When properly implemented, data fabrics are a key technology area in artificial intelligence (AI), an ideal means of preparing data for machine learning, and an irreplaceable component in modern data integration methods that deliver better, faster and less expensive analytics than almost any other approach. The closer we can bring the common-sense business terminology and questions as the underlying fabric of data in an IT ecosystem, the more agility the enterprise builds to establish trust with highly connected and context-rich data to assist customers along every step of their journey.
Data fabric is an umbrella term for multiple data management capabilities that effectively stitch together all data for unified access, regardless of its physical format or location. These logical fabrics include tools for everything from data cataloging and data quality to data governance and more. Semantic knowledge graphs are the foundation and connective tissue of the data fabric framework on which contemporary data integration is based.
With them, data fabrics provide highly contextualized relationship understanding between individual nodes and entire datasets to enable more nuanced and relevant analytics results than other approaches involving graph databases and relational techniques. In fact, the analytical might of data fabrics with semantic knowledge graphs is incomparable to that of either labeled property graph databases or data warehouses supported by relational technologies. Labeled property graphs, which were primarily designed for storage, often leave the challenge of competing definitions as an exercise for the developer.
On the other hand, relational data warehouses were designed for structured data and are difficult to use and reuse with the exorbitant amounts of structured and unstructured data deluging the enterprise. Data modeling is a time-consuming chore with these repositories, which frequently has to be redone each time new sources are added or business requirements change, leaving a scattered and growing set of data marts and custom repositories littering the data landscape.
Data fabrics, however, excel in several areas to provide heightened analytics prowess. They support complex discovery style analytics, intelligent inferences for machine readable data, and data virtualization capabilities that eliminate silos and reduce data transformation costs so organizations can connect and query data throughout their ecosystems. Subsequently, there are numerous real-world, compelling use cases where top firms abandoned property graph and relational databases for the advanced analytics might of data fabrics, proving they’re the future for cheaper, better, faster analytics.
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