![]() ![]() ![]() This can improve report performance, enable the addition of business logic to calculate measures, and make it easier for report developers to understand the data. Schema layer: These are the destination tables, which contain all the data in its final form after cleansing, enrichment, and transformation.Īggregating layer: In some cases, it's beneficial to aggregate data to a daily or store level from the full dataset. These tables hold the final form of the data for the incremental part of the ETL cycle in progress. Staging layer: Once the raw data from the mirror tables transform, all transformations wind up in staging tables. The process copies and adds source data to the target mirror tables, which then hold historical raw data that is ready to be transformed. Mirror/Raw layer: This layer is a copy of the source files or tables, with no logic or enrichment. When an ETL process is used to move data into a data warehouse, a separate layer represents each phase: Related Reading: ETL vs ELT Implementing ETL in a Data Warehouse Another common target system is the data lake, a repository used to store "unrefined" data that you have not yet cleaned, structured, and transformed. Google BigQuery and Amazon Redshift are just two of the most popular cloud data warehousing solutions, although you can also host your data warehouse on-premises. The most common target database is a data warehouse, a centralized repository designed to work with BI and analytics systems. Loadįinally, once the process has transformed, sorted, cleaned, validated, and prepared the data, you need to load it into data storage somewhere. There are many types of data transformations that you can execute, from data cleansing and aggregation to filtering and validation.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |