A semantic layer lets organizations connect data warehouses, data lakes, and data lakehouses to an existing data ecosystem to ensure their continued relevance now and in the future.
Data warehouses, data lakes, and data lakehouses are arguably the most popular methods of integrating data today. They’re older, current, and newer integration approaches—respectively—for running analytics and applications across all enterprise data. Using a semantic layer to connect them to the rest of the data ecosystem in the cloud, on-premises, and at the edge ensures their continued relevance now and in the future.
Why? Data warehouses rely on transformations to integrate schema across sources. Data lakes enable organizations to store all data together in a single repository, regardless of format or structure variation. Data lakehouses combine the low-cost storage of the latter with the data modeling capabilities of the former.
Although each approach has pros and cons for enabling organizations to leverage analytics to solve business problems and create competitive advantage, they’re all based on the same basic data management method of collocating data at the storage layer. Such physical consolidation requires moving data with predominantly batch replication processes that are rigid and oftentimes brittle.
But there’s now a superior approach to traditional data management called data fabric that enables organizations to avoid endlessly replicating data for integrations. A data fabric allows organizations to integrate data at the computational layer, instead of the physical storage layer, with a semantic graph layer connecting, rather than collocating, all sources. Data never moves unless it is required for business processes.
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