Is Data Fabric Architecture a Key to Modernizing Data Management and Integration?

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There is no argument that data is the most valuable asset for businesses today. While some organizations build entire business models around data, others routinely capture, store, and analyze large volumes of data to create decisive patterns, capture insights, predict business outcomes, track customer behavior, or improve customer engagement.

However, to make informed business decisions, we need a powerful automated system like Data Fabric, which can integrate and manage data more efficiently.

With this in mind, let’s explore what this data fabric has to offer and the latest trends shaping the way data management and integration work.

What Exactly is a Data Fabric?

Data Fabric is an architecture that employs intelligent and automated tools to enable the end-to-end integration of various data pipelines and cloud environments. Over the last decade, advancements in hybrid cloud, artificial intelligence, the Internet of Things (IoT), and edge computing have resulted in the exponential growth of big data, adding to the complexity that organizations must manage. As a result, the need for data environment unification and control has increased, as this growth has produced substantial issues such as data silos, security risks, and general decision-making bottlenecks.

With data fabric solutions, data management teams are confronting these difficulties head-on. They use them to combine disparate data systems, embed governance, increase security and privacy safeguards, and provide more data accessibility to workers, particularly their business users.

Data Fabric vs. Data Virtualization

One of the technologies that enable a data fabric approach is data virtualization. Rather than physically transporting data from numerous on-premises and cloud sources using typical ETL (extract, transform, load) methods, a data virtualization solution connects to the various sources, integrating only the necessary metadata, and producing a virtual data layer. This enables users to utilize the source data in real time.

Google introduced Dataplex, an intelligent data fabric service that allows organizations to locate, manage, monitor, and govern their data centrally across data lakes, data warehouses, and data marts. This also provides consistent controls, provides access to trusted data, and powers analytics at scale.

Benefits of Data Fabric that Simplifies the Complexity of Data Management

A data fabric can improve the overall productivity of data, some of the main benefits of a data fabric include:

Intelligent Integration

Data fabrics collect data across various data types and endpoints using semantic knowledge metadata management, graphs, and machine learning. This assists data management teams in collecting related datasets and integrating net new data sources into a company’s data ecosystem.

This functionality not only automates aspects of data workload management, resulting in the previously mentioned productivity savings, but it also aids in the elimination of silos across data systems, the centralization of data governance practices, and the improvement of overall data quality.

Microsoft just has disclosed a new type of AI-powered end-to-end data and analytics platform built on the company’s OneLake Data Lake called Microsoft Fabric. The platform also will support integration with other cloud storage services, such as Amazon S3 and Google Cloud Platform in the near future.

Democratization of Data

Self-service applications are enabled by data fabric architectures, extending data access beyond more technical resources such as data engineers, developers, and data analytics teams. Reduced data bottlenecks lead to increased productivity, allowing business users to make more timely business decisions and releasing technical users to prioritize tasks that best utilize their skill sets.

Better Data Protection

Data fabric provides granular control over data access to ensure that only authorized users have access to sensitive information. With data fabric, various data pipelines, and cloud environments are integrated into a single unified architecture for data management, making it easier to secure, backup, and store data.

Data fabric architectures also allow technical and security teams to enforce encryption and data masking around susceptible and proprietary data, lowering risks around data sharing and system breaches.

Emerging Trends That Are Reshaping the Data Fabric for Data & Analytics Leaders

Emerging trends that are increasing the popularity of data fabrics in the industry include:

Intelligent, More Reliable, Scalable AI

An AI with intelligence, reliability, and scalability will enable better learning algorithms, interpretable systems, and shorter valuation time.

Many organizations have started to acquire a lot more from AI systems, but they have to figure out how to scale the technology, which has been difficult so far. Although traditional AI algorithms rely mainly on historical data, this data may no longer be relevant to how COVID-19 has changed the business landscape. As a result, AI technology must be able to operate with less data by utilizing “small data” approaches and adaptive machine learning. To promote ethical AI, these AI systems must also preserve privacy, comply with federal standards, and minimize bias.

Composable Data and Analytics

The purpose of composable data and analytics is to combine components from various data, analytics, and AI systems to provide a flexible, user-friendly, and usable experience that allows D&A (Data and Analytics) leaders to connect data insights to business actions.

Composing new apps from each company’s packaged business capabilities boosts efficiency and agility. Not only will composable data and analytics stimulate cooperation and enhance the organization’s analytics skills, but it will also increase access to analytics.

Using Data Fabric as the Foundation

Data fabric is the architecture that will support composable data and analytics and its different components as data becomes more complex and digital business accelerates. Because the technological designs rely on the flexibility to use/reuse and combine different data integration types, data fabric decreases the time for integration design by 30%, deployment by 30%, and maintenance by 70%.

Furthermore, data fabrics can use existing data hubs, data lakes, and data warehouse capabilities and technologies while also providing new ideas and tools for the future.

Competitor Google has presented BigLake, which unifies data warehouses and lakes by enabling BigQuery. From a technical point of view, BigLake will be more focused on accessing data with fine-grained access control on the Data lakehouse or data fabric.

Small and Wide Data to Solve Complex Queries

In contrast to big data, small and wide data solves a number of problems for organizations dealing with more complex AI queries and challenges with scarce data use cases. Wide data allows the analysis and synergy of a variety of small and wide, unstructured & structured data sources to enhance contextual awareness and decisions by employing “X analytics” methodologies. As the name implies, small data might use data models that use less data but still provide useful insights.


XOps (data, machine learning, model, platform) aims to achieve efficiencies and economies of scale by leveraging DevOps best practices — and to ensure dependability, reusability, and repeatability while reducing technological and process duplication and enabling automation.

These technologies will allow prototypes to be scaled and will provide a flexible design and agile orchestration of controlled decision-making systems. In order to generate business value, XOps will allow organizations to operationalize data and analytics.

Engineered Decision Intelligence

Decision intelligence is a discipline that encompasses many aspects of decision-making, such as conventional analytics, AI, and complex adaptive system applications. Engineering decision intelligence extends not only to individual decisions but also to sequences of decisions, organizing them into business processes and even emergent decision-making networks.

This enables organizations to more quickly achieve insights required to drive actions for the business. Engineered decision intelligence opens up new opportunities when it is integrated with composability and common data fabric. This encourages organizations to rethink and reengineer their decision optimization processes to make them more accurate, repeatable, and traceable.

To Bring It All Together

Data fabric is a powerful tool for organizations looking to make sense of the massive amounts of data they generate. By integrating different data sources and enabling data flow across different IT environments, data fabric offers a streamlined approach to data management that can help organizations make better decisions and improve their operations.

Data fabric has a compelling ability to support real-time decision-making. With data fabric, organizations can quickly access and analyze large volumes of data, and use that information to make informed decisions on the fly. Additionally, data fabric provides a scalable solution that can grow and adapt as an organization’s data needs change over time. Of course, deploying a data fabric solution is not without its challenges.

By providing a consistent, integrated approach to data management, data fabric can help organizations unlock the full potential of their data and gain a competitive edge in today’s business landscape.

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