Zepl Integrates Snowflake 2.6 Spark Connector to Accelerate and Simplify Data Science at Scale
Integration highlights strengthening of partnership and growing customer momentum
Zepl, the data science platform built for your cloud data warehouse, announced that it has deepened its technical integration with Snowflake by utilizing Snowflake’s 2.6 Spark Connector in Zepl’s SaaS product. This new integration dramatically improves performance when accessing data for analysis and minimizes costs.
According to Snowflake’s recent benchmark tests, Snowflake’s 2.6 Spark Connection caused an immediate 4x improvement in the end-to-end performance of Spark jobs due to a 10x performance improvement in the time spent by the Spark Connector to fetch and process the results of the Snowflake query. In addition, Snowflake has a query-result cache for repeated queries that operate on unchanged data. With cached reads, the end-to-end performance for the Spark job described above is 14x faster than when using uncached CSV-format reads in previous versions of the Spark Connector.
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Zepl’s native Snowflake integration lets data scientists do real-time analysis on their live Snowflake data. Getting started with Zepl is easy. In less than a minute, Snowflake users can connect to Zepl through Partner Connect and immediately begin building machine learning models and analyzing results on massive quantities of data.
Snowflake simplifies the most time consuming and tedious part of data science – wrangling all of the data in a single high-performance data platform – so that the work of modeling and analysis can begin in Zepl. The results are easily fed back into Snowflake where it remains easily accessible to both technical and non-technical users alike.
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