pear_count AS count FROM example_data WHERE count IS NOT NULL UNION ALL SELECT shop_id, 'lemon' AS item_name, inventory. orange_count AS count FROM example_data WHERE count IS NOT NULL UNION ALL SELECT shop_id, 'pear' AS item_name, inventory. apple_count AS count FROM example_data WHERE count IS NOT NULL UNION ALL SELECT shop_id, 'orange' AS item_name, inventory. > SELECT * FROM ( SELECT shop_id, 'apple' AS item_name, inventory. The queries would also work with a non-temporary table.) (For this post, we will use a temporary table, but
This structured data by parsing JSON into the SUPER column type using The shop’s source systems store the inventory as JSON objects. Several shops, where each shop has an inventory of arbitrary items assume that In this post we’ll demonstrate UNPIVOT and how it enhances Redshift’s ELTĬonsider an imaginary inventory tracking system that tracks the inventory of Structured data with the new UNPIVOT keyword to destructure JSON
Recently, AWS have improved their support for transforming such Which allows the storage of structured (JSON) data directly in Redshift Need for a separate transformation tool, reducing effort and cost to make dataĪn example of Redshift’s support for ELT is the SUPER column type, ELT is beneficial because it often removes the Redshift, and then use Redshift’s compute power to perform any transformations. Redshift Spectrum can now directly query scalar JSON & Ion data types stored in Amazon S3, without loading or transforming the data. Steps, and instead load raw data extracted from a source system directly into Loading the transformed data into the warehouse.Ī common theme when using Redshift is to flip the order of the Transform and Load Representation suitable for use in a (relational) data warehouse and then In short, ETL is the process ofĮxtracting data from a source system/database, transforming it into a A common process when using a data warehouse isĮxtract, Transform, Load (ETL). AWS Redshift is Amazon’s managed data warehouse service, which we