Semi-Structured Data Processing


Data comes in diverse forms from diverse sources, including an explosion in machine-generated data. Semi-structured data formats with flexible schemas, such as JSON and Avro, have become the norm for sending and storing this data.

Conventional data warehouses were not designed to store and process semi-structured data. They were architected based the assumption of a fixed schema. You either store the data in the data warehouse in a raw form and sacrifice performance, or you transform the data before loading and lose information while adding complexity.

When we architected the Snowflake data warehousing service, we designed it from the start to handle semi-structured data without the trade-offs of current approaches. Snowflake natively understands semi-structured data so that you can load it as is without transformation and without a fixed schema. We transparently interpret and optimize the storage of data so that you get the full performance optimizations of a relational database, without compromising flexibility or performance.

Hear our founders on Snowflake’s support for diverse data, and learn more in our whitepaper.

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