corresponding Snowflake destination. It's well-suited for nightly full data warehouse refresh jobs.

Steps:

• Trigger on a nightly schedule or manual invocation
• Iterate over a configured list of PostgreSQL source tables
• For each table, extract records in paginated batches to handle large volumes
• Load each batch into the matching Snowflake table using staged bulk insert
• Log row counts and completion status for each table to a monitoring destination

Connectors Used: PostgreSQL, Snowflake

This template handles upsert logic by merging PostgreSQL records into Snowflake using a unique key, so updated records overwrite their previous versions rather than creating duplicate rows. That matters for keeping data accurate in the warehouse.

Steps:

• Query PostgreSQL for records modified since the last successful sync
• Stage incoming records in a Snowflake temporary or staging table
• Execute a MERGE statement in Snowflake to upsert records using the primary key
• Log matched, inserted, and updated record counts for auditing

Connectors Used: PostgreSQL, Snowflake

This template monitors a PostgreSQL table's schema for column additions or type changes, sends an alert to a Slack channel or email, and optionally applies the corresponding schema change to the Snowflake target table to keep both systems in sync.

Steps:

• Poll PostgreSQL information_schema on a scheduled basis to detect column changes
• Compare current schema against a stored baseline schema definition
• If changes are detected, send a notification alert with a diff summary
• Optionally execute an ALTER TABLE statement on the Snowflake target table
• Update the stored schema baseline to reflect the current state

Connectors Used: PostgreSQL, Snowflake

A one-time or on-demand template that exports large volumes of historical data from PostgreSQL in paginated batches and loads them into Snowflake. It's built to handle millions of rows without timeouts or memory issues.

Steps:

• Accept input parameters for source table name, date range, and batch size
• Paginate through PostgreSQL records in configurable batch sizes using LIMIT/OFFSET or keyset pagination
• Transform each batch to match the Snowflake destination schema
• Load each batch into Snowflake and log progress after each successful batch
• Send a completion summary with total rows migrated and any errors encountered

Connectors Used: PostgreSQL, Snowflake