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For any data user in an enterprise today, dataprofiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of dataprofiling, top use cases, and share important techniques and best practices for dataprofiling today.
Prime examples of this in the data catalog include: Trust Flags — Allow the data community to endorse, warn, and deprecate data to signal whether data can or can’t be used. DataProfiling — Statistics such as min, max, mean, and null can be applied to certain columns to understand its shape.
Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. We’ll dig into this definition in a bit. Design concept.
In the rapidly evolving landscape of dataengineering, Snowflake Data Cloud has emerged as a leading cloud-based data warehousing solution, providing powerful capabilities for storing, processing, and analyzing vast amounts of data. Include tasks to ensure data integrity, accuracy, and consistency.
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