6 Features, Key Challenges, and Applications of Open-Source Data Fabric Platforms

Abstract

In recent years, “Data Fabric” has become new analytic buzzword in Data Management agility where it has become a high priority in booming industries where they have an environment that is more complicated, scattered, and diversified. Data analytics experts began exploring beyond conventional data management techniques and shifted toward contemporary solutions like AI-enabled data integration in order to decrease human errors and total costs. Data fabric is a weave where it stretches spanning a wide area that connects numerous sites, various data source kinds, and accessing techniques. As it progresses through the various stages of the data fabric, the data collected from the source can be handled, processed, and stored. For a wide range of applications, the data can also be accessible by or shared with both internal and external apps. Data fabric applications’ main objectives are to optimize supply chains for end-to-end products, complywith data rules, and enhance consumer engagement through more sophisticated mobile apps and interactions. Always Companies can gain a competitive edge with data, but to meet customer demands, they must supply data rapidly. Most of the enterprises implemented cloud migration and IoT, with increased cost-effective data storage and processing. Because of this data is no longer tied to local centers, and most of the data are located in different places and it is very difficult to manage [1]. A Data Fabric is a strategic solution to the enterprise to incur storage operations and leverages the best version of cloud migration. This architecture can support centrally managed, public and private clouds, IoT and other devices. This reduces management tasks through automation, accelerates the development and deployment process, and protects assets without interruption. It enables changes to be made quickly, resolving problems, managing risk, reducing IT operations and complying with regulations. In this chapter, the best open source data fabric tools that meet the enterprise requirements are listed and highlighted with its benefits and challenges. The greatest data fabric tools are profiled in one location, which makes it easy for researchers to choose the tool throughout their search. Data categorization and discovery, data quality and profiling, data lineage and governance, and data exploration and integration are the four main functions offered by the data fabric technologies. These data collaboration platforms combine data integration with business applications. Atlan, Cinchy, Data.world, Denodo, IBM, K2 View are few open source tools that are used by enterprise to manage their data and its integration. There are wide ranges of Open Source Data fabric tools that are quick to list its benefits. Instead of using proprietary systems, the majority of firms are interested in open source solutions due to lower costs. The capacity to modify and offer creative solutions on the code to satisfy business objectives is another crucial advantage of working with open source proponents. However, in this chapter we discuss about the Key features, benefits and technical challenges of different open source data integration tools in detail. The primary challenges in the utilizing open source data tool in enterprise is they lack in community support. In general The IT departments of many businesses rely on vendor support to enhance their internal capabilities [2]. Having open source tools, make the enterprise to face and resolve the issues by their own, which is very hard. When developing a data management environment, technology teams frequently underestimate the amount of time and expertise required to properly employ open source software. Most of the organizations they frequently underestimate the amount of work necessary to integrate open source with other subsystems and, as a result, incorrectly evaluate the total cost of ownership of employing open source systems. Most businesses meet few significant obstacles when working on open source pilot projects, but they may run into problems when attempting to manage and maintain those deployments on a wide scale.