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The ability to scale virtual warehouses and easily handle terabytes of data make Snowflake Analytics the right solution for data science. This, combined with deep integration with machine learning and a long list of data science partners, makes it easier to deliver machine learning solutions.
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Using Snowflake Analytics is Important From a Technological Perspective
Snowflake has clearly been the leader in providing Cloud Data Warehouse platforms, and in 2009 Gartner recognized Snowflake as the leader in this field for the third year in a row. Very low maintenance management and near-real-time ability to import, transform and query data make it a formidable solution.
While the Technology failure of Hadoop gave the Data Lake concept a bad name, cloud-based data lake solutions were clearly the winners. The combination of unlimited computing power and unlimited, inexpensive data storage with Snowflakes' unique ability to query semi-structured data using SQL makes it the ideal foundation for a data lake.
Snowflake supports a wide range of data processing and integration tools, and combined with the ability to deploy virtual warehouses in milliseconds and scale to compute instantly, is a very attractive solution for data engineering weather type ELT. With a few performance tuning options, it's a remarkably low-maintenance platform that's fully charged as you go.
The ability to scale virtual warehouses and easily handle terabytes of data make Snowflake Analytics the right solution for data science. This, combined with deep integration with machine learning and a long list of data science partners, makes it easier to deliver machine learning solutions.
Data exchange refers to the ability to share and exchange data with affiliates, partners or third parties. Snowflake Data Marketplace provides direct access to query-ready data with just a few clicks.
Flexibility, scalability, and dynamic elasticity of Snowflake
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