A data fabric is a modern data management architecture that enables the cross-functional integration and connection of different data environments. areto offers data fabric solutions from IBM, Data Virtuality, AWS and Azure.
Many companies try to accelerate access to their data through point-to-point integration or data hubs. However, neither is satisfactory if the data is distributed across multiple locations or resides in silos. In addition, such concepts often create unnecessary complexity, a high cost and a high proportion of data that remains unused. The innovative solution to this problem is called Data Fabric: The architecture creates the virtual connection between different cloud and on-premises environments and ensures that all data is available through an enterprise insights platform. This creates the desired balance between decentralization and globalization.
A Data Fabric simplifies the use of data as a business resource and was redefined by Forrester analysts in mid-2020.
At its heart is a data management platform with a rich set of capabilities that deliver all key insights in real time, regardless of on-premises, cloud or hybrid cloud platforms. The concept, which links data and processes as an integrated layer (fabric), takes a metadata-driven approach.
Data Fabric can leverage existing technologies of data hubs, data lakes, data warehouses and data lakehouses while introducing new approaches and tools for the future. In doing so, Data Fabric architectures reduce the technological complexity for moving, storing and integrating data.
This is enabled by data virtualization technology, as offered by Data Virtuality. Starting with APIs and SDKs in conjunction with pre-built connectors, the Data Fabric can connect to any front-end user interface.
There is no need to move existing data from disparate sources. Instead, the tool connects to these sources and integrates only the required metadata into a virtual data layer through virtual data pipelines. Thus, the data is accessible to users in real time. Artificial intelligence and machine learning can conserve human resources.
Data Fabric helps organizations to meet the needs of users by giving them access to the information they want at the right time, at a low cost, and with end-to-end governance. This makes it much easier to use the data and ensures that it can be retrieved at any time and processed very quickly.
Fast self-service tools and seamless collaboration.
Self-service capabilities enable authorized users to access the data they need, when they need it – even across project teams from different locations.
Automate governance and data protection with active metadata.
AI-powered extraction of regulatory documents ensures fast and accurate implementation of data governance rules and definitions.
Automated data management with improved integration.
With the elimination of inefficient, repetitive, or manual integration processes, the delivery of high-quality data is accelerated – including automated, real-time AI analytics.
* Source: Forrester study “Projected Total Economic Impact” commissioned by IBM (2020); Gartner Top Data Analytics Trends for 2021.
There is no standard data architecture for Data Fabric solutions, as each company has individual requirements for the data layer. This can be applied through a variety of interfaces with tools from a wide range of providers. A large number of companies using this type of data framework have the following commonalities in their data fabric architecture.
*Source: 2021 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates.
Data management layer
Responsible for data security and data governance through easily set policies and classifications.
Data ingestion layer
Beginning to assemble and connect structured and unstructured (cloud) data from, for example, enterprise resource planning (ERP), CRM software, HR information systems, or even external systems such as social media.
Refining and filtering relevant data to ensure that only relevant data is used for data extraction.
Integrating and merging data from disparate sources opens up new opportunities for holistic business decision-making. This is supported by the use of AI and ML algorithms in real time. Analytics and knowledge graph systems are also used.
Transforming, integrating and cleansing data to prepare it for a wide variety of teams across the enterprise. This involves converting all metadata into a consistent format for further processing.
This layer enables the use of data. Embedded analytics in business applications and Virtual Assistants help with data exploration. In addition, this layer supports the use of tools for visualization. Access permissions can also be managed here to adhere to data governance and compliance guidelines.
A Data Fabric is characterized by these components and enables companies on their way to becoming a data-driven company, a great advantage in handling their data and security for the future.
A data fabric architecture provides an integrated approach to data access and use, whether the data resides on-premises in traditional data centers or in the cloud.
The areto example architecture provides a good illustration of the use of Data Virtuality’s data virtualization layer.
Data Virtuality offers a platform that can be used for various architectural approaches and provides a suitable basis for turning these concepts into reality. From Self Service Data Pipelines to Data Fabric, Data Virtuality supports your data management.
In doing so, Data Virtuality relies on data virtualization in combination with automated ETL and a unified metadata layer built on top of it. This enables a dynamic and holistic data management architecture.
The Data Virtuality platform facilitates the movement and exchange of data between different heterogeneous storage systems. Intelligent connectors within the platform support complex processes and optimize extraction and loading operations – even for large data volumes. In addition, AI-based recommendation engines analyze data usage patterns for improved management processes.
The platform provides an integrated metadata catalog – the Business Data Shop, which is searchable and downloadable by all business users. Data consumers can easily access the Business Data Shop through a web browser. Access to sensitive data can be managed through an authorization system, and it is possible to historically track who has had access to which data and who has changed data models.
Data in real time
The virtual layer enables direct access to real-time and historical data from its sources
Virtual data layer means users don’t have to physically replicate sensitive personal data outside of source systems
With over 200 different connectors and associated connector assistants, connections to data sources can be established in minutes (5 minute setup)
An integrated business data store makes data searchable and retrievable with web browsers
Security and Governance
Integrated user- and role-based permissions system with schema-, table-, and column-level granularity
Deploying the IBM Cloud Pak brings tremendous benefits to organizations, enabling the adoption of the AI lifecycle across the enterprise, based on an enterprise insights platform. The packaged solution from IBM combines storage capacity, computing power, networking capabilities and software with plug-and-play convenience. Cloud Pak for Data can be installed on any cloud platform (e.g. Azure, AWS, IBM Cloud) or on-premises for hybrid environments. Red Hat Openshift is placed over the respective environment and thus enables the use of IBM Cloud Pak for Data.
Using IBM Cloud Pak for Data simplifies data science projects and the automated collection, organization and analysis of all data, accelerating enterprise-wide transformation.
With the pre-configured platform, management and support efforts are reduced to a minimum. This leaves significantly more time for your core business and new business concepts.
It only takes a few hours to set up the data and AI platform in the private cloud
Scaling as desired
Intelligent concept with usage-based payment, plug & play and an app store for applications
Full flexibility and scalability in the public cloud behind the customer’s firewall
Simple data management via the easy-to-use dashboard
IT infrastructure optimization
Integrated FPGA hardware acceleration for AI-powered analytics
IBM is named a “Leader” in the Gartner Magic Quadrant for “Data integration Tools.”
* This chart was published by Gartner, Inc. as part of a broader study and should be understood in the context of the study as a whole.
The Gartner study is available from Microsoft upon request.
Gartner does not endorse any vendor, product or service mentioned in its research, and does not advise users of technology to base their selection of a vendor solely on its highest rating or other distinction. Gartner’s research reflects the opinions of Gartner Research and should not be construed as a statement of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
We offer great expertise when it comes to designing and implementing modern data architectures or complex database projects.
We work with over 130 specialists who have excellent knowledge of IT infrastructure, industry solutions and analytics.
We rely on strong business partners without ties to specific companies or products (best of breed approach).
We have outstanding know-how in hybrid cloud, analytics-as-a-service, data engineering, warehousing and science.
Financial services, pharmaceutical industry and automotive. These industries usually work with very large volumes of data from various sources – from machine data to financial data. In most cases, the use of edge computing and cloud structures is set.
In the banking environment, an IT infrastructure designed for zero data loss with systems and applications that meet strict regulatory requirements is hardly feasible using classic methods. The effort and costs would be far too high. The pharmaceutical and large automotive suppliers also have the highest security requirements: Here, research and development data in particular must be protected.
Now the management of your hybrid cloud architecture is easier than ever: Automate your data governance, compliance and integration processes with our Data Fabric concept!
Benefit from our many years of expertise in data management and request your consulting package now!
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