Today’s global organizations have data deployed both on-premises and across many cloud environments. In such a scenario, the biggest challenge is to find a single Data Management solution to enable businesses to access and connect data across disparate sources and provision a unified, virtual environment for data processing.
“The emerging design concept called ‘data fabric’ may prove a powerful answer to the perennial challenges in Data Governance, such as high-cost, low-value data integration cycles, the frequent maintenance of first-time integrations, increasing demands on real-time, event-driven data sharing, and much more,” according to Marc Beyer, Distinguished VP Analyst at Gartner.
According to Gartner, data fabric is at the top of a list of “strategic technology trends of 2022.”
Data fabric, a single-point data-processing architecture, connects all of an organization’s data sources and uses. A data fabric architecture is designed to help you manage your data more effectively and efficiently. Imagine a world where your company’s data is connected, accessible, and available wherever and whenever you need it – no matter where the source is.
The data fabric architecture is a combination of architectures and technologies designed to simplify the complexities of managing different types of data, spread over diverse database management systems, deployed on various platforms. The data fabric architecture includes knowledge graphs, data integration, AI, and metadata capabilities to allow for the continuous access, consolidation, and sharing of data throughout an organization with no prior processing, and for storing it in a centralized, structured repository. Data fabric architecture facilitates holistic use of heterogeneous data sources without data redundancy.
The final goal of a data fabric is to remove standalone data silos by connecting all the data and providing uniform distributed access.
How Does a Data Fabric Architecture Work?
Data fabric, with its auto integration capability, offers a “plug and play” environment for any type of front end (user interface), enabling insights to flow to a business application. The “knowledge graphs” technology helps deliver insights by analyzing relationships among data sources. Knowledge graph analytics and systems seamlessly convert all types of data to a consistent format, allowing them to be processed with no bottlenecks.
The data fabric automates the data integration process by detecting data and metadata. This allows for a unified data layer right from the data source level through analytics, insights generation, orchestration, and applications. Data fabrics can also allow for bidirectional integration with almost any component of a tech stack, creating a woven architecture.
Here are three examples of data fabric architecture implementation:
- K2DView Data Fabric scales up to handle hundreds of millions of secure micro-DBs at once, deployed on-premises, on-cloud, and across a distributed hybrid architecture. This environment is specifically suited to hybrid cloud deployments where organizations need to manage data access, security, controls, and visibility using several different Data Management frameworks. Your organization can implement necessary policies on security and risk management, according to different compliance requirements, on a global basis.
- Talend’s data fabric delivers the breadth of capabilities needed by today’s data-driven organizations in a single framework with native architecture, which allows them to quickly adapt to changes while building in data integrity.
- The Teradata QueryGrid works in conjunction with Teradata Vantage and Starburst Enterprise Presto to modernize analytics environments and accelerate insights. Teradata QueryGrid, the high-speed, concurrent data fabric system, delivers the kind of scale, agility, integration, full-stack management, and deep governance that enterprises demand from their data.
What Are the Benefits of a Data Fabric Architecture?
In a nutshell, a data fabric architecture helps you to manage your organizational data more effectively and efficiently.
Data fabric helps streamline and consolidate Data Governance processes between all data sources (databases, big data stores, IoT sensors, social feeds, mobile apps) and data entry points. You, the user, enjoy uninterrupted IT services across multiple IT infrastructure resources, based on changing business requirements. Users can also access and operate data using the tools they prefer, in a multi-cloud, hybrid cloud, or on-premise environment. You, the newly empowered customer, can consolidate and access data in the virtual world, regardless of whether data resides on the cloud, on the hybrid cloud, or on multi-cloud platforms.
Due to data virtualization, data from disparate sources is instantly accessed and transformed, enabling flexible self-service and real-time insights. The platform is driven by metadata; data is made available as smart, reusable data products, and served to users directly from the source. This distributed data platform provides organizations with an end-to-end view of their data universe, as well as guarantees a single, authoritative source of truth. As a no-code or low-code solution, this distributed data platform allows enterprise users to interact with the data on granular levels, without the presence of IT support.
Data Quality is continuously improved by artificial intelligence and machine learning algorithms, which leverage live metadata for integration and management of business data. The current data analytics practices involve smaller, wider-ranging data, rather than big data sitting in silos or data lakes, and data fabric is the answer for this type of data analytics. A data fabric architecture supports both core, end-user-oriented practices such as decision support and BI analytics, as well as specialized practices like Data Science, AI engineering, or ML.
Ideally, a data fabric is expected to have two integration modules: a “DIY” module for IT professionals, and an “out-of-the-box” module for business users.
Why Is a Data Fabric Architecture So Important?
A data fabric helps you manage your organizational data more effectively and efficiently. It does this by connecting all your company’s data sources and uses, regardless of where the data resides. A data fabric architecture allows you to manage your data centrally. A centralized data architecture is important because it helps you avoid duplication, and easily capture and analyze new data.
A centralized data architecture is particularly useful as your organization scales up, collects more data and transitions to new technologies. It’s critical as you implement AI, blockchain, and machine learning. A data fabric is also important because it helps you manage your data more securely. It lets you incorporate security measures into the data architecture, rather than tackling them at the end.
What Are the Key Components of This Type of Architecture?
A data fabric consists of several key components. These include a centralized data hub, standardized data schemas, and a common language. Together, these elements help you manage your data and make it more available and accessible to everyone in your organization:
- The centralized data hub is a critical element of the data fabric. It’s the place where all your company’s data is accessed in a single location. The data hub stores and manages structured and unstructured data. It also lets you process data and run analytics from one location.
- Standardized data schemas are rules that dictate how data is structured, stored, and managed. Having standardized data schemas across your organization helps you avoid duplication. It also makes it easier to collect new data and analyze it.
- The common language is key to making your data more accessible. The language lets you communicate about your data more effectively. It allows you to ask questions about your data, such as “What does our customer data look like?” or “Where are our customer data sources?”
What Are the Major Technologies and Use Cases of a Data Fabric Architecture?
Data virtualization is the core technology that sits at the heart of a data fabric and enables the seamless transformation of data and the orchestration of business processes across multiple data sources. Data virtualization technology allows for the creation of a centralized data store for all business data and data services, regardless of where it’s stored. A data virtualization platform enables organizations to focus on data discovery and application development while ignoring the need to focus on data source management. It’s an ideal solution for organizations that have multiple data sources, including data residing in different geographies, and need to use that data to support different lines of business, such as marketing, sales, or finance.
A data catalog is a centralized metadata technology that provides information about data, such as its structure, location, and schema. The data catalog also allows users to discover and request data from the data virtualization layer. Think of the data catalog as a dictionary of all your data, where you can look up to see what the data means, where it’s located, and what tools you can use to access it. The data catalog is also a tool that allows you to discover, author, and manage data services. Because the data catalog is tightly coupled to the data virtualization layer, the catalog can access data from all the underlying data sources. In addition to providing a centralized store of metadata, a data catalog provides easy-to-use data discovery tools and allows you to create reusable data services.
Data services are pre-built workflows that orchestrate data across multiple data sources. Data services offer a core technology service to a data fabric architecture because they enable you to easily build data-driven applications that are based on real-time data. However, a data service is not a single technology, but usually a combination of workflow orchestration, artificial intelligence, machine learning, and blockchain technologies. Data services can be used to build applications that are based on real-time data. For example, you can use data services to provide real-time recommendations to customers based on their purchase history.
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