Staff Articles

Modernizing Data Management with Data Fabric Architecture

Learn why data and analytics leaders need to work on modern data fabric architecture.

Introduction
1. The Evolution of Modern Data Fabric Architecture
2. Key Pillars of a Data Fabric Architecture
2.1. Collect and Analyze All Forms of Metadata
2.2. Convert Passive Metadata to Active Metadata
2.3. Create Knowledge Graphs
2.4. Develop a Robust Data Integration Strategy
In Summary

Introduction

Data has always been at the core of a business, which explains the importance of data and analytics as core business functions that often need to be addressed due to a lack of strategic decisions. This factor gives rise to a new technology of stitching data using data fabrics and data mesh, enabling reuse and augmenting data integration services and data pipelines to deliver integration data. 

Further, data fabric can be combined with data management, integration, and core services staged across multiple deployments and technologies. 

This article will comprehend the value of data fabric architecture in the modern business environment and some key pillars that data and analytics leaders must know before developing modern data management practices. 

1. The Evolution of Modern Data Fabric Architecture

Data management agility has become a vital priority for IT organizations in this increasingly complex environment. Therefore, to reduce human errors and overall expenses, data and analytics (D&A) leaders need to shift their focus from traditional data management practices and move towards modern and innovative AI-driven data integration solutions.

In the modern world, data fabric is not just a combination of traditional and contemporary technologies but an innovative design concept to ease the human workload. With new and upcoming technologies such as embedded machine learning (ML), semantic knowledge graphs, deep learning, and metadata management, D&A leaders can develop data fabric designs that will optimize data management by automating repetitive tasks

2. Key Pillars of a Data Fabric Architecture

Implementing an efficient data fabric architecture needs various technological components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working on the key pillars below, D&A leaders can create an efficient data fabric design to optimize data management platforms.

2.1. Collect and Analyze All Forms of Metadata

To develop a dynamic data fabric design, D&A leaders need to ensure that the contextual information is well connected to the metadata, enabling the data fabric to identify, analyze, and connect to all kinds of business mechanisms, such as operational, business processes, social, and technical. 

2.2. Convert Passive Metadata to Active Metadata

IT enterprises need to activate metadata to share data without any challenges. Therefore, the data fabric must continuously analyze available metadata for the KPIs and statistics and build a graph model. When graphically depicted, D&A leaders can easily understand their unique challenges and work on making relevant solutions. 

2.3. Create Knowledge Graphs

To get a better understanding of the data fabric architecture, D&A leaders should consider creating knowledge graphs based on semantic layers, as it makes them more intuitive and easy to interpret when making decisions. The semantic layers add depth and purpose to the data usage; the use of AI/ML algorithms further simplifies the information by providing AI-based decision-making and operational use cases.

2.4. Develop a Robust Data Integration Strategy

Data fabric should be compatible with numerous existing tools in the modern data stack and ease the work of data integration experts and data engineers by ensuring easy access to data and knowledge graphs. With built-in interoperability, data fabrics can be used to connect and migrate data to any preferred business intelligence (BI) tools to refine data products. 

In Summary 

Data Fabric presents an agile solution that needs a unified architecture and a metadata-driven approach, enabling organizations to efficiently access, integrate, and transform diverse data sources, empowering D&A leaders to adapt swiftly to business needs. Therefore, by providing a consistent data view, a data fabric enhances collaboration, data governance, data privacy, and data-driven decision-making for data engineers and other IT employees who use data. With a good data fabric design, an enterprise’s workflow gets streamlined and centralizes the data ecosystem, which makes your enterprise’s systems efficient.

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