Gathering actionable insights and information from raw data is an essential aspect of today’s data-driven world. Data management & analysis has become a priority for organizations as data has become increasingly diverse, distributed and complex. Hence, analysts and data scientists need to leverage traditional techniques and switch to modern practices such as artificially intelligent systems. A new and rapidly growing concept known as “Data Fabric” has emerged in recent years to aid such challenges.
What is Data Fabric?
Data Fabric is an all-in-one integrated architectural layer, i.e., fabric, that connects data and analytical processes. Data Fabric uses existing metadata assets to support the design, deployment and proper utilization of data across all environments and platforms. The concept aims to accelerate the inference of insights from data through several different automated processes. It can help many use cases and provide real-time insights, in turn managing the data flow and curation from all data sources. A data fabric integrates processes such as data integration, analytics, and dashboarding all into one and serves as a management solution. It enables a consistent user experience and instant access to data for any member in an organization in real-time, allowing frictionless access in a distributed environment.
Need for Data Fabric
In a data-centric environment, data needs to be instantly accessible to users who need it while being present in a secure and efficient environment. The traditional data integration methods do not meet the demand of dynamically changing businesses, and therefore, automated and self-efficient systems have been in demand. The data management processes today are required to deliver a comprehensive view for customers and products. A data fabric is a potential solution to all this. The term “data fabric” can be described as a cloth spread wherever the organization’s users are. The user could be present anywhere in the world and still have access to data without any real-time constraints. Organizations today need to have their data transformed and processed as a lack of comprehensive data access can often fail to produce useful predictions and desired outcomes. In such conditions, a data fabric comes to the rescue. It leverages both human and machine capabilities.
Key Components of Data Fabric
A data fabric is a combination of several different layers. Here are some of the key components necessary for its proper implementation :
- A well-connected pool of metadata lays the foundation of a Data Fabric Design. It comprises services that allow the data fabric to identify and analyse every type of metadata.
- A data catalogue provides access to all the metadata types through a well-connected knowledge graph. It also graphically depicts the present metadata in an easy to understand manner and creates unique relationships between them.
- Analytics, when connected with knowledge graphs, help in activating the metadata. It enables the graphs to enrich their data with semantics, making it easier for data analysts and scientists.
- Including a set of standard data integration tools ensures integrated data delivery through multiple data delivery styles and helps curate the analysed knowledge graphs.
- The created data fabric should have a robust data compatibility backbone. The fabric should be compatible with various data delivery styles and not be limited to any. Support for different data types ensures its availability for all kinds of users.
Impact of Data Fabric
Data fabric is not just a mere combination of traditional and contemporary technologies but a concept that aims to ease the workloads for humans and machines. The design optimises and leverages data management techniques by helping automate repetitive tasks such as data discovery, data profiling and aligning the data schema to new data sources. It can also help correct faulty data integration tasks and analyse what’s wrong. Data fabric helps an organisation by understanding business demands while gaining an edge over its competitors. Through this technology, the actual potential and power of a hybrid multi-cloud experience can be harnessed to its fullest. Built on a rich set of data management capabilities, it ensures consistency across all integrated environments. It can reach anywhere, be it on-premises, edge and IoT devices, or different cloud types.
Using Artificial intelligence with Data Fabric
Data fabric being an operational layer, not only brings data altogether but also transforms and processes it. Making use of technologies such as artificial intelligence and machine learning can discover patterns and insights. Data fabric can help prepare the data to meet the needs of AI and ML with higher sustainability levels and at an automated pace. Through this, decision-makers can promptly gain better insights — discovering hidden facts that previously would have been overlooked help find more solutions for problems proactively.
Risks and Benefits of Data Fabric
Concern about data fabric is the threat to data security when data is transported from one point to another. Therefore, it becomes a mandatory requirement to embed the system with security firewalls and protocols that ensure safety from breaches. With several cases of cyberattacks hitting organisations, the security of data points becomes a necessary step towards safety.
Data Fabric helps increase connectivity rapidly and is an agile model that can work with all operating and storage systems. No highly expensive investments in hardware or trained staff are required to control one. It provides better accessibility with the real-time flow of data and information.
With data growing exponentially and problems around data multiplying, the use of a data fabric architecture can be a potential solution towards a sustainable data future. A data fabric not only maximises the value of data but also provides a better infrastructure to manage it. Furthermore, it improves and simplifies end-to-end performance not only within but also outside the organisational space.
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Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.