Data Fabric: An established imperative for the digital era

In the rapidly evolving digital world, managing vast amounts of data has become a monumental challenge for organizations. As we navigate through the sea of data management terminologies, it’s crucial to understand the concept of a data fabric and its role in simplifying this complexity. Unlike traditional data management solutions, a data fabric offers a holistic and strategic approach to leverage all existing and future data investments within an organization’s data estate.

What is data fabric?

Data fabric?is an end-to-end data integration and management solution, consisting of architecture, data management and integration software, and shared data that helps organizations manage their data. A data fabric provides a unified, consistent user experience and access to data for any member of an organization worldwide and in real-time.

Data fabric is designed to help organizations solve complex data problems and use cases by managing their data—regardless of the various kinds of applications, platforms, and locations where the data is stored. Data fabric enables frictionless access and data sharing in a distributed data environment.

Implementation of data fabric

Data fabric begins with online transaction processing (OLTP) concepts. In online transactional processing, detailed information about every transaction is inserted, updated, and uploaded to a database. The data is structured, cleaned, and stored in silos at a center for further usage. Any user of the data, at any point in the fabric, can take the raw data and use it to derive multiple findings, helping organizations leverage their data to grow, adapt, and improve.

Successful implementation of data fabric requires:

• Application and services: Where the necessary infrastructure for acquiring data is built. This includes development of apps and graphical user interfaces (GUIs) for the customer to interact with the organization.
• Ecosystem development and integration: Creating the necessary ecosystem for gathering, managing, and storing the data. Data from the customer needs to be transferred to the data manager and storage systems in a manner that avoids loss of data.
• Security: The data collected from all sources is to be managed with proper security.
• Storage management: Data is stored in an accessible and efficient manner, with an allowance to scale when required.
• Transport: Building the necessary infrastructure for accessing the data from any point in the organization’s geographic locations.
• Endpoints: Developing the software defined infrastructure at the storage and access points to allow insights in real time.

How does artificial intelligence or machine learning work with data fabric?

In the initial phases of data storage, data engineers and?data scientists?tried to connect the dots in data to find patterns. They found that with traditional data integration techniques, they were spending most of their time in data logistics rather than learning about the data. This is not sustainable if we want to get to insights faster.

A data fabric is essentially a data operational layer that not only brings all the data together, but transforms and processes it using?machine learning?to discover patterns and insights. Without a data fabric, all of this has to happen in each individual application, which is not a very sustainable solution.

A data fabric can prepare data to meet the needs of AI and ML automatically and at sustainable levels. Machine learning can provide the data and insights proactively, helping decision-makers have better insights and more timely information. The desirable outcomes lie in discovering hidden facts from the data without being specifically looked for or requested, while finding solutions for problems or business insights.

Data Fabric Advantages

Data fabric offers many advantages over alternative data management approaches, such as master data management, data hubs, and data lakes, including:

• Enhanced data management Allowing data to be retrieved, validated, and enriched automatically – without any transformation scripts, or third-party tools
• Expanded data services Using innovative engines to manage and synchronize data with full support for SQL, and an embedded web services layer
• High consistency, durability, and availability Meeting enterprise standards, with a trusted database layer and processing engine
• Excellent performance Relying on an architecture capable of running every query on a small amount of data, and in-memory processing
• Tight security Eliminating the possibility of mass data breaches, due to a sophisticated, multi-key encryption engine

Knowi - Data fabric based Unified Analytics Platform

Knowi is the first analytics platform that natively supports and understands both traditional and non-traditional structures. Using structured data, data engineers may quickly see insights without moving or transforming the data first. By retaining existing data and obviating the necessity for data and ETL prep, Knowi dramatically cuts the price, time to market, and the difficulty of your analytics initiatives.

Knowi is a part of the new wave of analytics tools designed specifically for contemporary data stacks. Knowi establishes a native connection to your data source(s), runs a schema-on-read to rapidly identify data, and enables you to begin displaying your data using more than 30 visualization options in minutes.

Knowi securely offers enterprise business intelligence to everyone, everywhere, at any time, with options for cloud, on-premise, or hybrid implementation. All in all, Knowi is a platform for unified analytics without the requirement for unified data. It does away with conventional ETL and the need to keep raw data in a data warehouse. It can connect data from several sources and communicate in real time with any data source natively, including SQL databases, NoSQL databases, cloud services, and APIs. Additionally, Knowi offers BI capabilities and analytics based on natural language search.

Conclusion

In conclusion, understanding and implementing a data fabric is critical for organizations looking to navigate the complexities of modern data management. By providing a strategic and holistic approach to leveraging data, a data fabric enables businesses to access, manage, and utilize data more effectively, driving innovation and improving customer experiences. As the digital landscape continues to evolve, the role of data fabrics will only grow in importance, making it an essential concept for data-driven organizations.