Data Fabric Architecture is Key to Modernizing Data Management and Integration

Data management agility has become an essential priority for companies in an emerging distributed, diverse, and complex environment. To decrease human errors and overall costs, data & analytics (D&A) managers need to think beyond legacy data management strategies and move towards modern technologies such as AI-enabled data integration.

The increasing design concept called “data fabric” is a powerful practice to overcome data management challenges like frequent maintenance of earlier consolidation, low-value and high-cost data integration cycles, the rising need for real-time and event-based data sharing and more.

What is Data Fabric?

A data fabric is an upcoming data management design for acquiring reusable, flexible, and augmented data integration services, pipelines, and semantics. They support analytics and operational use cases across several deployment and orchestration processes and platforms. Data fabrics assist in integrating different data integration styles and use active metadata, semantics, ML, and knowledge graphs to augment data integration delivery and design.

Why Data Fabric is Important?

If you’re in search of a modern and effective method for handling your data, using a data fabric could be the ideal solution. This approach enables you to amalgamate data from various sources, offering you a clear and comprehensive real-time perspective of your data landscape. Not only does it streamline the data management process, but it also expedites data processing, empowering you to make quicker and more well-informed decisions.

Data fabrics possess the ability to seamlessly expand to accommodate the ever-growing volume and diversity of data that businesses generate today. This aids in boosting productivity, facilitating better decision-making, and maintaining a competitive edge. Moreover, employing a fabric ensures users have secure and compliant access to high-quality data required for performing their data-related tasks.

Use of Data Fabric in Modern Businesses

Recently, there has been a remarkable surge in the number of businesses, entrepreneurs, and companies entering the networked environment. Ever since the widespread accessibility of the internet, all websites have transformed into valuable sources of data. Consequently, enhancing the value of this data has become of utmost significance. Nevertheless, certain obstacles are impeding the endeavour to increase the value of data, and these challenges can be outlined as follows:

1. Data is stored on different file systems, storage systems, and SaaS applications.
2. There is no universal format for the available data. They are all accessible in various formats.
3. The database includes both structured and unstructured data.
4. Data is stored in various platforms having various scenarios.
5. Data is situated at several on-prem locations and clouds.

These obstacles prove that data is growing at an exponential rate. These challenges make accessing data and extracting insights from it extremely tough. Any company or organization specializing in ML, big data solutions, and AI must gather, organize, and process their data. Many businesses typically address this issue by managing data across warehouse departments, employing diverse methods. However, while this approach works well for multiple teams, a significant amount of data is often overlooked and needs to be tapped for enterprise-wide data access.

The challenges related to data usage and accessibility result in reduced productivity and a shortage of reliable data for deriving insights and making future predictions. The solution to address these issues lies in data fabric. By using data fabric, businesses can effectively gather credible data from their entire network and comprehensively analyze it to extract valuable insights.

Benefits of Data Fabric

The term “Fabric” is defined as the integrated layer of data and interlinking processes across all data units such as multi-cloud and hybrid platforms.

By bringing together AI models, automated technologies, continuous analytics, and machine learning across complex data units, companies can enhance data trust, make effective decisions, and drive digital transformation. Some of the common ways include:

• Business insights: A unified infrastructure enables adequate data visibility- and insights which come from that visibility.
• Governance: Enhanced data access and manage excessive streamlines data management initiatives, giving time back to governance teams.
• Security and cost: A data fabric model allows companies to effectively protect their data and decrease costs from managing and maintaining data- specifically in multi-cloud environments.

How to Implement Data Fabric?

Data fabric starts with online transaction processing, where data linked with all transactions is updated, stored, and integrated into a warehouse over a database. This data is then cleansed, structured, and processed for advanced applications. Anyone with access to the data can use it to derive insights to help the business adapt and grow. The implementation of data fabric requires the following:

1. Applications: The appropriate infrastructure includes GUIs and applications that acquire data. It is designed for customers to communicate with the company.
2. Developing the right environment: It is crucial to create the right environment to gather, manage, and store data.
3. Security: All the data gathered from internal and external sources must be secured.
4. Storage: All the data gathered needs to be stored efficiently and successfully. It should also be easily allowed and accessible to scale when needed.
5. Transport: It is significant to design the proper infrastructure to access data from any place in the company.
6. Endpoints: Construct the right software which would help to extract valuable data in real-time.

Factors Data and Analytics Leaders Need to Know About Data Fabric

• Data fabric is not just an amalgamation of legacy and modern technologies but a design concept which reforms the emphasis on human and machine workloads.
• New and upcoming technologies like embedded machine learning, active metadata management, and semantic knowledge graphs are needed to understand the data fabric design.
• The design enhances data management by repetitive functions like profiling datasets, identifying and aligning schema to new data sources, and at its most advanced, healing the failed data integration jobs.
• A complete data fabric architecture can only be achieved with an independent solution. Data and analytics leaders can establish a robust data fabric architecture by combining internally developed and externally purchased solutions. One approach could involve selecting a capable data management platform encompassing approximately 65-70% of the necessary functionalities for integrating a data fabric. The remaining capabilities can then be supplemented with a custom-built solution.

Conclusion

Data fabric is particularly well-suited for businesses that are geographically dispersed, have multiple data sources, and encounter complex data-related challenges. Nonetheless, it’s crucial to remember that data fabric differs from the need for data processing and integration. To accomplish those tasks, data virtualization becomes essential.

Companies with vast volumes of data can always turn to data fabrics as it offers a real-time flow of data and accessibility, adapts per the requirements, function across all systems, and requires minimum training without interference.

WRITTEN BY

Anjali Goyal

Anjali Goyal is a content writer at TechEela. She helps businesses increase their online presence with optimized and engaging content. Her service includes blog writing, technical writing, and digital marketing.

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