![]() Avoid using columns with low cardinality or skewed data distribution, as they can lead to an unbalanced distribution of data across partitions. High-cardinality columns with an even distribution of data values are often good choices for partitioning. Partition your tables based on the access patterns and query requirements of your data. Partitioning can significantly improve query performance by reducing the amount of data scanned. They can be used as external tables to meet data residency requirements within a modern data stack as Snowflake recommends, or they can be the foundation of an organization’s data platform, either self-managed or by using a platform such as Tabular.Īs more organizations recognize the value of Iceberg and adopt it for their data management needs, it’s crucial to stay ahead of the curve and master the best practices for harnessing its full potential. Iceberg tables are also incredibly extensible as they support multiple file formats (Parquet, AVRO, ORC) and are compatible with multiple query engines (Snowflake, Trino, Spark, Flink). What makes Iceberg tables so appealing is they can store raw data at scale to support typical data lake use cases, but they also have data lakehouse-like properties as well such as well-organized metadata, ACID transactions, and critical features like time travel. It’s designed to improve upon the performance and usability challenges of older data storage formats such as Apache Hive and Apache Parquet. Initially developed by Netflix and later donated to the Apache Software Foundation, Apache Iceberg is an open-source table format for large-scale distributed data sets. If you don’t know Apache Iceberg, you might find yourself skating on thin ice. ![]()
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