The native integration enables Snowflake users to scale enterprise analytics workflows while reducing data transfer costs

Datameer, makers of the world’s leading enterprise data pipeline and preparation solution Datameer X and virtual data and analytics hub Neebo,  announced its native integration between Neebo and Snowflake. Neebo, Datameer’s newest product line, uses the latest advancements in data virtualization to enable scalable, highly secured, and governed self-service analytics across the enterprise.

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With the integration, data and analytics teams can now:

• Search for and discover data in very complex enterprise data landscapes in a highly governed fashion
• Securely access any structured or unstructured data across the enterprise—whether it’s in Snowflake or any other source on-premises or in the cloud
• Create new datasets from any data source without replicating or moving the data; Neebo keeps all data in place at the source making it the most secured and governed way to deliver data to employees across the enterprise

With this new native integration, Snowflake users can now experience increased access and modeling performance while reducing data movement and, by extension, the costs associated with data storage and transfers over the wire.

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Neebo leverages the Snowflake Connector for Spark for optimal query performance and, through the integration, enables:

• Push down processing. SQL queries and transformation logic are now executed directly in Snowflake instead of within Neebo or other downstream tools that consume the data. In cases of large volumes of data, this drastically accelerates query times while reducing compute and networking costs. In addition, user data uploaded directly to Neebo is intelligently pushed down to Snowflake to optimize join performance where appropriate.
• Caching. Neebo users now have the ability to materialize their virtual datasets in Snowflake on demand—whether or not the data comes from Snowflake originally—to leverage the data warehouse’s blazing-fast query execution speed. This allows users to easily enrich existing Snowflake datasets and perform transformations and joins without any coding or costly ETL processes. These datasets are immediately accessible directly from Snowflake, bringing unmatched performance and optimizing Snowflake compute resources.

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