Introducing Data Fabric

Data Fabric refers to an integrated data management architecture and set of features that conveniently connect data endpoints. It provides consistent functionality that enables end-to-end data management capabilities. Let’s look at the data fabric architecture.

What is a data structure?

Data assets are generated in silos and hidden in a hybrid mix of infrastructure environments. Data preparation cycles are long, and users need a wide range of data management capabilities to overcome the limitations faced by complex multi-vendor, multi-cloud, and evolving data environments.

The Data Fabric architecture is designed specifically to address the challenges facing the complex hybrid data landscape. Essentially, data fabric can be described as a converged platform supporting the diverse data management needs to deliver the right IT service levels across all disparate data sources and infrastructure types. It operates as a consolidated framework to manage, move, and protect data across multiple isolated and incompatible data center deployments.

As a result, organizations can invest in infrastructure solutions that align with their business requirements—without concerns surrounding data service levels, access, and security.

7 Key Components of Data Fabric Solutions

Since it is relatively new, there are only a handful of solutions that can be called true data fabric technology. Here are the components to look for when choosing a solution:

Network-based design with universal controls instead of data copy

The Data Fabric should be designed as a network. This forms the basis for everything else the data fabric can provide. In addition, data structures need to leverage this network structure to provide universal access control to their data. If you are familiar with setting permissions in the cloud-based productivity suite, you should understand the basic assumptions here. Instead of sharing a copy of the data, set permissions for users to access a single source. The data structure should allow these permissions to be controlled at the data level. You can set data permissions once, not per app. These controls are embedded at the data level, so they are ubiquitous where the data is displayed. For example, you can give your marketing team permission to view a customer’s email address. If you set this permission once, the marketing team can view the customer’s email address each time it appears as a record. This saves time in managing data permissions. This allows you to further reduce the time and cost of building new technologies while preparing for meaningful data ownership and privacy.

 Autonomous data capacity

Previously, data was always associated with the application that created it. This is the underlying problem behind the current reliance on data copying and costly integration projects. Data Fabric provides the ability to separate data from your application and create autonomous data. The data is independent and can be accessed by multiple applications without the need for point-to-point integration. This autonomous data has many uses and provides a very efficient way to design new solutions. Imagine how to use the API to reuse your code in a new application. The data fabric should be able to reuse data in a similar way. New technologies can leverage data that already exists on the fabric, so solutions built for X can easily be extended to Y without rebuilding key components. Autonomous Data also provides the ability to add new functionality to legacy systems. These projects have been very frustrating in the past. When working with data structures, adding new features to existing applications makes it much easier to “teach old dogs new tricks.” The data fabric marks the end of the familiar (but very inefficient) purchase/ build/integration paradigm. Building a solution on the data fabric cuts build time in half by eliminating the need to perform point-to-point integrations.

Existence of plasticity

Plasticity is the ability to reconstruct and reorganize existing information more efficiently. This allows the brain to process more data than any other company in the world. The brain constantly optimizes and connects what you are learning more efficiently. In fact, studies have shown that high IQ scores correlate with fewer such connections. Currently, point-to-point integration means that the data architecture has the maximum number of connections possible. As a result, the IQ score is very low. Data flexibility means that you can coordinate these connections to create real intelligence for your business. So far, this has never been replicated to machine data in a meaningful way. For enterprises, plasticity removes the barriers that limit schema development. Developers can build integrations through data contracts (that is, models) to prevent integrations from breaking as the data fabric schema evolves over time. This allows you to change the data schema without breaking internal or external dependencies, such as relationships with other tables, APIs, queries.  By allowing the evolution of the schema, the data model is free to evolve. This is the same as the human brain continuously adapting as it absorbs new information.

Meaningful ownership of the data

Meaningful data ownership is important to protect privacy and corporate security and is a fundamental step in entering the ultra-intensive data future of AI/ML, IoT, and other new technologies. As such, legislators have recently been pushing to create and enforce data ownership regulations. But every integration project means a new copy of the data, and today’s organizations need to manage thousands of copies of the data. There is really no such thing as “data ownership” because there are so many copies of the data. Therefore, all attempts to manage data, including the GDPR and other similar legislation, are at issue until data copying is curtailed and data ownership becomes true. Data Fabric must provide an ideal platform for establishing and enforcing meaningful ownership of data.

Active metadata

Metadata is data about data and is the key to unleashing most of the magic of data structures. Traditional metadata is dormant, which greatly limits its usefulness. The data structure activates this metadata. That is, it is updated in real time and can be queried, analyzed, and otherwise manipulated like traditional data. This is where the true power of data structures resides. With metadata enabled, you can perform universal data operations and streamline the entire end-to-end process of data management and data and structure changes. An important part of the data structure is this valid metadata that enables standardized governance and universal data APIs. Also, the active metadata is updated in real time, so you can modify the data acquisition event to connect both upstream and downstream sources to the fabric. In short, it is this valid metadata that enables a key component of the plasticity of the data fabric architecture. Overall, active metadata facilitates data management in an intuitive way. This is the essence of data fabric technology.

Metadata-driven experience

True data structures should be able to replace traditional applications with a completely metadata-based experience. For end users, these experiences are indistinguishable from APIs and apps, but they are as easy to build as working with spreadsheet data. A mature metadata-driven experience requires a mature data structure with a robust set of connected data sources, which will be a technology of the future. But the foundation of these experiences needs to lie in current technology, which itself calls data structures. That is, the ability to use active metadata in a way that replaces the need for coding in the traditional sense. These metadata-driven experiences helps rebuild how future solutions are built, empowers data owners, and enables business users to build custom data solutions without the need for IT resources. The benefits of this range from faster construction times to easy-to-customize solutions. Imagine allowing team members to create their own custom solutions for working with data without any technical skills other than being familiar with spreadsheets and SQL. This is exactly what these metadata-driven solutions promise.

Network effect capacity

Perhaps the most promising advantage of a real data structure is the capacity of the network effect. This is a phenomenon in which the network becomes more efficient and effective as more nodes are connected. For example, the first phone was almost meaningless until the invention of the second phone, but it got better as more and more phones were networked. Data Fabric provides the same results for enterprise data. The more data that already exists on the fabric, the easier it will be to use in new solutions. The more you use the actual data structure, the more efficient it will be.

Why use data fabric software?

Data Fabric software offers several benefits.

  • It makes build times significantly faster, powering digital transformation efforts.
  • It allows for low-code and no-code solutions, giving data owners and other business users the ability to solve problems without taking up valuable IT resources (if someone can work with spreadsheets or SQL, they can create APIs via a data fabric).
  • Data Fabric eliminates data copying, forming the foundation for meaningful data ownership. This helps future-proof solutions ahead of new data privacy laws, which are being introduced regularly.
  • Data Fabric brings the combined efficiency of network effects to your data. The more you manipulate the data structure, the more effective and efficient it becomes. This gives early adopters of the data fabric a significant competitive advantage.

Entry cost for the data fabric is low. There is no downtime when deploying new fabrics. Simply select an existing project and use the new data fabric to build your solution. It coexists with legacy systems and grows organically as it is used in future projects.

Conclusion

Data Fabric technology is often compared to data virtualization technology, both of which provide innovative ways to process enterprise data. However, there is an important difference between the two. Data virtualization simulates changes, but data structures provide the actual changes to the physical structure of the data. This is the difference between wearing VR goggles and taking a virtual tour of the Grand Canyon and actually being there. Data Fabric is very realistic. Large companies in global finance and other data-intensive industries are already relying on this fact to revolutionize the way they interact with data. And their first reaction is very positive. Data Fabric allows these companies to build solutions faster than ever while eliminating copies of data, protecting privacy and creating meaningful data ownership.

Data Fabric is a promising new technology that has the potential to end the bi-build integration paradigm that has dominated enterprise IT for over 40 years. However, because data fabric technology is so new, it is important to understand the key components and features that make up a true data fabric platform.