Data Fabric – Practical Advice on How to Architect Next-Generation Data Management

Organizations today face significant challenges when it comes to data integration and generating insights from data silos. One of the biggest hurdles in the current data landscape is data fragmentation, where data is distributed across various systems and platforms, making it difficult to access, analyze, and manage. With the increasing number of data sources in a hybrid and multicloud world, organizations are struggling to integrate data from multiple heterogeneous sources to create a unified view of data.

Data fabric encompasses a broader concept that goes beyond standalone solutions such as data virtualization. Rather, the architectural approach of data fabric integrates multiple data management capabilities into a unified framework. Data Fabric is an emerging data management architecture that provides a “net” that is cast to stitch together multiple heterogeneous data sources and types, through automated data pipelines. It offers several capabilities that differentiate it from other solutions:

• Utilizes intelligent orchestration by analyzing metadata to provide recommendations for effective data orchestration.
• Incorporates data quality measures within pipelines to ensure the data delivered to end users is highly reliable.
• Provides data observability, allowing for the detection of schema drifts, lineage, and anomalies. Users get real-time alerts that allow them to take required actions for fixing errors.

This all-encompassing data fabric meets the needs of key data stakeholders and business users of the organization as well. For business teams, data fabric empowers non-technical users with the ability to easily discover, access, and share the data they need to perform everyday tasks. It also bridges the gap between data and business teams by including subject matter experts in the creation of data products. For data teams, data fabric improves the productivity of these resources by automating the data integration process and accelerating the delivery of the data business teams need.

To create an efficient data fabric architecture, start by following these five critical processes:

1. Establish a Data Integration Framework: Integrating data from heterogeneous sources is the first step in building a data fabric. To begin, organizations should employ data crawlers, which are designed to automate the acquisition of technical metadata from structured, unstructured, and/or semi-structured data sources in on-prem and cloud environment. Then, this metadata can be used to initiate the ingestion process and integrate diverse data sources. By implementing a metadata-driven ingestion framework, organizations can seamlessly integrate structured, unstructured, and semi-structured data from internal and external sources, which enhances the effectiveness of the underlining data fabric architecture.

2. Practice Active Metadata Management: Unlike traditional methods that focus on technical metadata storage only, data fabric incorporates operational, business, and social metadata. What sets data fabric apart from other options, is its ability to activate metadata, allowing seamless flow between tools in the modern data stack. Active metadata management analyzes metadata and delivers timely alerts and recommendations for addressing issues like data pipeline failures and schema drifts as needed. This proactive approach also ensures a healthy and updated data stack within the data fabric architecture.

3. Gain Better Insights through Knowledge Graph: One of the key advantages of data fabric is its ability to leverage knowledge graphs to showcase relationships among different data assets. In a knowledge graph, nodes represent data entities, and edges connect these nodes to illustrate their relationships. Leveraging knowledge graphs within the data fabric enhances data exploration and enables more effective decision making processes. This contextualization of data facilitates data democratization, empowering business users with the ability to access and understand data in a meaningful way.

4. Foster Collaborative Workspaces: Data fabric enables diverse data and business users to consume and collaborate on data. These collaborative workspaces enable business and data teams to interact so together they can standardize, normalize, and harmonize data assets. They also support the development of domain-specific data products by combining multiple data objects for contextual use cases.

5. Enable Integration with Existing Tools: In the data fabric architecture, it is crucial to establish seamless integration with existing tools in the modern data stack. Organizations can leverage data fabric without the need to replace their entire tool set. With built-in interoperability, data fabric can work alongside existing data management tools such as data catalogs, DataOps, and business intelligence tools. This allows users to connect and migrate curated data to any preferred BI or analytics tool, so they can refine data products for specific use cases.

Unlike other solutions that struggle to handle large and/or complex datasets and provide real-time data access and scalability, data fabric presents an agile solution. Through a unified architecture and metadata-driven approach, data fabric enables organizations to efficiently access, transform, and integrate diverse data sources, empowering data engineers to adapt swiftly to evolving business needs.

By providing a consistent data view, data fabric enhances collaboration, data governance, and decision-making. Workflows get streamlined along with improved productivity and optimized resource allocation. More importantly, data fabric empowers organizations to effectively manage, analyze, and leverage their data assets for true business success.