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Organizations need to ensure that data use adheres to policies (both organizational and regulatory). In an ideal world, you’d get compliance guidance before and as you use the data. Imagine writing a SQL query or using a BI dashboard with flags & warnings on compliance best practice within your natural workflow. In Summary.
Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? You can define expectations about data quality, track data drift, and monitor changes in data distributions over time.
Efficiently adopt data platforms and new technologies for effective data management. Apply metadata to contextualize existing and new data to make it searchable and discoverable. Perform dataprofiling (the process of examining, analyzing and creating summaries of datasets).
Attach a Common DataModel Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common DataModel (CDM) format by another application. We suggest establishing distinct Dataflows for various source types like on-premises, cloud, SQL Server, and Databricks.
If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the datamodeling stage. How frequently you would require to transfer the data is also of key interest.
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