Building a Modern Data Platform with Data Fabric Architecture

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Read more about author Tejasvi Addagada.

In today’s data-driven landscape, organizations face the challenge of integrating diverse data sources efficiently. Whether due to mergers and acquisitions (M&A) or the need for advanced insights, a robust data platform with streamlined data operations are essential.

Shift in Mindset

Data fabric is a design concept for integrating and managing data. Through flexible, reusable, augmented, and sometimes automated data integration, or copying of data into a desired target database, it facilitates data access across the business and data analysts. Historically, business ownership of data has been a popular methodology, while data management and governance have been considered a science of implementation. While data mesh provides data product ownership to business domains, data fabric builds an integrated semantic layer of connected data from various sources. 

Core Tenets of Data Fabric

Democratize data

Data fabric drives discovery and innovation in a business environment at a pace to build products. Data democratization isn’t just about technology; it’s a cultural transformation that involves people, processes, and mindset shifts. By embracing these principles, organizations can unlock the full potential of their data and drive innovation. Implementing a data catalog will define data and expose data, and its characteristics through a universal feature of SEARCH, to everyone in organization.

Ease availability of data

An internal marketplace is a strategic solution and an effective means to democratize and exchange data within a firm. The data is available to access through an internal marketplace, which provides a centralized repository of available data assets for analysts to use. However, a marketplace cannot function properly by giving away free access to all the data in the system. It requires active management of data management controls. These include privacy, security, authentication, encryption, entitlements, user access management, device management, and data rights management. These controls can be actively managed as metadata in a data dictionary.

Exchange data efficiently

Data contracts enable the formalized and – if required – automatic exchange and delivery of data to the data consumers when integrated with ETL/ELT or virtualization capabilities. They are formal agreements between a data provider and a data consumer, abstractly describing the structure, format, characteristics, and schema of the data. Data virtualization seamlessly integrates and presents data from various sources without moving the data. These contracts establish guidelines and rules for data sharing, storage, deletion, or archival, while also ensuring that the data is reliable and high-quality, and can be trusted by all parties involved.

Innovate through data products

Data products are applications or services that deliver insights, predictions, or recommendations. Innovate by building data products that solve real-world problems or enhance user experiences. Examples: customer masters, personalized recommendations, fraud detection models, or supply chain optimization tools. From an average of 21 days to provision data for a product, a data product can have data provisioned in four hours that results in significant cost savings.

Why Is a Data Fabric Important?

Search and coverage of data

The importance of data coverage for building data products cannot be overstated. Finding data, understanding where it is stored, realizing where it is located physically, and analyzing it for trustworthiness and qualification can take anywhere from two hours to a couple of days. Finding data and processing data for modeling outcomes is crucial for all data science and artificial intelligence use cases. The earlier activity of finding data can be a time taking process, where bias needs to be avoided. Understanding where data resides – both physically and logically – is essential. Analyzing data for trustworthiness is crucial before incorporating it into a data product.

Seamless integration of heterogeneous data sources across platforms without physical data movement

Physically moving data can be tedious, involving planning, modeling, and developing ETL/ELT pipelines, along with associated costs. However, a data fabric abstracts these steps, providing capabilities to copy data to a target database. Analysts can then replicate the data with minimal planning, reduced data silos, and enhanced data accessibility and discovery. Data fabric is an abstracted semantic-based data capability that provides the flexibility to add new data sources, applications, and data services without disrupting existing infrastructure.

Scalable and flexible solution to address future growth needs

Data fabric seamlessly handles growing datasets, and applications while also ensuring optimal performance. As the data volume increases, the fabric adapts without compromising efficiency. Data fabric empowers organizations to leverage multiple cloud providers. It facilitates flexibility, avoids vendor lock-in, and accommodates future expansion across different cloud environments.

Self-service data access layer for end users

Self-service data access typically involves tools or platforms that allow end users (such as analysts, business users, or data scientists) to explore, query, and retrieve data independently. While the data fabric itself isn’t a self-service layer, it sets the foundation by ensuring data availability, quality, and consistency. However, some integration tools that provide the capabilities of a data fabric provide self-service capabilities

A holistic view of data – enhanced analytics and insights

A data fabric extends from a catalog and business glossary as foundational capabilities. They provide a complete view of the characteristics of a business term, as well as its lineage and certain insights into its usage across processes including the operational aspects that involve data personnel like stewards and owners.

Data fabric ensures that data quality, lineage, and governance are considered, reducing the risk of using unreliable data

By maintaining high-quality data, organizations reduce the risk of making decisions based on erroneous or incomplete information. A data fabric can actively monitor and enforce data quality rules that are simplistic. It can perform data profiling and validation on samples of data to give a high-level view of data and its quality. Data fabric integrates governance policies, access controls, and metadata management. Effective governance of data maximizes value and minimizes the risk of unauthorized access, data breaches, and non-compliance. It ensures that data complies with regulations, security protocols, and privacy requirements.

Understanding Data Fabric

Data fabric is an architectural approach that treats data as a strategic asset. The focus is on creating a logical layer of data, providing a unified framework for managing, accessing, and analyzing data across various sources, formats, and locations. The key components of a data fabric include:

1. Data Integration: Data fabric seamlessly connects disparate data sources such as databases, APIs, cloud data services, and legacy systems, eliminating data silos and ensuring a holistic view of data semantically irrespective of the type of storage.

2. Scalability and Flexibility: Data fabric allows organizations to scale horizontally and adapt to changing data requirements. It accommodates both structured and unstructured data, making it ideal for mergers and acquisitions scenarios.

3. Cost Reduction: Centralizing data management through data fabric helps organizations reduce operational costs associated with maintaining multiple data pipelines. It optimizes resource utilization and minimizes redundancy.

4. Advanced Insights: Leveraging AI and machine learning, data fabric enables predictive analytics, anomaly detection, and personalized recommendations. It empowers data scientists and business analysts to extract valuable insights. The focus is on creating a logical layer of data, providing a unified framework for managing, accessing, and analyzing data across various sources, formats, and locations. The key components of a data fabric include:

Data Fabric in M&A Scenarios

During mergers and acquisitions, integrating data from different entities is a complex task. Data fabric simplifies this process by:

Harmonizing Data: It assists in analyzing data from different storages, and assists in migrating data into a common format, ensuring consistency and compatibility across systems.

Reducing Redundancy: Data fabric eliminates duplicate storage of data by bringing commonality thus minimizing storage costs and improving data quality.

Accelerating Decision-Making: Real-time access to integrated data or virtualized data or metadata allows organizations to make informed decisions during critical M&A phases.

Automated Data Governance: AI-driven algorithms enforce data quality, entitlements, contracts, privacy, and compliance policies.

Predictive Analytics: GenAI models analyze historical data to predict future trends, enhancing strategic planning.

Personalization: AI tailors customer experiences by understanding preferences and behaviour patterns such a trust on data elements from consumers and from data profiles.