From the course: Advanced Snowflake: Deep Dive Cloud Data Warehousing and Analytics

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Table structures in Snowflake

Table structures in Snowflake - Snowflake Tutorial

From the course: Advanced Snowflake: Deep Dive Cloud Data Warehousing and Analytics

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Table structures in Snowflake

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In order for Snowflake to be able to efficiently process large amounts of data, the design of their table structures is quite critical. If you had a huge amount of data in petabytes and you stored this within a table, in a single contiguous blog, queries run on such tables will likely to be very, very inefficient. If you just have a single blob of contiguous data, your queries will run slowly because they need to process this huge amount of data and queries will be unable to run in parallel on a contiguous block. Almost every big data processing platform partitions the data that it stores in tables. Now, if this partitioning is static, well, that helps with query parallelization, but static partitioning has its own limitations. Your data may be skewed, certain partitions may be very large, other partitions may be small, resulting in slow query performance. In Snowflake, your tables are divided into micro-partitions. These micro-partitions are extremely small partitions automatically…

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