This leads organizations inevitably to compare data lakes to Snowflake and other cloud data warehouses. Businesses adopted Snowflake either as part of a migration from on-premises enterprise data warehouses (such as Teradata) or as a more elastically-scalable and easier-to-manage alternative to an existing cloud data warehouse (such as Amazon Redshift or Google BigQuery).īecause of the sizable investment Snowflake often represents, some data teams consider it for every use case. Over the past five years, Snowflake has grown from a virtual unknown to a vendor with thousands of customers. Let’s look, simply and clearly, at where Snowflake best fits. Use Snowflake WITH a data lake? Use Snowflake AS a data lake? Use Snowflake INSTEAD OF a data lake? It’s easy to feel a bit adrift as to whether and how to plug Snowflake into your cloud data stack. Trying to understand where Snowflake fits into your cloud infrastructure? Check out our 2022 benchmark report to learn how to best design your data pipeline in order to get the best latency and price/performance from your cloud data warehouse. How Best to Build a Real-Time Streaming Architecture Using Snowflake, Data Lake Storage, and Upsolver on AWS. Making your data lake analytics-ready with Upsolver.Snowflake + Optimized Cloud Data Lake = Flexible, Affordable Analytics.Where to put your data is often a question of cost.
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