As more and more companies take advantage of data for their decision-making, there is a lot to comb through in data platforms that some business owners may feel lost in. A business owner may feel lost in a soup of terms from data silos to data lakes and machine learning to cloud environments. While it’s best to hire a data management team to look into analytics and data architecture for you, it never hurts to understand what data can do. Let’s look at what makes up two of these terms: data fabric and data virtualization.
What is data fabric?
Data fabric is an end-to-end data integration and management solution that consists of architecture and integration software, allowing organizations to share data through their networks safely and efficiently. A data fabric provides a consistent user experience and access in real-time, helping solve complex data problems and use cases by funneling through mountains of big data to create insights to move a company forward steadily throughout all of its business units.
Any data-centric organization will face initial hurdles in developing business intelligence due to the volume of data they are dealing with or the data quality and formatting present in this mountain of information. Data fabric provides a secure, future-proof solution for advanced analytics. Traditional data integration no longer meets the demands of real-time connectivity and automated processes.
Companies struggle to process and curate diverse data to view their customer bases and their production comprehensively. Data fabric allows businesses of any size to harness the power of cloud computing, letting business users modernize systems to enhance business value and eliminate constraints.
What is data virtualization?
Data virtualization is often intertwined with data fabric, so it’s important to understand data fabric vs. data virtualization. Virtualization lets data scientists abstraction layer that is relied upon to integrate data assets quickly, allowing data management teams to gather and transform this data product from different sources to garner real-time insights. These data sources can be connected to either on-premises or cloud services. Meantime, data fabric refers to an overarching management architecture used for broader use cases like customer intelligence.
Data analysts recommend using data virtualization as one tool to contribute to data fabric architecture. Data virtualization software provides a modern data layer that enables users to access, combine, and deliver datasets at a breakthrough speed and in a cost-effective fashion. This technology can be grown in a self-serve data infrastructure platform, combining traditional databases with cloud systems. These data science platforms help companies with their overhead costs, as these cloud-based systems operate at a fraction of the cost of a physical data warehouse.
Implementing Data Fabric Architecture
Data fabric begins with online transaction processing, which lists detailed information about every transaction in a single place for shared use. This information is then inserted, updated, and uploaded to a database. The data is structured, cleaned, and stored within a data hub for future use. A company can derive multiple insights from raw, unstructured data to help organizations leverage massive amounts of data into a method that works for product development, market research, and even corporate expansion.
Data fabric architecture includes developing apps and user interfaces that allow for customer interaction and transparency with a company. Creating this ecosystem for data better caters to customer needs and lets a data management team better grasp the use of data. A properly structured data fabric collects from all sources safely and securely but makes sure that data sharing is accessible at any scale. This software-defined infrastructure at data storage and access points allows for insights in real-time, getting ahead of the competition.