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Welcome to Upsolver

Data Movement for Data Developers.


Upsolver’s unique approach offers you no-code and low-code options to easily create pipelines for high-scale data movement in minutes. Upsolver makes working with data easier by automatically mapping columns and data types between sources and targets, evolving the schema in pace with data even for nested data structures, and parsing and flattening JSON structs and arrays.
Got a minute? Check out this quick introduction to Upsolver:
Introducing Upsolver. Build Pipelines. Not DAGs.

What Can Upsolver Do for Me?

  • Easy ingest to Snowflake: use no-code or low-code options to build ingestion pipelines in three steps
  • Failsafe exactly once delivery: up-to-the-minute freshness without lost, duplicated, or out-of-order data
  • Automatic schema evolution: automatically map source fields to targets despite column type and naming conflicts
  • Built-in data quality and observability: detect and fix data drift quickly and retroactively
  • Support for mainstream data platforms: Amazon Kinesis, Amazon Redshift, Amazon S3, Apache Kafka, data lake, Confluent Kafka, Elasticsearch, Microsoft SQL Server, MongoDB, MySQL, PostgreSQL, and Snowflake
Current Release: 2023.11.26-08.24
Check out the November 2023 Release Notes to discover the latest features.

New to Upsolver?

Follow the tailored Learning Path for your data ingestion journey. Designed to get you up and running quickly, each path steps through the options for your data source and target combination.
Get Started
Quickstarts: hands-on learning for data developers.
How-To Guides
Step-by-step guides: Browse the guides for everyday use cases.
SQL syntax reference: view code samples to create advanced jobs.
Learning Videos
Videos: learn how to build ingestion pipelines with our in-house experts.
Peer-learning: engage on our socials, sign up for a workshop or meet-up.
Resources: browse our extensive range of data topics in our blog posts.
Last modified 15h ago