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Upsolver Creates First-Ever Truly Open Cloud Lakehouse, Releases Native Ingestion Connectors to Redshift and Snowflake

Upsolver’s new release empowers enterprises to break free from the notorious database vendor lock-in 

Upsolver, provider of a cloud-native lakehouse engine, announced the release of native ingestion connectors to Amazon Redshift and Snowflake, creating the first-ever truly open cloud lakehouse. Using Upsolver’s platform, enterprises can now easily switch between data warehouses and data lake query engines, across multiple vendors. Since Ori Rafael and Yoni Eini (CEO and CTO of Upsolver, respectively) founded Upsolver in 2014, the company has set a new standard for cloud-native computing that eliminates the complexity of data pipeline management.

While data warehouses are excellent for business intelligence, they cannot address all modern enterprises’ data processing needs, such as streaming, text search, and machine learning. And although data lakes are a cost-effective way to store vast amounts of data, they are complex to manage and require expensive engineering expertise (on-premise and in the cloud). Upsolver’s cloud lakehouse engine empowers organizations to now achieve the cost and flexibility advantages of a data lake combined with the ease-of-use of a data warehouse. 

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“Solutions like Redshift and Snowflake are amazing for making data valuable, but one database cannot solve all use cases,“ said Rafael. “Organizations should be able to leverage multiple database engines and easily switch between them according to their use case, in-house skills, and cost restrictions. This is the vision of the open cloud lakehouse and Upsolver is the engine that powers it.”

Upsolver initially focused on making cloud object storage easily queryable using engines like Apache Presto, becoming the only formally recommended partner by Amazon Web Services Athena. Now Upsolver has applied the same for Redshift and Snowflake, giving data practitioners a simple, SQL-based tool for turning raw data into queryable data in their favorite database. 

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De-coupling data preparation from querying is crucial to achieving vendor-unlock. As opposed to traditional ETL tools, Upsolver runs fully stateful data preparation in the lake and pours the results into the database. To switch databases, the user simply pours these results into a different target, without changing a single line of code. The Redshift and Snowflake native ingestion connectors allow:

  • The transformation of raw data into tables using a visual SQL-based interface which can be operated by any data practitioner

  • Real-time streaming data ingestion 

  • The meshing of real-time and historical data using high cardinality joins

  • Database table management

  • Upserts

  • Reprocessing of historical data 

Customers who’ve turned to Upsolver include data-intensive enterprises such as Sisense, Cox Automotive, ironSource, and more.

“We chose Upsolver to feed our pipelines to AWS S3 and using Athena and Snowflake since we believe all raw data should be centralized in the lake and query engines should be easily replaceable,” said Guy Boyangu, the CTO of Sisense. “Upsolver’s visual interface made it possible for our data analysts to quickly iterate on large scale raw data instead of investing much more in large data engineering projects.”

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