What is Augmented Data Management?

What is Augmented Data Management?

Augmented data management refers to the automation of data quality checks and the improvement of manual data cleansing processes.

Data mesh and data fabric are two new data management techniques that aim to tackle the issues of understanding, managing, and dealing with company data in a hybrid multi-cloud environment. The excellent news is both approaches to data architecture are complementary. But what do a data mesh and data fabric mean, and how do you utilize these data governance tools to make better decisions with your company data?

What is a Data Fabric?

According to Gartner, a design approach functions as a consolidated layer of connecting processes and data. Data fabrics employ continual analytics over available, discoverable, and inferred metadata to facilitate the creation, distribution, and use of consolidated and re-usable datasets in all settings, including hybrid and multi-cloud platforms.

The data fabric architectural concept help organizations ease data access and enable large-scale personal data usage. This method simplifies data silos, opening new possibilities for data management, integration, specific customer perspectives, and reliable AI deployments, among many industry application cases.

The abstraction layer of data fabrics makes it simpler and more convenient to model, consolidate, and analyze any data source, design data pipelines, and consolidate data in real-time because it is metadata-driven. By automating manual operations throughout data platforms using machine learning, a data fabric facilitates obtaining insights from data by improving data observability and quality. For the data users, this enhances data engineering productivity and time-to-value.

What is a Data Mesh?

A data mesh is a decentralized socio-technical strategy to distribute, access, and control analytical data in sophisticated and massive ecosystems—within or across enterprises employing, according to Forrester.

The data mesh infrastructure aligns data sources with data proprietors based on business categories or functionalities. Data proprietors can generate data products for their domains due to data ownership decentralization, which means data users, both data scientists and business customers, can employ a blend of these data products for data analytics and science.

The benefit of the data mesh is that it delegates data product generation to subject matter experts upstream who are most acquainted with the business domains, rather than depending on data engineers to cleanse and integrate data products downstream.

Furthermore, by supporting a publish-and-subscribe paradigm and using APIs, the data mesh speeds up the re-use of data products, making it more straightforward for data users to obtain the needed data, including reliable updates.

What is the Difference Between Data Fabrics and a Data Mesh?

Both can exist in the same system. Data fabrics facilitate the deployment of a data mesh in three ways:


● Allows data proprietors to create data products by cataloging assets, translating resources into products, and adhering to federated management principles.
● Permit owners and users utilize data products in various ways, including publishing data products to a catalog, searching and finding data products, and querying or visualizing data products using APIs or data virtualization.
● As part of the data product creation process or data product monitoring, use insights from data fabric metadata to automate tasks by learning from patterns.

Data fabrics allow you to begin with a use case and get quick time-to-value irrespective of data location.

In data management, data fabrics can help you adopt and use a data mesh to its full potential by automating a lot of the problems involved in creating data products and managing their lifetime. You may build a data mesh leveraging the data fabric foundation's agility, continuing to benefit from a use case-centric data architecture irrespective of your data location.

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