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It is important to note that normalization often overlaps with the data cleaning process, as it helps to ensure consistency in data formats, particularly when dealing with different sources or inconsistent units. DataValidationDatavalidation ensures that the data meets specific criteria before processing.
Shifting left involves moving data processing upstream, closer to the source, enabling broader access to high-quality data through well-defined data products and contracts, thus reducing duplication, enhancing data integrity, and bridging the gap between operational and analytical data domains.
One example of a popular drag-and-drop transformation tool is Alteryx which allows business analysts to transform data by dragging and dropping operators in a canvas. In this sense, dbt may be a more suitable solution to building resilient and modular data pipelines due to its focus on data modeling.
Sure, terabytes or even petabytes of data are involved, but generally it’s not the size of the data but everything surrounding the data–workflows, access permissions, layers of dependencies–that pose data migration risks. When you know you can rely on your data, validating successful migrations is easier.
This commonly introduces: Database or Data Warehouse API/EDI Integrations ETL software Businessintelligence tooling By leveraging off-the-shelf tooling, your company separates disciplines by technology. This proactive approach to datavalidation allows you to minimize risks and get ahead of the issue.
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