Welcome to Upsolver

Upsolver is the self-serve cloud data ingestion service for high-scale workloads such as big data, streaming, and AI. Our unique approach offers you no-code and low-code options to 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.

What is Upsolver?

• 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 Iceberg, Apache Kafka, data lake, ClickHouse, Confluent Kafka, Elasticsearch, Microsoft SQL Server, MongoDB, MySQL, PostgreSQL, and Snowflake.

Support for Apache Iceberg

• Ingest high-scale data to Iceberg: Upsolver's zero-ETL solution ingests your streaming, database, and file data into Apache Iceberg and includes hands-off table management and optimization so you don't need to worry about administering your tables.
• Save money on storage and increase query performance: if you have an existing lakehouse, use Upsolver's Iceberg Analyzer to discover tables that need compaction. Then run the optimizer for continuous tuning and improved performance through reduced file storage.
• Discover tables for optimization: use the open source Iceberg Table Analyzer CLI tool to find tables that could benefit from optimization and discover the percentage of gain to be achieved across your lakehouse.