What is a data fabric?

Data fabric vs. data virtualization

Both data fabric and data virtualization are data management strategies that serve slightly different purposes. The key features of data fabric and data virtualization include the following.

Data fabric

• Data fabric simplifies data management by providing real-time access to all data using a virtual access layer. The simplification is offered through a platform on which all technologies and systems across the company can run.
• Data fabric focuses on seamlessly integrating data from different data sources such as cloud, on-premises and edge devices.
• Data fabric offers scaling for handling large volumes of data across the entire data fabric.
• It provides data services such as data governance, security and integration that operate across the fabric.

Data virtualization

• Data virtualization is a concept of data integration and is the virtual access layer that data fabric uses for providing real-time access for data management. It abstracts data from underlying sources and presents it in a virtual layer to give the impression that it all resides in one place.
• Data virtualization enables applications to retrieve and manipulate data without needing technical details about the data, such as its location or format.
• The abstraction layer data virtualization provides helps speed up data integration.
• By minimizing data access time and the need to duplicate data, data virtualization provides efficient data access and processing.

What is the purpose of data fabric?

A data fabric offers a comprehensive and integrated approach to data management. The following are the main purposes of a data fabric:

• Unified data. Traditional data integration systems often fall short of providing real-time connectivity, automation and seamless data transformation capabilities, which can lead to data silos. The purpose of a data fabric is to create a unified view of the associated data to facilitate application access to information regardless of the data's location, database association or structure. Achieving a unified view of customers, products and data provides businesses with a competitive advantage.
• Business intelligence. It's also used to simplify the analysis, often with artificial intelligence (AI) and machine learning (ML). As such, data fabrics are becoming a primary tool in converting raw data into business intelligence.
• Application development. Data fabrics can also facilitate application development by creating a common model for accessing information, which is a departure from the application and database silos already common. This same harmonization can improve operational efficiency. At the line organization level, they can provide better information access. At the IT level, data fabrics improve efficiency by creating a single layer where data access is managed across all resources.
• Simplification of access. A data fabric is a recent innovation in enterprise data management and digital transformation. The most general application of data fabrics is the simplification of database access, which is made complicated by the wide variety of apps, data models, formats and distributed data assets found in a typical enterprise.
• Multi-platform support. A data fabric architecture is designed to provide extensive data integration, management and delivery across several deployment and orchestration platforms and processes, supporting both operational and analytics use cases.

Data fabric architecture

A data fabric architecture is the structural design and components that enable the creation and management of a unified and integrated data environment across distributed systems.

The main components and layers of data fabric typically include the following:

1. Data integration. Data integration involves gathering data from multiple sources, converting it into a single format and storing it in a central data repository. To achieve certain data requirements and operational scenarios, data integration uses techniques such as batch processing, real-time streaming and data virtualization.
2. Data storage. A data fabric architecture includes a centralized data storage repository that stores the integrated data, so it supports scalability, performance and accessibility. This layer can include data warehouses, data lakes and other storage options that provide optimization for different data types.
3. Data governance. This layer of the data fabric architecture uses policies, access controls and auditing procedures to guarantee data integrity, security and compliance with legal regulations. Features include data lineage, metadata management and data lifecycle management.
4. Data processing. A data fabric architecture empowers organizations to process data in a distributed and scalable fashion, using techniques such as data analytics, visualization and ML to extract insights and value from integrated data. Processing can occur centrally or across diverse environments and can be tailored to specific needs and use cases.
5. Data orchestration. Data orchestration is a key element of a data fabric architecture, as it oversees the smooth flow of data across various sources, systems and processing environments. Data orchestration ensures efficient data ingestion, transformation and processing and enables organizations to effectively utilize their data resources.
6. Data access. This layer facilitates data consumption, ensuring team-specific permissions to adhere to regulatory requirements. It also enhances data accessibility through dashboards and other visualization tools.

Data fabric advantages

Data fabrics offer both line operational benefits and benefits to the IT organization:

• Breaks down data silos. Modern databases are usually associated with applications or groups of applications. Databases also tend to grow as applications are added to the enterprise inventory. This often results in silos of data with different structures and formats. Data fabrics improve the ability to gain insight into the full range of enterprise information and use the collected data to improve operational efficiency and empower workers.
• Unites databases spread over a large area. Data fabrics can make sure that their location differences don't form a barrier to access. They simplify application development by harmonizing different data access application programming interfaces (APIs). They can be used either to optimize specific application data use without making data less accessible to other applications or to unify data that's already become siloed.
• Provides a single way to access information in both the cloud and data center. The number of applications hosted in or partially in the public cloud has increased. Data fabrics can improve application portability and cloud bursting or backup of cloud components in the data center.
• Offers data agility. With data fabric, organizations can achieve greater agility in data management by quickly accessing and transferring data across various platforms and environments. This capability enables companies to promptly adapt to evolving business requirements. It also facilitates proactive decision-making and reduces the risks associated with shadow IT.
• Supports analytics. Data fabrics can integrate a variety of analytics options such as business intelligence, data exploration, natural language processing and ML.

Data fabric deployment challenges

If a data fabric isn't deployed effectively, it can create several challenges, including the following:

Data silos

The greatest challenge in deploying data fabric options is the wide variety of databases, data management policies and storage locations found in most enterprises. With the rise of big data and innovative technologies such as AI, hybrid cloud, edge computing and the internet of things, enterprise management has become more complex. A fabric option should be able to harmonize all these differences. If not, application silos and data silos will persist, limiting the sum of information available in the data fabric.

Addressing this challenge begins with creating a unified platform as the foundation of a data fabric. Multiple platforms can add to the silo problem and reduce the operational efficiency benefits considerably. This means that if data fabric technology is initially applied to a specialized set of data, or an operating unit or subsidiary, the technology must be extendable to the company at large, and that extension should be the goal.

Scalability issues

If a data fabric isn't scalable, it can cause issues with growth accommodation. Therefore, a data fabric should be able to scale horizontally and vertically to accommodate increasing data volumes while maintaining optimal performance.

Harmonization risks

Harmonization and unification through virtualization always create a risk, and that's true of data fabrics. For example, location-independence means that applications that access information via a data fabric are insulated from knowing where the data is located.

This can create serious performance implications. In cloud computing, it can create high data transfer charges if data is moved regularly across the hybrid or multi-cloud boundary.

Data integration challenges

The data fabric should be able to support a range of data delivery methods such as extract, transform and load, streaming, replication, messaging and data virtualization or microservices.

It should also meet a wide range of user requirements, such as those of business users looking for self-service data preparation tools as well as IT users with intricate integration requirements.

Varying access and query mechanisms

Different access mechanisms found among the various databases and the difference in APIs and query languages can pose challenges with data fabric.

A good fabric strategy should support a common access and query mechanism. At the same time, it can't exclude the use of specialized APIs or query languages, or current applications wouldn't be able to run. Thus, the fabric concept must reach the goal of harmonizing the fabric access and query technology as applications are added or modified.

Data fabric uses and examples

Data fabric is applied across diverse use cases and industries, empowering organizations to effectively manage data and enhance their operational capabilities.

Common examples and use cases of data fabric include the following:

• Centralized business management. The most common use of a data fabric is the virtual or logical collection of geographically diverse data assets to facilitate complete access and analysis. In this application, the data fabric is typically used for centralized business management. The distributed line operations that collect and use the data regularly are still supported through their traditional applications and data access and query interfaces. This is particularly valuable for organizations that have regional or national segmentation of their activities but require central management and coordination.
• Unified data model. A second common use is the creation of a unified data model for a company following a merger and acquisition. In these situations, it's almost certain that the database and data management policies of the previously independent organizations will be different, making collection of information across organizational boundaries difficult. A data fabric can resolve this by creating a unified view of data. This enables the combined entity to gradually harmonize on a single virtual data model if desired, but at the best pace for operational efficiency while sustaining profits and sales.
• Machine learning models and AI. By using semantic knowledge graphs and extensive integration of organizational data, data fabrics can optimize data feeding and preparation for ML models. A data fabric also speeds up the creation of ML models by streamlining data preparation and guaranteeing reusable model data across various platforms.
• Real-time personalization. Organizations can use data fabric to bring together data from all interaction points of the customer and create a holistic customer view. This enables seamless real-time personalization and customization for customer initiatives.
• Cloud migration. A data fabric helps with cloud migration strategies by offering a unified framework that spans across both on-premises and cloud environments. For example, it supports scalable, flexible and cost-effective migrations, including real-time data analysis in the telecommunications industry. This is achieved through elastic data processing, which refers to the ability to dynamically allocate computing resources such as processing power and storage as needed to handle varying workloads and data volumes efficiently.
• Healthcare data tracking. Healthcare organizations must deal with large amounts of data from multiple sources, such as electronic health records, wearable sensors, medical devices and administrative systems. Data fabric integrates these disparate data sources into a unified view, making it easier to access and analyze comprehensive patient information in real time. By using data fabric to track all instances of patient data across their systems, healthcare organizations can also ensure compliance, data quality and data security.

Data fabric software market

According to a report by Precedence Research, the data fabric market is projected to reach $8.9 billion by 2032. However, it must be noted that the boundaries of the data fabric market are still hazy and have yet to be defined. But in the meantime, Precedence Research notes the following vendors provide data fabric software:

• Hewlett Packard Enterprise.
• IBM.
• Informatica.
• Oracle.
• NetApp.
• SAP.
• Talend.
• Tibco Software.

Learn how to harness the full benefits of DataOps through effective architecture design and explore the core components of a DataOps architecture.