One development environment
Provides an intuitive, role-based GUI for managing process with drag-and-drop functionality, source system access and a customizable metadata tree.
No matter where your data is stored – from cloud, to legacy systems, to data lakes, like Hadoop – SAS Data Management helps you access the data you need. Create data management rules once and reuse them, giving you a standard, repeatable method for improving and integrating data – without additional cost.
As an IT expert, it's easy to get entangled in tasks outside your normal duties. SAS Data Management enables your business users to update data, tweak processes and analyze results themselves, freeing you up for other projects. Plus, a built-in business glossary as well as SAS and third-party metadata management and lineage visualization capabilities keep everyone on the same page.
SAS Data Management technology is truly integrated, which means you’re not forced to work with a solution that’s been cobbled together. All our components – from data quality to data federation technology – are part of the same architecture. And we’re experts at making sure data is prepared for visualization, analytics or operational use. We promote data quality, transparency and accountability. Built-in auditing tools monitor processing. And source data lineage enables you to trace data usage across the organization.
SAS Data Management gives you the perfect balance of choice and control, enabling you to run in a variety of compute environments with virtually any data – from new computer tiers (Spark, MapReduce, Presto), files systems (S3, Parquet, Avro, Orc) and databases (MongoDB, RedShift, Cassandra) to existing database systems (Teradata, Oracle, SAP). Deploy your ETL flows to run in multiple computing frameworks. Reuse ETL and data quality processes to ensure consistent, trusted data and prevent new silos from developing. Combine batch data with data in motion to glean new insights for calls to action, with real-time alerts and notifications.
Store and manage technical, business, process and administrative metadata to facilitate reuse of existing table definitions, business rules and more. Mapping technologies make it easy to propagate column definitions from sources to targets and create automated, intelligent table joins across both SAS and third-party data integration and data modeling tools.
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