…as Stardog CEO Kendall Clark termed it, “we’re in a corner; the only way out is alternatives to the physical consolidation of data.”
Stardog founder and CEO Kendall Clark has been quoted in various articles predicting trends in data management, data science and analytics.
The modern data analytics stack is undergoing shocks as the requirements imposed by a hybrid multicloud world upon data analytics become more apparent. What’s ahead in 2022?
Graph models help provide capabilities like improved feature engineering, root cause analysis, and graph analytics. This functionality is key to helping knowledge graphs transition to the dominant data management construct as data management and AI converge.
Data fabrics are the cornerstone of modern analytics architecture, especially when implemented with data virtualization, knowledge graphs, and expressive data models.
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 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.
