Puneet Gupta

Need to replicate your SQL Server tables into Snowflake? Supaflow's SQL Server connector supports two query modes: Standard for simple cursor-based sync, and Change Tracking for capturing every insert, update, and delete -- including hard deletes that standard mode can never detect.

This guide walks through setting up a SQL Server to Snowflake pipeline with Change Tracking enabled, so your Snowflake tables stay in sync with every change made in SQL Server.

Most people who want to build a Supaflow connector already have deep domain expertise. They know the Asana API inside out, or they understand exactly how Stripe's pagination works, or they have spent years working with Dynamics 365. What they do not know -- and should not have to learn from scratch -- is the Supaflow connector SDK: how schema discovery works, how incremental sync state is managed, what lifecycle methods to implement, and what contracts the pipeline engine expects.

That is the gap this project fills. We codified everything we know about the Supaflow connector SDK into an AI skill framework that any coding agent can follow -- so you can focus on your domain expertise while the skill handles the SDK plumbing.

It is open source and available now on GitHub: supaflow-labs/supaflow-connector-dev-skills.

Got files sitting in Google Drive that you need in Snowflake? Whether they are CSVs, Excel workbooks, TSVs, or Google Sheets, Supaflow's Google Drive connector can sync them into Snowflake tables automatically -- with schema discovery, incremental sync, and schema evolution built in.

This guide walks through the full setup end to end using CSV files, but the same workflow applies to all supported file types.

Most ETL tools move your data through a third-party cloud before loading it into Snowflake. Supaflow takes a fundamentally different approach: the pipeline engine runs inside your Snowflake account as a Snowflake Native App. Your data never leaves Snowflake.

We are now live on the Snowflake Marketplace -- install directly from Snowsight and start running pipelines in under 20 minutes.

Introducing Supaflow​

A single platform to replace multiple stitched-together pipeline tools.

Modern data teams are drowning in vendor sprawl. The average company uses four to five different tools just to move data from Salesforce to Snowflake—and back again. Each vendor adds another contract, another integration to maintain, and another potential point of failure.

That's why we built Supaflow: a unified data movement platform that collapses ingestion, transformation, activation, and orchestration into one secure, flexible solution—without your data ever leaving your environment.

Export Salesforce to S3 in 10 Minutes​

Export Salesforce to S3 and query it in Athena — no ETL tools, no long‑running servers.

This open‑source, serverless pipeline uses AWS Lambda, Step Functions, and the Salesforce Bulk API 2.0 to export every Salesforce object to S3. After each run, it automatically updates the AWS Glue Data Catalog, so your data is immediately queryable in Athena.

After a single deployment, you can query your Salesforce data directly in Athena:

SELECT * FROM salesforce_export.account_raw LIMIT 10;