Articles about Data Virtualization

In a perfect world, all your data would be stored in an updated, organized database or data warehouse where your business intelligence and analytics teams could keep your company ahead of the competition by accessing the precise data they need in real time. In reality, as your organization has grown, your data has probably been stretched across multiple locations, including outdated databases, localized spreadsheets, cloud-based platforms, and business apps like Salesforce. This not only causes costly delays in accessing information, but also impacts your teams’ ability to make informed, data-driven decisions related to both day-to-day operations as well as the long-term future of your organization. So, how do you improve access to your data when it’s siloed in multiple areas? Data virtualization, while still fairly new, is an efficient, effective data delivery solution that offers real-time access to the data your teams need, and it is rapidly growing in popularity among large to enterprise-level organizations. While the market was estimated at $1.84 billion in 2020, a 20.9 percent CAGR has the data virtualization market projected to go beyond $8 billion by 2028 according to a 2022 Verified Market Research report. To help you determine if data virtualization solutions are the best option for your company, we’ll take a look at what data virtualization is, how it can solve your greatest data challenges, and how it stacks up to other data integration solutions. Understanding data virtualization First, what is data virtualization? When you have data housed across multiple locations and in various states and forms, data virtualization integrates these sources into a layer of information, regardless of location or format, without having to replicate your information into new locations. While this layer of data is highly secure and easily managed within governance best practices, it allows the data consumers within your organization to access the information they need in real time, bypassing the need to sift and search through a variety of disparate sources. Data virtualization supports your existing architecture Data virtualization does not replace your existing data architecture. Instead, it’s a single component in a larger data strategy, but it is often essential in executing the strategy successfully and meeting the goals of your organization. Think of your current data architecture as an old library where your data is kept on a variety of shelves, over multiple floors, and some of it is even stored in boxes in the basement. When you are looking for specific information, you have to go on an exhaustive, lengthy search, and you may not even find what you need. Data virtualization acts as the librarian who understands the organizational system, knows exactly where everything is located, and can provide you with the information you need immediately. Choosing data virtualization vs an ETL solution When reporting is delayed, analytics are inaccurate, and strategic planning is compromised due to bottlenecks, it’s essential that your organization prioritizes how data is integrated and accessed. Traditionally, organizations would only choose Extract, Transform, and Load (ETL). ETL is an intensive process in which all your data is duplicated from the original sources and moved into a data warehouse, database, or other storage. While ETL can bring your data together, there are two key problems with this method. The cost of moving and relocating data is often the chief concern most organizations have. And while it does improve your collection of data by keeping it siloed in one location, it doesn’t improve your connection to analyzable data that is needed to improve day-to-day operations. On the other hand, data virtualization solutions streamline how you access and connect to your data. Your business users submit a query, and the Denodo data virtualization platform pulls the data from across locations, extracts the relevant information and delivers it in real time in the needed format so it’s ready to analyze and use. The result? Increased productivity, reduced operational costs, and improved agility among business users while your architects and IT teams have greater control on governance and security. Take a deeper dive into data virtualization solutions Ready to dig deeper into data virtualization? We partnered with data management leader Denodo Technologies to put together Modernizing Integration with Data Virtualization, a highly informative webinar to help you learn how data virtualization helps your company save time, reduce costs, and gain better insight into your greatest asset. To learn how Fusion Alliance can create custom data virtualization solutions to scale your data management and improve access, reach out to our team. Ask us any questions or set up a quick call to explore your options. Learn more about modern data platforms >>

Whether your data is housed in a monolithic data architecture or across multiple, disparate sources such as databases, cloud platforms, and business applications, accessing the specific information you need when you need it probably presents a huge challenge. The length of time it takes to find data may have you or your analytics teams constantly relying on outdated information to run reports, develop strategies, and make decisions for your organization. If you’re exploring data solutions that will improve time-to-market while simplifying governance and increasing security, you’ve probably come across the terms “data fabric” and “data mesh,” but you may not know how to apply them to your business. To help you better understand these emerging trends in data architecture, we’re digging into what a data fabric and data mesh are and the specific benefits they bring to large and enterprise-level organizations. This will give you the foundational knowledge to determine how to choose data fabric vs data mesh or how both may be able to serve your organization. What is data fabric? When you think of every bit of data in your organization as an individual thread, it makes sense that it takes so long to access specific information. If thousands of individual threads are stored together in a bin, like in a monolithic architecture, or separated across hundreds of individual boxes with little to no organizational method, like in a distributed architecture, how long would it take to find the single thread you’re looking for and get it untangled so you can use it? A logical data fabric solves this problem by weaving all the threads of data together into an integrated, holistic layer that sits above the disparate sources in an end-to-end solution. Within the layer are multiple technologies working together to catalog and organize the data while machine learning and artificial intelligence are implemented to improve how new and existing data are integrated into the fabric as well as how data consumers access it. Are data virtualization and data fabric the same? A common misconception is that data virtualization and data fabric are the same. On the surface, they both support data management through the creation of a single, integrated layer of processed data atop distributed or unstructured data. Data virtualization is an integrated abstraction layer that speeds up access to data and provides real-time data returns, and this technology is a key component within the data fabric. However, data virtualization is still only one of the multiple technologies comprising the entity, which is a more comprehensive data management architecture. Benefits of data fabric Now that you have a better understanding of what data fabric is, let’s consider the problems it solves and why it may be right for your organization. Access your data faster When your data is in multiple formats and housed in a variety of locations, gaining access to the specific details you need can take hours, days, or even weeks, depending on your architecture. A logical data fabric leverages metadata, semantics, and machine learning to quickly return the needed data from across multiple sources, whether it’s a large amount of historic information or highly specific data used to drill down into a report. Democratize your data Data fabric uses advanced semantics, so the data is accessible in the language of business users, such as BI and analytics teams. Data consumers within the organization can access what they need without having to go through data engineers or the IT department, eliminating bottlenecks and sharing ownership of data. Improve governance Because of the automation capabilities of data fabric, you can implement a governance layer within the fabric. This applies global policies and regulations to data while allowing local metadata management to reduce risk and ensure compliance. What is data mesh? Monolithic data architecture keeps data in one centralized location. On paper, this seems like a more cost-effective, efficient option compared to a distributed architecture, but it still brings several challenges. Consider that in many large organizations relying on a monolithic architecture, massive volumes of unstructured data are stored in a data lake. For information to get into the hands of data consumers or before productization can occur, the data must be accessed and processed through the IT department, creating significant bottlenecks and bringing time to market to a crawl. A data mesh can solve this challenge. This is a new type of data architecture, only proposed in 2019 by Zhamak Dehghani of Thoughtworks, in which a framework shifts data from a monolithic architecture to a decentralized architecture. More specifically, the data is distributed across autonomous business domains where the data consumers own, manage, and share their own data where they see fit. While the domains are given a separate virtual schema and server so they can have full ownership over data productization, governance, security, and compliance are still unified within the monolith. Benefits of data mesh The challenges of centralized data ownership include latency, added costs of storage, software, replication, and lack of practical access for consumers, but implementing a data mesh can solve these. Eliminate IT bottlenecks When all data is forced to go through the IT department before being distributed to the individuals or teams requesting it, bottlenecks occur and slow down the flow of data. A data mesh allows data to bypass the IT department, allowing data to flow freely to the needed source. Improve flexibility and agility Finding specific information within the massive volume of unstructured, undefined data stored in a data lake requires increasingly complicated queries to get the needed information. However, a data mesh gives ownership of datasets to individual teams or business owners, simplifying access and offering real-time results through scalable, automated analytics. Increase connection to data By transferring data ownership to the data consumers, those who use it directly have a greater connection to it. The data is available in the language of business, and it can be shared across teams with greater ease and transparency. Choosing data fabric vs data mesh Data fabric and data mesh both support data democratization, improve access, eliminate bottlenecks, and simplify governance. While data fabric is built on a technology-agnostic framework to connect data across multiple sources, data mesh is an API-driven, organizational framework that puts data ownership back in the hands of specific domains. So, which is better in the debate between data fabric vs data mesh? The simple answer is neither one is better than the other, and the right option is determined by the use case. If the goal of your organization is to streamline data and metadata to improve connection and get real-time results across multiple teams, a data fabric built on a data virtualization platform can help you meet your goals. On the other hand, if you need to improve the process of data productization and decentralizing your data, a data mesh may be the best option. But the real answer is that contrary to popular belief, the two are not mutually exclusive and most businesses succeed by implementing both options. Data fabric and data mesh are complementary solutions that can work together to solve the challenges of your existing architecture. Learn more about data fabric and data mesh Want to gain further insight into choosing data fabric or data mesh? We partnered with data management leader Denodo Technologies for a recorded webinar. In Logical Data Fabric vs Data Mesh: Does It Matter? we provide an in-depth look at monolithic and distributed data architecture, the challenges they bring, and how both data fabric and data mesh can improve agility, reduce costs, and elevate the quality of your data. To ask additional questions or learn how Fusion Alliance can help you create and implement a successful data strategy to meet your unique challenges and goals, connect with our team today. Learn more about modern data platforms >>

Today’s businesses collect more data than ever before, but many don’t have the architecture in place to store, process, and recall the data in real time. Whether an enterprise-level organization stores all its data in a single data lake or relies on multiple, disparate sources, both options cause significant delays in finding the specific information you’re looking for. Traditionally, if your organization wanted to update and upgrade the existing architecture, the only option was extract, transfer, and load (ETL) the data to a new framework but implementing a logical data fabric offers a better alternative — giving companies a cost-effective, efficient way to collect and integrate data while building a stronger framework across the organization. At a recent CDO Data Summit, Mark Johnson, Fusion Alliance Executive Vice President and editorial board chair for CDO magazine, sat down with thought leaders in the data industry to discuss why logical data fabric is essential in accelerating time to value. What is a logical data fabric? When you have multiple disparate data sources, a data fabric acts like a net cast over the top, pulling individual information sets together in an end-to-end solution. Data fabric is a technology-driven framework that lies within the existing architecture, unlike a data mesh, which is a methodology regarding how data should be distributed among data owners and consumers. In a logical data fabric, multiple technologies are implemented to catalog and organize existing data and integrate new data into the fabric. Data virtualization is the central technology deployed within this framework, creating an abstracted layer of unified data that is more secure and easily accessible. What challenges are solved by a data fabric architecture? Logical data fabric architecture offers a solution to the challenges organizations relying on numerous data storage solutions or repositories of structured and unstructured data face: Overcome slow data delivery By consolidating data into an integrated semantic layer, common business applications can process, analyze, and return the data in real time, in the language of the data consumer. This improves accessibility and significantly reduces latency that comes from applications having to search across multiple sources to return information. Simplify governance If every data warehouse, database, and cloud-based platform within your organization relies on separate governance, you are dealing with significant inconsistencies. By stitching the data together in a logical data fabric, centralized governance can be applied across all data and automated to maintain and streamline the process. Reduce IT bottlenecks Data fabric automates how data is processed, integrated, governed, and utilized, enabling real-time analytics and reporting. This puts data in the hands of your BI and analytics teams more quickly while removing bottlenecks from your IT department. With a logical data fabric architecture, your business can respond to trends and changes within your industry more quickly, helping you to evolve both short and long-term strategies to reflect what your data is telling you in real time. Is a logical data fabric the right solution for your organization? Learn more about data fabric architecture from the CDO Data Summit’s round table discussion. Mark Johnson is joined by: Baz Khauti, President at Modak USA Richie Bachala, Principal, Data Engineering at Yugabyte Ravi Shankar, SVP and Chief Marketing Officer at Denodo Saj Patel, VP of Data Solutions at Fusion Alliance This panel addresses critical questions about data in today’s business to help you solve your unique data challenges, including: Is the fabric of data virtual, physical, or both? How do we get value out of our data? Do we take a connect or collect approach? How comprehensive do we need our data approach to be? Are we optimizing for agility or for flexibility? How do we deliver unified data? Is the organization in agreement with what we are looking for out of their data? What AI/ML techniques do we want to employ, if any? If you have specific questions or are ready to take the next step and learn how we can help you create custom data solutions for your organization, reach out to us today for a quick chat! Learn more about modern data platforms >>