You can improve efficiency and cut costs by minimizing the amount of data scanned by each query by partitioning or splitting your data. Your data can be divided up using any key. Partitioning data based on time is a typical method that frequently results in a multi-level partitioning system.
For instance, a client with hourly data inflow might choose to split by year, month, day, and hour. Another client might divide their data by a data source identification and date if their data is loaded only once daily but comes from several distinct sources.
The partitioning of data in Amazon S3 is typically done based on the structure of the data and the access patterns of the applications that use the data. Partitioning can be done at several levels, such as by date, time, region, or customer, depending on the needs of the application. By partitioning the data, the data can be stored, managed, and retrieved more efficiently, which can improve the performance and scalability of the application.
Amazon Athena, an interactive query service, uses conventional SQL, to analyze data directly in Amazon Simple Storage System or Amazon S3.
Athena can use partitions, whose data paths contain key value pairs connected by equal signs. For example, country=uk/city=London or year=2023/month=10/day=02/.
Athena can also use partitioning whose data paths are not key value pairs. For example, By Date: S3/bucket/data/2023/10/02/uk/filename.csv
By Product Id: S3/bucket/data/product-id/filename.csv
When a query is conducted against a partitioned table, only the partition that is specified in the WHERE clause is scanned by Athena. Utilizing AWS glue, data partition is possible. AWS Glue crawlers automatically find partitions in your Amazon S3 data.
Although Athena can read up to 1 million partitions at once, it can only query AWS Glue databases with up to 10 million partitions. Therefore, in some cases, partition indexing is beneficial.
What is Partition Projection in S3
Partition projection in Amazon S3 refers to the ability to retrieve only a subset of the data stored in a partition, rather than the entire partition. This feature is designed to optimize data retrieval and reduce the cost of data retrieval by allowing you to retrieve only the data that you need, rather than the entire partition.
You can use partition projection to avoid managing partitions. For heavily partitioned tables whose structure is known in advance, partition projection is an alternative.
When using partition projection, Instead of reading from a metadata repository, partition values and locations are calculated using table properties that you set. The use of partition projection can dramatically improve query performance since in-memory calculations are quicker than distant look-up.
End Notes
In addition to improving data storage and retrieval, partitioning can also help reduce the cost of data storage by allowing data to be stored in lower-cost storage classes, such as Amazon S3 Intelligent-Tiering or Amazon S3 Glacier, based on the age or infrequency of access of the data.
Learn more about Implementation of Amazon S3 Data Storage
