![]() lemon_count AS count FROM example_data WHERE count IS NOT NULL ) d ORDER BY shop_id, item_name + -+-+-+ | shop_id | item_name | count | | -+-+-| | 10 | apple | 2 | | 10 | orange | 6 | | 20 | pear | 10 | | 30 | apple | 3 | | 30 | lemon | 5 | + -+-+-+īut that does not seem easy to read, or maintain, due to the level of duplication. 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 If you do this on a regular basis, you can use TRUNCATE and INSERT INTO to reload the table in future. Use a CREATE TABLE AS command to extract (ETL) the data from the new Redshift table into your desired table. Which allows the storage of structured (JSON) data directly in Redshift The preferred method would be: Use the Amazon Redshift COPY command to load the data into a Redshift table. 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. 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 ![]()
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