The question of the day is, “What is data fabric?” Data-driven decision-making and increasing data practices are becoming more widespread in the business world. The epidemic may have compelled them to act, but they’ve recognized the value of data and will never go back to making judgments based on hunches. So, the phrase “data fabric” has become synonymous with enterprise data integration and management over the last few years. Data fabric is an end-to-end data integration and management solution that includes architecture, data management, integration software, and shared data for managing information. Let’s have a closer look at it.
Data fabric definition: What is data fabric?
An architecture that allows the end-to-end integration of disparate data pipelines and cloud platforms through smart and automated systems is known as a data fabric. Over the last decade, advances in hybrid cloud, artificial intelligence, the internet of things (IoT), and edge computing have created an abundance of big data, adding to enterprises’ difficulties in managing it.
As data volumes have grown, organizations need to manage and control them. This has made the unification and governance of data environments a higher priority. This growth has presented numerous issues, such as data silos, security risks, and decision-making bottlenecks. Data management teams are leveraging these tools to unify their disparate data systems, embed governance, improve security and privacy measures, and increase worker access to data.
“By 2024, 25% of data management vendors will provide a complete framework for data fabric – up from 5% today.”Gartner
Data fabric is a solution that allows organizations to manage their data—whether it’s in different types of apps, platforms, or regions—to address complex data issues and use cases. Data fabric makes it simple for users to access and share information in a distributed data environment.
Simplified: What is data fabric?
Maybe we can better understand what is data fabric with this example. Consider a self-driving vehicle as an example. Consider two situations. At first, the driver is in control and pays close attention to the route, while the car’s autonomous component takes minimal or no action. The driver is slightly negligent in the second scenario, and the machine instantly changes to a semi-autonomous mode and makes required adjustments.
The two examples above summarize how data fabric works. As a passive observer, it begins monitoring the data pipelines and offering better alternatives. When the data “driver” and machine learning are comfortable with repeated scenarios, they automate improvisational activities (which take too much manual labor), leaving leadership free to focus on innovation.
Data fabric architecture explained
A key feature of data fabric architecture is that it is used across all data structures and sources in a hybrid multicloud environment, from on-premises to cloud to edge.
Data fabric aims to make an organization’s data as useful as possible – and as quickly and safely as possible – by establishing standard data management and governance processes for optimization, making it visible, and providing insights to numerous business users.
Businesses that utilize this sort of data architecture have certain similarities in their architecture that are unique to a data fabric. More specifically, they include the following six layers:
- Data Management layer: This is in charge of data management and security.
- Data Ingestion Layer: The layer stitches together cloud data and establishes connections between structured and unstructured data.
- Data Processing: The data processing layer cleanses the data, ensuring only relevant information is presented for extraction.
- Data Orchestration: The data fabric’s most essential layer, which performs critical tasks such as transforming, integrating, and cleansing data to make it useful for teams throughout the company.
- Data Discovery: This layer exposes new ways to aggregate diverse data sources. It may, for example, discover methods to link data in a supply chain data mart and a customer relationship management system, allowing for the creation of new product offers to clients or improvements in client satisfaction.
- Data Access: This layer enables data consumption, ensuring appropriate authorization for certain teams to follow government rules. This layer also aids in presenting important data via dashboards and other data visualization technologies.
Data fabric must-haves
We explained what is data fabric and the following are the features that a good data fabric solution should have:
- Autonomous data engineering: This is done by monitoring real-time data and analyzing it to make just-in-time query optimization for efficiency and usage consumption that can anticipate the demands of the data consumer in a single architecture, lowering the complexity of data management.
- Unified data semantics: A data warehouse for all consumer data to establish corporate meaning and obtain a single-source-of-truth (SSOT) point. Regardless of architecture, database technology, or deployment platform.
- Centralized data security & governance: A single security policy can distribute access and apply Zero Trust principles uniformly across the infrastructure, regardless of whether data is stored in the cloud, across clouds, in a hybrid scenario, or on-premises.
- Data management visibility: The capacity to monitor data reactivity, availability, dependability, and risk in a centralized location is crucial for businesses.
- Agnostic to platform and application: Consumers and data managers will be able to choose from various analytics solutions, thanks to the ability to integrate with the data platform or BI/ML application.
- Future-proofs infrastructure: Modernize legacy systems to maximize investments while limiting the disruption of new technologies and data types. New infrastructure builds are seamless with current infrastructure, and existing infrastructure is not disrupted.
- No need for data movement: Intelligent data virtualization creates a unified view of data collected from many sources without copying or transporting it.
But be careful. These requirements may cause you to confuse data fabric with a data lake and data mesh. Because of that, we have prepared some comparisons for you.
Comparison: Data fabric vs data lake
A data lake is a repository for data and data assets, whereas a data fabric is a method for extracting and utilizing such information. The two phrases are synonymous; many experts believe that using a data fabric to extract the most value from stored data is the greatest way. However, there are significant differences between them.
A data lake is a repository of data in its raw form that has not been sorted or indexed. The data might be anything from a simple file to a large binary object (BLOB), such as a video, audio, image, or multimedia file. When the data is extracted, it’s evaluated and manipulated to make it usable.
The term “data fabric” refers to a system used by an organization’s data across all storage and usage scenarios and that uses the same set of protocols, processes, organizations, and security.
Comparison: Data fabric vs data mesh
Although the terms data fabric and data mesh are sometimes used interchangeably, they represent distinct ideas. In general, a data fabric and a data mesh are similar in that they are techniques for recognizing how businesses manage large quantities of stored information. A data fabric approach aims to regulate data by constructing an administrative layer on top of it wherever it is kept. The latter differs from the former in that aspects of certain types of data management are handled by teams or groups within the organization who utilize that information.
On the other hand, a data fabric is a technology-centric architectural approach that addresses the difficulty of data and metadata. In contrast, a data mesh focuses more on organizational change, emphasizing people and procedure than architecture.
Data fabric advantages
Gartner has noted specific gains in efficiency for data fabric providers as they gain more market adoption. It can “reduce the time for integration design by 30%, deployment by 30%, and maintenance by 70%.”
While it’s clear that data fabrics may boost productivity across an organization, the following business benefits have been shown:
Data fabrics employ semantic knowledge graphs, metadata management, and machine learning to connect disparate data sources and endpoints. This helps data management teams group related datasets together while also integrating net new data sources into a firm’s data ecosystem. Automating parts of data workload administration leads to the efficiency mentioned above, but it also aids in the breakdown of silos across IT systems and centralized governance processes. The overall quality of your information improves as a result of this functionality.
Democratization of data
Data fabric architectures allow for self-service applications, broadening access to data beyond more technical resources like data engineers, developers, and data analytics teams. Lowering data bottlenecks allows for greater productivity, allowing business users to make faster business decisions while freeing technical users to focus on activities that better utilize their talents.
Better data protection
The open-data movement also does not imply giving up on data security and privacy protections. It requires the establishment of additional data governance barriers around access controls, ensuring that specific data is only accessible to designated people. Data fabric designs also enable technical and security teams to implement data masking and encryption across sensitive and proprietary material, minimizing the chance of data sharing or system breaches.
Data fabric risks
The worry of data security when data is passed through the data fabric from one location to another has become a major concern for businesses. To guarantee safety from security breaches, the infrastructure for data transportation must include secure firewalls and protocols. Data security at all stages in the data cycle is essential as cyber assaults on firms increase.
Data fabric examples/use cases
Data fabrics are still in their early days when it comes to adoption, but their data integration capabilities enable firms to perform a wide range of use cases. While the tasks that a data fabric can handle may not be vastly diverse from other data solutions, it distinguishes itself by the scope and scale of operations it can manage because it eliminates data silos. Companies and their data scientists may create a comprehensive view of their customers by integrating various data sources, which is particularly beneficial for banking clients.
What is data fabric, and what does data fabric adoption offer your company? Consider the following use cases for further information:
- Customer profiles,
- Fraud detection,
- Preventative maintenance analysis,
- Return-to-work risk models,
- Enterprise innovation,
- Preventative maintenance,
- Slaying silos,
- Deeper customer insights,
- Enhanced regulatory compliance,
- Improving data accessibility across healthcare organizations and academic institutions, and more.
Best data fabric companies and tools
The major objective of data fabric is to provide integrated and enhanced data – in the proper time, in the appropriate format, and to the correct data consumer – for operational and analytical purposes. Here are some of the best solutions:
In both on-premises and cloud environments, IBM offers a variety of integration methods for nearly every business use case. The company’s on-premise data integration suite comprises tools for traditional (replication and batch processing) and modern integration synchronization and data virtualization). IBM also provides several prebuilt functions as well as connectors. The cloud integration solution from mega-vendors is widely regarded as one of the finest in the market, with new features being introduced ongoing.
Denodo is a prominent supplier of data virtualization tools. Denodo, which was founded in 1999 and is based in Palo Alto, California, provides high-performance data integration and abstraction across a range of big data, business intelligence, analytics, and unstructured and real-time data services. Denodo also offers unified business data access to businesses wanting to use BI solutions such as statistics and single-view apps. The only data virtualization platform on Amazon AWS Marketplace is the Denodo Platform.
The K2View Data Fabric is a unified platform for data integration, transformation, enrichment, orchestration, and delivery. The product was created to enable real-time activities while integrating fragmented data from various business units into their micro-DBs to offer a comprehensive perspective. One micro-DB is maintained for each instance of a company entity, with web services component sizing and exposing the information in the micro-DBs for use by external applications. K2By utilizing a distributed architecture, the K2View Data Fabric can handle hundreds of millions of micro-DBs at once.
We explained what is data fabric and everything about it in this article. If you are interested in the topic, please drop a comment below to start a conversation.
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