What is an Enterprise Data Hub? By Lewis Carr October 25, 2021 When managing big data, organizations will find that there will be many consumers of the vast amounts of data, ranging from applications and data repositories to humans via various analytics and reporting tools. After all, the data is an expression of the enterprise, and with digital transformation, that enterprise is increasingly expressed in the form of applications, data and services delivered. Data that is structured, unstructured, and in various formats become sources and destinations of exchanges between functional units in the organization that are no longer just done manually or with middleware but can now be hosted collaboratively utilizing data lakes, data warehouses, and enterprise data hub technologies. The choice of which data management solution to use depends on the organization’s needs, capabilities, and the set of use cases. In many organizations, particularly large or complex ones, there is a need for all three technologies. Organizations would benefit from understanding each solution and how the solution can add value to the business, including how each solution can mature into a more comprehensive higher-performing solution for the entire organization. What is Enterprise Data Hub? An Enterprise data hub helps organizations manage data directly involved – “in-line” – to the various business processes, unlike data warehouses or data lakes, as they are more likely to be used to analyze data prior to or after use by various applications. Organizations can better govern data consumption by applications across the enterprise by passing it through an Enterprise data hub. Data lakes, data warehouses, legacy databases, and data from other sources such as enterprise reporting systems can contribute to governed data that the business needs. Besides data governance protection, an enterprise data hub also has the following features: Ability to make use of search engines for enterprise data. The enablement of search engines acts as filters to allow quick access to the enormous amounts of data available with an enterprise data hub. Data Indexing to enable faster searches of data. Data Harmonization enhances the quality and relevance of data for each consumer or data, including improving the transformation of data to information and information to knowledge for decision making. Data integrity, removing duplication, errors, and other data quality issues related to improving and optimizing its use by applications. Stream processing binds applications with data analytics, including simplifying data relationships within the enterprise data hub. Data Exploration increases the understanding and ease of navigating the vast amount of data in the data hub. Improved Batch, Artificial Intelligence, Machine Learning processing of data because of the features listed above. Data Storage consolidation from many different data sources. Direct consumer usage or application usage for further processing or immediate business decisions. Enterprise data hubs can support the rapid growth of data usage in an organization. The flexibility in using multiple and disparate data sources is a massive benefit of selecting a data hub. Leveraging the features mentioned above increases this benefit. Difference Between Enterprise Data Hub, Data Lake, and Data Warehouse Data Lakes are centralized repositories of unorganized structured, and unstructured data with no governance and specifications for organizational needs. The primary purpose of a data lake is to store data for later usage though many data lakes have developer tools that support mining the data for various forward-looking research projects. A Data Warehouse organizes the stored data in a prescribed fashion for everyday operational uses, unlike a data lake. Data Warehouses can be multitiered to stage data, transform data and reconcile data for usage in data marts for various applications and consumers of the data. A data warehouse is not as optimized for transactional day-to-day business needs as an enterprise data hub. In addition to drawing data from and pushing data to various enterprise applications, an Enterprise data hub can use a data lake, data warehouse, and other data sources as input into or as destinations from the data hub. Once all the data is available for the hub, the aforementioned features, such as governance, can be applied to the data. Enterprise data hub vs data lake can be easily differentiated based on the data hub’s additional capabilities for processing and enriching the enterprise data. Enterprise data hub vs data warehouse can be confusing, but the data hub has additional capabilities for using the data more business process-oriented rather than business analytics-oriented operations. Enterprise Data Hub Architecture The following diagram shows an Enterprise data hub architecture that includes multiple data sources, the hub itself, and the data consumers. The Enterprise data hub Architecture is designed for the most current needs of organizations. The architecture itself can grow to accommodate other data management needs, such as the usage of data in emerging technologies for decision support and business intelligence. In Summary With the increasing adoption of disparate data and Big Data practices, Enterprise data hubs are becoming the architectures to create a unified data integrated system to enable better business processes across the enterprise. Enterprise data hub can utilize data for any source and type to create a single source of data truth about the organization’s customer, service, and products. This single source of truth can be used collaboratively across the organization to share data for timely, higher-performing business operations, automation, and decision-making. Organizations with data hubs and supporting data sources can become more competitive than those that do not. Data is the lifeblood of the organization that enables optimized and automated business processes and decision support for organizations to make better decisions. This capability is well worth the time and investment for the organization. Actian can help you with your cloud data integration challenges. Actian DataConnect is a hybrid integration solution that enables you to quickly and easily design, deploy, and manage integrations on-premises, in the cloud, or hybrid environments. About Lewis Carr Senior strategic vertical industries, horizontal solutions, product marketing, product management, and business development professional focused on Enterprise software, including Data Management and Analytics, Mobile and IoT, and distributed Cloud computing.