#datafabric

Data Fabric: the main advantages

Data Fabric is a technology that offers a new approach to integrating data sources across platforms and business users while making data even more accessible, no matter where it is located. This technology makes it easier to access data within an organization, which in turn makes it easier to use the data in self-service mode. This architecture is independent of data environments, processes, and so on, providing end-to-end data management capabilities. Data Fabric also allows to automate the discovery, management, and use of data.

The main Data Fabric advantages:

  1. Smart integration

The main goal of this technology is to combine data of different types with endpoints using semantic knowledge graphs, metadata management and machine learning. Thanks to this, it is possible to group related data sets, add new data sources to data ecosystems. This eliminates silos in data systems and improves data quality.

  1. Data availability

The use of Data Fabric architectures allows to create self-service applications. Thus, not only technical teams (data engineers, developers, analysts, etc.) have access to data, but also non-technical teams. This ensures higher productivity and helps to make better and faster decisions.

  1. Better data management

The data structure provides a more efficient data management process by offering a unified view of all data. This contributes to easier identification, tracking of data and efficient use of data in general.

  1. Cost reduction

Data Fabric allows to consolidate data on a single platform while reducing data management costs.

  1. Data Protection and Compliance

Advanced access to data should not cause data security risks. Additional security measures ensure the security of data and allow to customize the availability of certain data for certain roles. The use of Data Fabric architectures reduces the risks associated with data sharing.

Efficient Data Management with Data Fabric

Modern companies often deal with large and complex data sets from different and possibly unrelated data sources (CRM, IoT, streaming data, marketing automation, finance, etc.). Large companies often have branches in different geographic locations. This can complicate the process of data using or storing (in the cloud, hybrid multicloud, on-premises, etc.). Data Fabric will help to combine data from different sources and repositories, transform and process it for further work. As a result, users get a holistic picture of the current situation, that allows them to explore and analyze data to conduct effective business activities.

Data Fabric is a data integration architecture using metadata assets to unify, integrate, and manage disparate data environments. The main task of Data Fabric is to structure the data environment, and it doesn’t require replacement of existing infrastructure. Metadata and data access are managed by adding an additional technology layer over the existing infrastructure. Standardizing, connecting, and automating of Data Fabric data management practices improves data security and availability, enables end-to-end integration of data pipelines and on-premises cloud, hybrid multicloud, and edge device platforms.

Benefits of Data Fabric using:

Data Fabric simplifies a distributed data environment where they it can be received, transformed, managed, stored. It also defines access for multiple repositories and use cases (BI tools, operational applications. This is made possible by continuous metadata analytics to build the web layer. It integrates data processing processes and many sources, types, and locations of data.

Differences Data Fabric from the standard data integration ecosystem:

The Data Fabric architecture depends on individual data needs and queries of business. However, there are 6 main levels:

  1. Data management (ensuring management and security processes);
  2. Receiving data (determining the relationship between structured and unstructured data);
  3. Data processing (only relevant data extraction);
  4. Data orchestration (data cleansing, transformation and integration);
  5. Data discovery (identifying new ways to integrate different data sources);
  6. Access to data (the ability of users to explore data using BI tools).

 When implementing Data Fabric, you need to consider:

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