Implementing the fastest, most open data lakehouse for Snowflake ETL/ELT

Organizations face a plethora of use cases - GenAI, machine learning, analytics, and more - that require they access their Snowflake data with query engines such as Athena, Databricks, Google BigQuery and Snowflake. While Snowflake is gradually opening its storage with support for Apache Iceberg, an open table format that provides access to many query engines, the future of the project is uncertain. Tabular, the commercial company backing Iceberg, was recently acquired by Databricks.

Join this webinar to learn how Onehouse simplifies the adoption of Iceberg for Snowflake, while supporting popular data lakehouse formats such as Apache Hudi™ and Delta Lake. Onehouse enables you to ingest your data faster, at scale, for a fraction of the cost. With Onehouse support for multiple lakehouse formats, your data will play as nicely with Snowflake as it will with other processing engines such as Databricks.

Join this webinar to learn how companies are:

• Reducing merge expenses in Snowflake by up to 70% by integrating a data lakehouse.
• Achieving up-to-the-minute data freshness while reducing overall data infrastructure costs.
• Querying a single copy of data in the lakehouse with Snowflake for BI, analytics and reporting alongside Databricks for AI/ML, Flink and Spark for data engineering use cases, and more.
• Eliminating data catalog lock-in, the lock-in du jour amongst data management technologies.

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