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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.
- 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
Last modified 15h ago