Meet the Snowflake CDC tool
that engineers love

Replicate your operational data into Snowflake reliably and cost-effectively.

Terabyte scale, production ready. No code, low code, or code.


Your operational data is trapped in your operational databases

MongoDB, MySQL, or SQL Server are great when you’re trying to capture millions of user interactions or transactions. But when you want to replicate that data into your analytics targets at terabyte scale, things get tricky. 

You want to ensure data quality and pipeline durability, and existing solutions don’t cut it:

  • General purpose ELT tools get crazy expensive
  • DIY solutions clog your engineering backlog
  • Open source is complicated and messy

What’s a dev to do?

4 Simple Steps to Create a Live Replica

To build a CDC pipeline, you use the Upsolver IDE or CLI to

  1. Connect to your source database,
  2. Snapshot your database and then ingest change events,
  3. Transform (e.g. cleanse, filter, aggregate, enrich, join) the data, and
  4. Output live tables (merging changed rows) to Snowflake.

“Upsolver is like the easy button for Snowflake ingestion”

Alexander Adam, Cloud Platform Engineering Manager, Centene

7 Reasons Developers Love Upsolver for Snowflake Ingestion

1. Easy to use with a no-code experience. Upsolver is easy to extend with a low-code, SQL developer experience, including dbt integration and a python SDK

2. Reliable, self-healing data ingestion pipelines that recover from outages without you needing to do anything

3. Automated schema detection and evolution that matches Snowflake data types and automatically resolves type mismatches.

4. Insert and Merge options: Use insert for fast, low-cost append only ingestion; choose Merge to accurately mirror the source in the target.

5. Replication groups: Group tables that require similar behavior when replicating. Ex. Some tables must be append-only since they are very large, so we will group them and set insert operation to INSERT. Others include common JSON strings that need to be parsed so we’re going to group them and apply a parsing in-flight transformation.

6. Built in data observability and quality monitoring so you can catch problems early and before they propagate throughout your analytics and ML flows

7. Laser-focused on production data at scale: Upsolver currently supports MySQL, Microsoft SQL Server, MongoDB and Postgres. We’re focused on the places where developers store their data – so if your main challenge is Facebook Ads or Google Analytics, Upsolver might not be a great fit.

Let’s get started.

Look, we can sit around all day and talk about Upsolver. But why not try it for yourself? The best way to do that is to get in touch.

We’ll help you setup Upsolver in your AWS environment, explain how it works, and get out of your way.

Schedule a call to get started


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