Queries in Snowflake: Getting Started with Performance Optimizations

Snowflake    |    Intermediate
  • 16 videos | 1h 51m 33s
  • Includes Assessment
  • Earns a Badge
Rating 4.6 of 51 users Rating 4.6 of 51 users (51)
Partitioning data based on a column is a common technique for performance query optimization in many database technologies. Snowflake is a highly-performant, big-data technology that uses its own form of data partitioning called micro-partitioning that stores data in columnar format. In this course, discover the advantages of Snowflake micro-partitions over regular, static partitioning. Next, examine how caching works in Snowflake. Finally, learn how to perform the clustering of Snowflake tables and how clustering helps the performance of filters, point-lookups, and range queries on the clustering key columns. Upon completion, you'll be able to leverage micro-partitioning in Snowflake, differentiate between retrieval optimization caching and local disk caching, and implement clustering with the correct clustering key.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline micro-partitioning in snowflake
    Recognize query results and local disk caching
    Load data into tables
    Use caching for retrieval optimization in snowflake
    Disable caching in snowflake
    Query data using the local disk cache
    Recall how clustering works in snowflake
  • Recall how the overlap depth of micro-partitions is computed
    Outline the concepts of clustering in snowflake
    Choose clustering keys and view the resources saved by clustering on different columns
    Use clustering and recall its advantages
    Run queries on clustered date to query performance
    Suspend clustering on a table
    Cluster with multiple columns and evaluate the resulting performance
    Summarize the key concepts covered in this course

IN THIS COURSE

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