What is the best way to connect data across multiple sources while supporting large data sets, complex data structures, and real-time needs?
How do Data Fabric and Data Virtualization compare when speeding up data & analytics? What do they have in common, and how do they differ?
Let’s define the two before diving into the differences, pros, and cons.
What is Data Fabric?
Forrester defines data fabric as a platform for “orchestrating disparate data sources intelligently and securely in a self-service and automated manner… to deliver a unified, trusted, and comprehensive real-time view of customer and business data across the enterprise.”
What is Data Virtualization?
Data virtualization is a logical data layer that can integrate enterprise data siloed across disparate systems, manages and unifies data for centralized security and governance, and delivers it to the business users in real-time.
How do they compare?
What are the Pros of Data Virtualization?
What are the Cons of Data Virtualization?
What are the Pros of Data Fabric?
What are the Cons of Data Fabric?
What is a business use case of a Data Fabric?
Let’s say your business is in the beverage industry. You have data from Salesforce, Excel and Oracle. The trick is, data about your corporate accounts live in Salesforce, data about the account managers maintaining relationships with vendors live in Excel and data about supply-chain updates Oracle. Data Fabric connects all three. Not to mention, it models the relationships between each source – all without moving any of the data and running queries across them through Natural Language Processing.
Learn how the Promethium Data Fabric connects 400+ data sources in a single data analytics platform saving go-to-market time and over 91% of integration costs.
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