What Is Data Fabric?
A data fabric is a structure that enables automated end-to-end integration of several data pipelines and cloud environments.
In the past decade, the IT landscape has grown more complex, with edge computing, AI, hybrid clouds, and IoT. Data privacy regulations have become stricter and more widely prevalent. A data fabric is used to integrate governance, unify data systems, enhance security and privacy controls, and give employees improved access to data.
A data fabric allows decision-makers to access data more coherently and derive more insights from it. As a result, data fabrics can accelerate digital transformation, integration, and automation projects across organizations.
This is part of a series of articles about data security.
How Does a Data Fabric Architecture Work?
A data fabric architecture utilizes auto-integration capabilities to provide a plug-and-play connection between business applications and data sources. Knowledge graphs analyze the relationships between data sources, converting all data into a consistent format. This level of consistency helps make data accessible and prevents bottlenecks.
Automating the data integration process typically involves detecting existing data and metadata. It creates a unified data layer, from the data source level, using analytics, orchestration, and automated insights generation. You can also leverage data fabrics to set up bidirectional integration with your technology stack components.
What Are the Benefits of Data Fabric Technology?
Organizations looking for a quick solution to integrate and process data should use data virtualization. Data fabric technology is suitable for more complex challenges. It enables organizations to utilize several data sources across various geographical locations, solve complex data issues, and implement challenging data use cases.
Data fabric provides an agile model that enables organizations to adapt and adjust systems as needed while ensuring they continue to work across all operating systems and storage locations. It facilitates scalability with minimal interference and does not require investment in expensive hardware or expert personnel.
Organizations leverage data fabric technology to establish maximum integrity while complying with compliance regulations and maintaining accessibility and real-time data flow. This technology helps organizations get real-time insights into supply chains, sales optimization, consumer behavior, marketing, forecasting, and other aspects that provide a competitive edge.
Related content: Read our guide to data discovery
Data Fabric vs. Data Mesh
Data mesh is a new strategy emphasizing decentralized teams to facilitate data scaling. Data sets, governance, administration, and processes related to various business disciplines are handled by pods of experts, who are responsible for hosting and serving the data.
In a data mesh architecture, data is viewed as a product. Its storage, pipelines, metadata, quality, security, and service-level contracts are all viewed as parts of its value.
Differences between data fabric and data mesh include:
- Data mesh encourages data product thinking as a design feature. As a result, data is managed and provisioned in the same way as any other product. By contrast, data fabrics treat data as a commodity that needs to be processed to derive value from it.
- Data mesh depends on human data product owners to drive goals, while data fabric automates the discovery, linking, recognizing, proposing, and distribution of data assets to data consumers.
- Data mesh modifies cultural norms and organizational systems, advocating decentralization of data practitioners. Data fabric technology focuses on the technology aspect of data and ensures it is high quality and accessible by the relevant stakeholders.
Data Fabric Challenges
A data fabric implementation involves many components, including different databases, storage locations, and data management policies. A data fabric solution needs to harmonize all these differences. Otherwise, it might lead to application silos and data silos, limiting the amount of information available within the data fabric.
Operational issues and silos
You can address this challenge by creating a unified platform to serve as the foundation of your data fabric implementation. Using several platforms creates more silos and hinders operational efficiency. You should use an extendable data fabric technology that enables you to start small and scale as needed. Apply data fabric initially to an operating unit, subsidiary, or specialized data set, and extend later.
Harmonization and unification
Whether you use virtualization or data fabric technology, harmonization and unification carry a certain risk. For instance, location independence prevents applications accessing information through a data fabric from knowing the data’s location. It might result in performance issues and lead to high data transfer charges when moving data regularly across a hybrid or multi-cloud environment.
Databases and APIs
Databases, APIs, and query languages often use different access mechanisms. An effective data fabric strategy should incorporate a common access/query mechanism without excluding specialized APIs and query languages. Excluding these components prevents existing applications from running. A data fabric solution must harmonize the access/query technology as applications are modified or added.
Best Practices for Handling Data Fabric Tools
Adopt a DataOps Process
DataOps is not the same concept as a data fabric, but it can be important as an enabler. According to the DataOps model, there should be a close connection between data tools, processes, and users who apply the extracted insights.
With DataOps, users can consistently rely on data, easily use the tools available, and apply the insights from processed data to optimize their operations. Without the DataOps approach, users may struggle to take full advantage of a data fabric.
Understand Your Compliance and Regulatory Requirements
The data fabric’s architecture can impact security, regulatory compliance, and governance. It provides a comprehensive environment where the data is produced and processed. Because the data isn’t spread across disparate systems, the attack surface is smaller and the risk of exposing sensitive data is lower.
However, carefully understanding the regulatory and compliance requirements related to your data is essential before implementing the data fabric. Different types of data are subject to different regulatory and industry requirements, with different sets of laws and standards. The best way to address the complexity of compliance is with automated policies to enforce data transformations and ensure they comply with relevant laws.
Enable Native Code Generation
Native code generation is a critical data fabric feature and allows it to automatically create integration code. The data fabric can natively generate optimized code in various languages even while processing incoming data, including Spark, Java, and SQL.
IT practitioners can then use this automatically generated code to integrate new systems when a relevant API or SDK does not yet exist. This approach helps you accelerate your digital transformation and easily introduce new data systems, without worrying about major integration efforts. However, keep in mind that native code generation needs to work together with pre-existing connectors, and should not replace existing integrations.
Adapt Data Fabric to Edge Computing
An edge data fabric is specially-built for IoT implementations. It removes major data-related tasks from the centralized management application, instead placing them in a dedicated edge layer. This layer is distributed but tightly linked to the data fabric. By adapting the data fabric to support edge computing, organizations can extract more value from IoT (edge) devices.
For example, smart factories can use an edge data fabric to automatically calculate the characteristics of a product on a production line without requiring inputs from a centralized cloud. The data fabric can then automatically select the most appropriate production processes. This accelerates decision-making and enables faster automated actions with less overhead.
Imperva Data Security Fabric
Imperva Data Security Fabric protects all data workloads in hybrid multicloud environments with a modern and simplified approach to security and compliance automation. Imperva DSF flexible architecture supports a wide range of data repositories and clouds, ensuring security controls and policies are applied consistently everywhere.
