What Is Data Fabric? Definition, Architecture, and Best Practices

Data fabric is defined as an emerging approach to handling data using a network-based architecture instead of point-to-point connections. This enables an integrated data layer (fabric) right from the data sources level to analytics, insight generation, orchestration, and application. This article explains data fabric, its key components, and best practices in detail.

What Is Data Fabric?
Key Architectural Components of Data Fabric
Top 8 Best Practices for Implementing and Managing Data Fabric for Enterprises

What Is Data Fabric??

Data fabric is an emerging approach to handling data using a network-based architecture instead of point-to-point connections. This enables an integrated data layer (fabric) right from the data sources level to analytics, insight generation, orchestration, and application. It places a layer of abstraction over underlying data components to make information and insights available to business users without duplication or mandatory data science efforts.

As enterprise data needs evolve, companies are grappling to come to terms with its complexities, heterogeneous nature, and the fact that it resides in multiple applications and environments scattered across the enterprise landscape. According to Statista, global data generation and consumption volumes will cross 149 zettabytes by 2024, and unstructured data will comprise around 80% of this.

Data fabric is seen as an answer to this problem. It improves upon the older concepts of data warehouse and data lake to introduce an architecture that enables uniform data utilization across the enterprise. For this reason, Gartner identified data fabric as one of the top 10 most influential data and analytics technologies in 2019 and states that by 2022, companies will be forced to redesign their infrastructure to support bespoke data fabric designs.

Applications of data fabric

Let us explore the key functionalities and enterprise applications of data fabric to understand how it works.

Data fabrics support unstructured data, including IoT: Enterprises are rapidly expanding their perimeters beyond on-premise servers and fixed workstations. From bring-your-own-device (BYOD) and WFH to ruggedized handheld devices on the field and the Internet of Things (IoT), the scope of networked devices is growing. A data fabric connects with all these endpoints, processes unstructured data collected via sensors, and delivers insights with minimal back-end complexities.
Data fabrics handle information at scale: Enterprise data volumes are constantly growing, and organizations that can mobilize their data effectively stand to gain a competitive edge. Data-driven insights and decisions can power new business opportunities, improve customer experiences, and enable more efficient ways of working. Data fabric makes it possible to automatically ingest and leverage data that would be otherwise sitting idle.
Data fabrics are compatible with hybrid hosting environments: One of the key traits of data fabric is that it is environment-, platform-, and tool-agnostic. It can enable bidirectional integration with nearly every component in the technology stack to create an interwoven or fabric-like architecture. This is well suited to a multi-cloud or hybrid cloud enterprise, where data initiatives need to run uniformly and consistently across all clouds. The solution ingests data from multiple sources spread across environments to create a consolidated â€œfabricâ€ for insights generation.
Data fabrics generate insights at an accelerated pace: These solutions can handle even the most complex datasets with ease, accelerating time to insight. Due to its architecture, there are pre-built analytics models and cognitive algorithms to process data at scale and speed. For example, NASA was able to work with a data fabric provider called Stardog to reduce time to insight by 90%.
Data fabrics require less IT intervention than traditional warehousing models: An important trait of data fabric is that it relies on a set of prebuilt and preconfigured components to go from raw data to processed and actionable information. These systems are typically hosted on the cloud and are managed by an experienced service provider. This means that it doesn’t require IT involvement when implementing and maintaining data production initiatives.
Data fabrics are used by both technical and non-technical users: The architecture of a data fabric makes it malleable to a wide range of user interfaces. You can build sleek dashboards that can be quickly understood and leveraged by business executives. Data fabrics also come with sophisticated tools that enable drill-down and deep data exploration by data scientists. It is suitable for various levels of data literacy.

The main purpose of implementing a data fabric is to consolidate data governance and data security, no matter where it resides in the enterprise. You can also integrate the solution with new data sources, analytical models, user interfaces, and automation scripts to improve data use. Recent advancements in data fabric technology mean that you can even process metadata using graph models to become relevant for business users instead of being just passive assets. Its architecture allows enterprises to add new features through extensions, superimpose a security overlay, and perform other key functions without having to retrench the core database.

See More: What Is Data Governance? Definition, Importance, and Best Practices

Key Architectural Components of Data Fabric

Data fabric is a packaged solution that utilizes seven key components to extract insights from data and deliver them consistently across the enterprise. These key architectural components include:

Key Architectural Components of Data Fabric

1. Data sources for ingestion

Data sources are the system’s generating information that will be processed, stored, and utilized by the data fabric. These sources may exist within the enterprise, such as your enterprise resource planning (ERP) software, customer relationship management (CRM) software, or human resource information systems (HRIS). You may connect to unstructured data sources like document submission systems that support PDFs and screenshots, as well as IoT sensors. The data fabric can also ingest data from external systems that provide publicly available data like social media. Finally, enterprises can purchase third-party data repositories to enrich the information already available in-house.

2. Analytics and knowledge graphs for processing

A lot of data ingested by the data fabric is in semi-structured or unstructured form, including metadata from various sources. Analytics and knowledge graph systems will transform all data types consistently into a coherent format so that they can be processed without any bottlenecks. Specifically, users need to be able to view and understand relationships between the various data sources in the enterprise. That’s why processing analytics is a key architectural component of data fabric before you can go on to generate insights.

3. Advanced algorithms for insight generation

For this component, you can leverage AI/ML algorithms for continuous data monitoring and real-time insight generation. The use of AI/ML significantly cuts down the processing time and helps you generate insights faster. The data has to be aligned with operational use cases like workforce optimization or location-specific business decision-making to surface the most relevant insights. Also, all activity has to be logged for security and compliance purposes.

4. APIs and SDKs for connectivity with delivery interfaces

This is possibly the most important component of data fabric, which sets it apart from traditional data lakes or warehouses. Data fabric has integration-readiness built into its architectural backbone and can connect with any front-end user UI to deliver insights where they are most necessary. It uses application programming interfaces (APIs) and software development kits (SDKs) for this purpose, along with pre-built connectors. Ideally, it should have two integration modules â€“ a do-it-yourself (DIY) feature that IT professionals can use to set up complex integrations and an out-of-the-box capability for business users to start gaining from the data fabric through self-service business intelligence (BI) tools.

5. Data consumption layer

The data consumption layer refers to the user-facing interface that enables data consumption at the front-end. There are several ways you can tweak this layer to get maximum returns from your data fabric investment. For instance, embedded analytics inside of business apps could help users access information in context to their workflows. Virtual assistants and chatbots can help in natural data exploration. And, real-time dashboards can keep operations managers abreast of key enterprise events in real-time. The advantage of data fabric is that it supports all of these requirements with equal ease.

6. Data transport layer

The transport layer is what helps data move across the fabric. A robust data transport layer would not only be able to move data between systems without disruptions, but it would also be able to enforce stringent security through end-to-end encryption. This layer can also be designed to preserve deduplication so that new copies aren’t created during movement. It should also maintain the compression efficiency enforced by different components of the data fabric so that data rehydration does not take place in motion to cause inadvertent inefficiencies or security risks.

7. The hosting environment

While this component is technically external to the data fabric architecture, it influences its core component. You may choose to host the data fabric on-premise or on the cloud. In the case of the latter, it might be able to gain from cloud-based data management tools such as Snowflake and containers. On-premise data fabrics should integrate with your non-cloud IT tools, be it Oracle on-premise, SAP on-premise, or anything else. Data fabric is also well suited to multi-cloud and hybrid cloud environments, provided you partner with the appropriate vendor.

While we live in a data-driven age, organizations spend a disproportionate amount of time on routine tasks and not enough on value addition. A 2020 survey by Gartner titled Data Management Struggles to Balance Innovation and Control found that data teams can devote only 22% of their time to innovation. The remaining efforts are spent on maintaining production initiatives, training users, and other non-value-adding tasks. Data fabric rectifies this balance using the above seven components and frees up your top talent by removing back-end bottlenecks in data management.

See More: What Is Enterprise Data Management (EDM)? Definition, Importance, and Best Practices

Top 8 Best Practices for Implementing and Managing Data Fabric for Enterprises

From $1.1 billion in 2020, the global data fabric market will grow by over 3X times to reach $3.7 billion by 2026 (as per Global Industry Analysts) â€“ indicating strong demand in this space. If you are looking to implement a data fabric architecture to optimize how your enterprise data is utilized, here are some best practices to remember.

Data Fabric Management Best Practices

1. Embrace a DataOps process model

While data fabric and data ops aren’t identical concepts, DataOps can prove to be an important enabler. According to a DataOps process model, there is close connectivity between data processes, tools, and the users applying the insights.

Users are aligned to continuously rely on data, meaningfully leverage the available tools, and apply insights to optimize operations. This model has a symbiotic relationship with the architecture of data fabric. Without a DataOps process model and a DataOps mindset, users will struggle to get the most out of the data fabric.

2. Proactively avoid building just another data lake

A common pitfall when building a data fabric is that it may end up becoming just another data lake. If you have all the architectural components in place â€“ data sources, analytics, BI algorithms, data transport, and data consumption â€“ but without the APIs and SDKs, the result is not a true data fabric.

Data fabric refers to an architecture design, not a single technology. Interoperability between components and integration readiness are defining traits of this design. That’s why enterprises need to pay special attention to the integration layer, seamless data transport, and automated insights delivery to newly connected front-end interfaces.

3. Understand your compliance and regulatory requirements

Data fabric architecture can help improve security, governance, and regulatory compliance as there is a holistic environment in which the data operates. As data isn’t scattered across disparate systems, there is a smaller threat vector and less risk of sensitive data exposure.

However, it is important to carefully understand the compliance and regulatory requirements surrounding your data before implementing a data fabric. This is because different data types may fall under different regulatory jurisdictions, with a different set of laws governing them. You can address this through automated compliance policies that enforce data transformations to comply with laws as necessary.

4. Deploy graph-based analytics to find correlations

Graph analytics is a smarter alternative to relational databases that helps visualize metadata and data relationships using knowledge graphs. It enriches the data with a semantic context to understand what the information implies instead of only text strings.

Knowledge graphs powered by graph analytics are ideal for data fabrics â€” the primary purpose of a data fabric architecture is to enable holistic use of disparate data sources without duplication. A knowledge graph can provide business and operational insights by investigating the relationships between data sources. It is better at integrating disparate data than a relational database approach, and the insights unearthed are also more relevant to business users.

See More: Top 10 Data Governance Tools for 2021

5. Set up a data marketplace for citizen developers

Typically, data fabric architecture will generate and deliver insights directly to business applications or create segmented data repositories for analysis by IT or your data team. There is another way you can leverage the potential of data fabric â€“ through a data marketplace that democratizes access for citizen developers.

Business users with some understanding of data analysis and with years of business analysis expertise can weave data from this marketplace to create new models for emerging use cases. In addition to implementing use case-specific BI, enterprises can empower citizen developers to leverage the data fabric in new and flexible ways.

6. Make use of open source technology

Open source can be a game-changer when building a data fabric. By its very definition, a data fabric is meant to be extensible and integration-ready, which means that open source tools are best suited for its architecture.

Open source components can also reduce your dependence on a single vendor, as data fabrics may involve a hefty investment, and you’d want to preserve the investment even if you choose to switch vendors later on. Make sure to check out a newly launched Open Data Fabric project that uses big data and blockchain to enable a decentralized streaming data processing pipeline.

7. Enable native code generation

Native code generation is a vital feature that allows your data fabric solution to automatically generate code that can be used for integration. Even as the data fabric processes incoming information, it may be able to generate optimized code natively in a variety of languages like Spark, SQL, and Java.

IT professionals can then leverage this code to integrate new systems for which APIs and SDKs may not yet exist. This practice will help you speed up digital transformation and add new data systems with ease, without concerns around outsize integration efforts or investments. Remember that native code generation must work in tandem with pre-built connectors to make the data fabric easy to use.

8. Adapt data fabric to edge computing

Edge data fabric (also known as edge-to-cloud data fabric) is purpose-built to support IoT implementations. It moves key data-related tasks away from the centralized application into a separate edge layer, which is distributed yet tightly connected to the data fabric. By adapting data fabric to edge computing, enterprises can get more value from their IoT devices.

For example, a smart factory may automatically calculate the weight of a cargo container using an edge data fabric (without communicating) with the centralized cloud and automatically initiate picking processes. It accelerates decision-making and enables automated actions in a manner that is not possible with a traditional, centralized data lake model.

See More: What Is a Data Catalog? Definition, Examples, and Best Practices

Key takeaways

As our data utilization volumes grow, data silos must increasingly break down to make way for connected enterprises. Data fabric implementation is a major leap on this journey â€“ indeed, among the most revolutionary breakthroughs since the invention of relational databases in the 1970s. That’s because data fabric isn’t just a technology or a product. It refers to an architecture design, a structured process, and a mindset shift where data and business actions are closely interwoven. Here are the three key takeaways enterprises have to remember:

Data fabric can significantly reduce the time spent on routine, non-value-adding data management tasks â€“ but it might involve a sizable initial investment.
A data fabric has seven key architectural components, and the API and SDK layer requires the most attention to avoid being restricted to a data lake in scope.
By definition, data fabrics are infinitely extensible, which means that you need to update and upgrade the architecture as your enterprise grows.

Data fabric can be the secret ingredient for making every process, application, and business decision data-driven. Remember the ten best practices we discussed and select the right vendor to ensure success on the road ahead.

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