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Snowflake Micro Partitions - Snowflake Training

Snowflake is a cloud-based data warehousing platform known for its architecture that separates storage and compute resources, providing scalability, elasticity, and ease of use. However, I can provide you with a general understanding of Snowflake micro partitions.

In Snowflake, data is stored in micro partitions, which are small, compressed, and immutable units of data within a table. Micro partitions are a fundamental concept in Snowflake's architecture and play a crucial role in its performance and efficiency.

Here are some key points about Snowflake micro partitions:

Immutable Units: Snowflake stores data in micro partitions, which are append-only structures. This means that once data is written to a micro partition, it cannot be modified. Instead, any updates or modifications to the data result in the creation of new micro partitions.

Columnar Storage: Snowflake stores data in a columnar format, which provides several benefits like compression and efficient data retrieval. Each column within a micro partition is compressed separately, allowing for high compression ratios and minimizing storage requirements.

Metadata and Statistics: Snowflake maintains metadata and statistics for each micro partition. This information helps the query optimizer make intelligent decisions about data placement, data skipping, and pruning during query execution, improving performance.

Predicate Pushdown: Snowflake leverages predicate pushdown to optimize query execution. It examines the query predicates and skips scanning micro partitions that do not satisfy the conditions, reducing the amount of data that needs to be processed.

Micro Partition Pruning: Snowflake employs micro partition pruning to further improve query performance. It leverages the metadata and statistics associated with micro partitions to determine which ones to include or exclude during query execution, minimizing data scanning.

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