Eckerson Group Report
Data fabric has emerged to provide a unified view of disparate and distributed data and to support any type of workload, from business intelligence to ad-hoc analytics to data science. Data fabric uses metadata and artificial intelligence/machine learning (AI/ML) to automate data management functions such as onboarding new data sources and managing metadata. It also simplifies combining data of different types and from different locations to provide seamless access to enterprise data.
This reports focuses in data fabric including:
- Evolution of Data Architecture
- Definition of Data Fabric
- Potential Benefits and Pitfalls of Data Fabric
- Characteristics of Data Fabric
- Implementation Options
- Data Fabric Evolution
- Data fabric is an architectural approach that uses metadata, machine learning, and automation to provide a unified view of enterprise data regardless of its format or location.
- Data fabric does not replace data warehouses and data lakes but rather encompasses them to support business intelligence, data science, and embedded analytics.
- Data fabric seeks to significantly reduce time to insight: the amount of time it takes to use data to get to an “aha” moment that informs business action.
- Data fabric incorporates AI-driven processes that automate many aspects of data management such as discovery, cataloging, preparation, validation, and monitoring to help data teams keep up with the never-ending demand for data.
- A data fabric is not a single product that you can buy. The options are to buy pre-integrated tools from one vendor or buy best-of-breed components from multiple vendors and integrate them yourself.
- Data fabric is best suited for organizations whose data landscape is growing rapidly, with a variety of data formats stored in multiple locations, and the need to democratize data access to meet many analytical demands.