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Do you want to unlock the full potential of your data to boost your business and reach a strong market position? Data fabric technology will come to the rescue. Find out all about data fabric in our article.
So, what is data fabric?
Data fabric is a self-contained architecture for data management that provides flexible access through a hybrid cloud, making it easy to search, process, structure, and integrate. This is one of the most advanced DataOps practices and one of the trending solutions for cloud systems in general. Data fabrics accelerate digital transformation and automation initiatives across businesses.
To implement data fabric, specialists usually use a microservice architecture, with inherent orchestration and data virtualization. Also, this concept is friendly with machine learning, artificial intelligence, and data science at all stages of the implementation of this architecture—from data structuring to processing. When it comes to data integration, data fabric uses an API.
As an upgrade or addition to an existing IT infrastructure, data fabric provides a fast response to any changes in data, improves the quality of predictive data analysis, and also simplifies data maintenance.
In essence, instead of centralizing data, this architecture cleans up data sources and storage. This is made possible by creating a new layer of data virtualization through which users can access this data. Thus, data fabric doesn’t require replacing the existing infrastructure but instead adds a new technology layer on top of it for managing and accessing data.
Since data fabric is not something physical, it’s better to understand what it is by focusing on the basic principles of this concept. So, let’s discuss the five main principles of data fabric implementation.
Initially, data fabric is a network architecture that provides simple and fast integration of data pipelines and cloud environments through advanced technologies. As a rule, DataOps specialists use various automation solutions based on machine learning and artificial intelligence to carry out end-to-end integration of all information sources (including file storage, DBMS, and data lakes) into a single information system via APIs.
In the context of data fabric implementation, this principle provides new capabilities for linking disparate data sources. A typical example of its usage is the connection between supply chain software and CRM, to optimize the process of delivering goods to the end consumer and increase the level of customer loyalty to the company.
Data fabric ensures the unification and efficient governance of disparate data, no matter how rapidly the IT infrastructure expands. At the same time, this technology can maintain the same level of data security without increasing the risk of data leakage.
When implementing data fabric, data curation means seamlessly integrating and structuring disparate data, preserving its value over time. This means that the collected data can be reused.
Data orchestration that accompanies the deployment of data fabric is the process of bringing together disparate data sources to use that data for global analysis. As a rule, special tools like Kubernetes take part in the implementation of this process.
Thus, data fabric is an approach for implementing DataOps processes, providing a rapid response to events, a high level of predictability, process optimization, and resource maintenance. Using the full potential of cloud technologies to the maximum and virtualizing all components of the IT infrastructure, data fabric allows DevOps and other teams to access data in ways they prefer.
The main challenge that data fabric copes with is the constant increase in the amount of data. An improperly designed infrastructure can stall business processes due to the inability to scale. Data fabric helps companies unlock the full potential of data to meet their needs and gain market advantage.
In particular, one of the key benefits of data fabric is the elimination of “piecewise-continuous” data processing functions. The main problem is to combine different systems into a single ecosystem, where each will have its own workload and scaling parameters. However, this approach alone doesn’t solve the main problem, as the data is still in different places.
In addition, there are high costs for IT operations as companies have to manage a large number of systems. Data has to be copied between systems, as well as transformed. All this leads to the appearance of numerous copies, often contradicting each other and requiring additional synchronization. Using data fabric with artificial intelligence or/and machine learning is key to breaking down dependency on separate formats and sources, helping companies migrate their applications to a common platform that combines both data and the tools to work with it.
For example, when processing information, machine learning is provided at every stage, from the analysis of the received data to the optimization of processing algorithms. Used in conjunction with data fabric, this technology allows users and analysts to quickly access trusted data for applications, analytics, and business process automation. In the future, this will improve the quality of decisions and accelerate the digital transformation of the company.
The use of ML and/or AI with data fabric directly depends on the types of data: it can be image analysis using neural networks, text parsing, accident prediction at key nodes of enterprises, or intermediate obtaining of key data features for further analysis. Here it’s important to understand how justified the use of intelligent technologies is. As practice shows, the best option is a symbiosis of the work of AI and ML and a mathematical model, which together allow companies to get the best result.
Now let’s talk about when data fabric can be useful:
In all the above examples, companies face numerous data sources with different structures, supported data types, and localizations (cloud services, local data centers, etc.). In this case, traditional data centralization approaches are no longer effective, requiring too many resources to implement and maintain.
Data fabric gives companies great capability to solve these problems not partially or gradually, but completely and at once, without the need to create several different ways of managing data all within one business. It optimizes the ability to reuse data in any of the systems maintained by the company.
Also, data fabric allows corporations to implement a pipeline for their digital projects, reducing the time to market for creating new functionality. Thus, the development of a digital project doesn’t take a year or more, but only a few months.
As a result, enterprise data fabric solutions see higher ROI, rapid scaling, and performance retention.
For operational support of management processes, they need to be collected and processed very quickly. Data fabric allows companies to efficiently store and process disparate and unstructured information, as well as provide it in the right form for decision support systems. Below are six typical enterprise data fabric solutions.
The first trivial case of applying data fabric architecture is the development of applications and services with a microservice architecture and own infrastructure for data collection. Thus, this technology greatly simplifies the procedure of interaction between users and disparate data sources within an application/service.
Data fabric is also useful in building entire ecosystems for collecting, managing, and storing data. In this case, with the correct preliminary modernization of outdated security mechanisms, data fabric allows companies to significantly eliminate risks connected with data leakage and loss.
For companies that need to comply with certain security and privacy policies for user data, data fabric is the best solution. Moreover, this concept determines the possibility of reusing this data in any other solutions created by a particular company.
Unlike centralized solutions that are difficult to scale, merging multiple systems into one with a new layer of virtualized data provides an excellent foundation for further expansion functionality as the company’s business needs grow and change.
Companies with a distributed physical infrastructure have certain challenges in implementing and maintaining standard centralized solutions. In turn, data fabric provides access to data from any physical point where the company’s office is located.
Finally, data fabric is useful in software development for endpoints, providing delivery, structuring, and processing of data in real-time, no matter where these endpoints are located.
Note that this type of architecture has not only benefits. That’s why we offer to teach you about what problems may be faced by companies who decide to implement data fabric.
Even though there are currently a huge number of scenarios for using data fabric—from the financial sector to smart logistics warehouses with the ability to build end-to-end processes for moving equipment—the main stumbling block in the implementation of this technology lies in the unpreparedness of its potential users. Many companies aren’t familiar with this technology, and some of them don’t have the appropriate level of knowledge to apply it, support it, and train their employees.
Also, companies may face some issues with data transport and security. A system that is ready to work effectively with data must be equipped with modern technologies and tools. The use of legacy systems limits performance and scalability, and as a result, the company won’t benefit from a data fabric approach. That’s why some companies have to update their old data transport and security scenarios to effectively implement this new technology concept. You can learn more about business trends in the IT world in our blog.
Marketing research claims that, back in 2018, the size of the data fabric market was estimated to be about 812 million US dollars. According to experts, these numbers will grow by almost a quarter by 2026, up to a record $4,547 billion. Indeed, being perhaps the most trending cloud technology for medium and large businesses, data fabric determines the scalability of existing IT infrastructures of companies without the need for their global restructuring.
In particular, instead of increasing hardware resources by acquiring local or renting remote web servers, enterprise data fabric solutions can virtualize and consolidate all their data, even if they are located in disparate sources.
This means that thanks to data fabric architecture, the amount of financial investments allocated to upgrade existing IT infrastructure is reduced, their payback is increased, and business owners gain the excellent capability to launch their products to the market faster than ever.
If you are interested in other cloud trends, you can visit our blog to learn more.
Data fabric architecture combines existing tools for collecting, processing, storing, and analyzing data. It’s an integrated ecosystem with a single interface and consistent architecture that allows users to access data from multiple platforms without long waits and constant technical coordination with the IT department. In this way, users become less dependent on IT professionals and are able to spend more time analyzing the data, rather than learning how to access it. Also, a correctly-designed data fabric solution reduces the time to market for obtaining the needed data for the end customer.
However, data fabric architecture isn’t a panacea. To increase the efficiency of existing analytical processes within the company, the implementation, and support of this technology require the participation of experienced professionals. If you are just looking for such professionals just contact us. We will transform your IT infrastructure to scale further without having to rebuild it from scratch.
Yevhenii has more than a decade of experience in the software engineering industry and dozens of completed projects under his belt. He has a knack for building cost-effective solutions and processes, and is confident there is nothing impossible with the right approach.
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