You Can’t Buy a Data Fabric!

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By Rick F. van der Lans, Founder of R20/Consultancy BV, Ambassador of Axians Business Analytics Laren

Rick F. van der Lans

You can buy an ETL tool, a reporting tool, and a database server, but generally you can’t buy a data fabric. When your company needs a data fabric, you must design and develop it yourself. It’s just like you can’t buy a data warehouse environment or microservices architecture. These need to be designed and implemented and requires the use of many different tools. The same applies to data fabrics.

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But what is a data fabric? Conceptually, it’s a layer of software that allows any type of data consumer to access data available in one of the many IT systems. In other words, it’s about data abstraction, it’s about making all the enterprise (and possibly external) data available to all the data consumers, including simple reports, advanced dashboards, apps running on mobile devices, data science tools, real-time applications, and transaction processing applications.

Gartner defines a data fabric as follows: “A data fabric enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage.”

The key terms here are frictionless data access, sharing of data, and a single and consistent data management framework.

Frictionless data access means that all the data can be accessed without difficulties, regardless of where and how it is stored. Whether it’s stored in a data mart, a transaction database, hidden deeply in a packaged application, or in a simple flat file, all this data must be accessible for those data consumers who need it. Frictionless data access matches the need to democratize data, making the data you own easily available to the entire organization.

Sharing of data refers to making data available to many data consumers and for a wide range of use cases. Sharing refers to data consumers who share the same data and the same metadata describing that data. This does not apply to some popular data architectures. For example, data lakes are primarily used by data scientists and data warehouses only by BI users.

A single and consistent data management framework indicates that the data fabric manages all the data and metadata and delivers a consistent view of the data to all the data consumers.

Technically, a data fabric provides some service interface layer that can be used to retrieve, analyze, insert, update, and delete data. This layer hides the different technologies used by the systems that contain the data, it hides the language or API that is used and the location of the data. If data from different source systems must be integrated, a service will be available that will present the integrated view. The layer is also responsible for data security and data privacy aspects and provides data consumers with descriptive metadata. As with data, metadata should also be shared by all the data consumers.

A data fabric can be developed in many ways. For example, companies can use a low-level programming language to develop a large number of services with JSON/REST interfaces to accesses all the data. These services may communicate with applications and with each other through some messaging technology. This is a feasible approach but leads to a costly and time-consuming software development exercise, as all the aspects need to be covered, including metadata access, data security, data integration, data cleansing, and so on.

Another approach is to copy all the relevant data from all the source systems to one big data store, a so-called data hub or data lake. A service interface offers access to this data store. Developing a service interface on one central data store is easier than the previous approach, but it will still be an enormous development effort. A drawback of such an approach is that the services can’t provide real-time data.

The third approach to develop a data fabric is by utilizing a data virtualization platform. The main advantage of this approach is that data virtualization platforms support almost all the required features needed to develop a data fabric. They are built to deliver a data abstraction layer on top of a heterogeneous set of source systems. It can integrate data from source systems without storing data redundantly, metadata is automatically kept and made accessible to data consumers, it supports multiple interfaces and languages, including SQL and JSON/REST, centralized data security and protection features, and it can use the full power of underlying database servers by using query pushdown. As a supporting technology, data virtualization is a perfect match for the data fabric concept.

Some refer to data fabrics developed with data virtualization with the term logical data fabric. Which would be inline with the term logical data warehouse.

As indicated, you generally can’t buy a data fabric, you need to design and develop one that fits your organization. If you do, make sure to select an approach that provides all the features you require and offers you high productivity and easy maintenance, as that will ultimately determine whether the data fabric dream becomes a reality.

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