Snowflake and Qubole bring a new level of integrated product capabilities that make it easier and faster to build and deploy Machine Learning (ML) and Artificial Intelligence (AI) models in Apache Spark using data stored in Snowflake and big data sources.
Through this product integration, Data Engineers can also use Qubole to read Snowflake data, perform advanced data preparation to create refined data sets and write the results to Snowflake, thereby enabling new analytic use cases.
The Snowflake and Qubole integration enables companies to leverage Machine Learning (ML) and data science techniques to derive even greater value from critical information already present in Snowflake.
Companies interested in using ML with Snowflake often face limitations when training ML models with only a subset of data- this leads to inaccurate predictions. Data science teams may spend long hours training models and dealing with infrastructure restrictions that delay the deployment of ML models. Qubole eliminates these limitations by automatically scaling and managing the Spark infrastructure on behalf of the data scientist. With this integration, our joint customers can leverage the complete Snowflake data set to train models while reducing the required training time by more than 50%. As a result, companies are able to maximize the value of their Snowflake data at a much faster pace.
Accelerate ROI for ML, data science, and streaming processing use cases
Reduce the complexity and cost of running Spark.
Reduce manual configuration steps
Secured credential management between Qubole and Snowflake
Free access to Qubole for 30 days to build data pipelines, bring machine learning to production, and analyze any data type from any data source.
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