Data Lakes

Turbocharge data lake analytics

Many organizations have or will have in place a cloud-based data lake to ingest and store data from a variety of disparate data sources. But in order to maximize the utility of a data lake, you need to do something with the data and objects stored in it. You want to create real-time dashboards to report on the data, run fast analytics to uncover insights and relationships, interactively explore the data to find new trends, and much more. And, you want to accomplish all this across multiple workgroups and stakeholders that need to access the data concurrently.

Executing these broad set of use cases requires more than the typical unlimited storage capacity and convenient data access capabilities that traditional data lakes excel at offering. You need a highly performant, relational database and data warehouse. Snowflake–the only data warehouse built for the cloud and delivered as a service–provides a complete solution capable of high performance, low-latency relational analytics, self-service capabilities, as well as virtually unlimited multi-workgroup concurrency scaling–all from one environment.

In addition, with Snowflake you have the power and flexibility of both worlds. You can land data such as JSON, XML, and Avro, including Parquet data stores, directly in Snowflake. No external data lake required. Or you can land your data in a cloud blob store like Amazon S3 or Azure Blob Storage and turbocharge the data lake with Snowflake and achieve up to 100X performance compared to running relational queries from the data lake. No ETL effort, pre-transformations or pre-schemas required.