As data grows in complexity and volume, businesses need better ways to manage it. This is where data mesh and data fabric come in. These are two of the most popular next-generation data management concepts. They both have their strengths and weaknesses, but they are also complementary.

We break down these two concepts and examine how you can use these solutions to leverage your enterprise data for better decisions.

Zhamak Dehghani first defined the term data mesh in 2019 as a concept for data management based on four core principles: 

While the traditional data management model focuses on a central repository of information, the data mesh concept takes a more distributed approach. Under this model, each team or department is responsible for managing its own data and for creating a federated system that all can use.

This approach has several advantages. First, it helps to ensure data is accurate and up-to-date, as each team is responsible for maintaining its own records. It also promotes transparency and collaboration, as teams can see how others are using data within the organization. In addition, data mesh eliminates the need for a centralized IT infrastructure, giving organizations a more flexible and scalable way to manage data.

The value of the data mesh concept also lies in its ability to create a single source of truth for an organization’s data while giving greater control to individual business units. This helps prevent data silos and makes it easier to govern and manage the organization’s overall data estate.

In addition, the self-serve data platform principle enables business users to access the data they need more easily when they need it. This can help to improve decision-making and increase operational efficiency. 

Ultimately, the data mesh concept can provide significant benefits for organizations looking to better manage their ever-growing data estates.

A data fabric is a design concept that serves as an integrated layer (fabric) of data and connecting processes. Gartner first coined the term, and it has become a popular way to think about data management. 

A data fabric takes advantage of continuous analytics over known, identified, and inferred metadata resources. This makes it possible for data to be shared and reused across multiple platforms, data types, and locations.

The data fabric draws on human and automated capabilities to gain access to existing data or help with its consolidation where appropriate. It finds distinct, business-related connections between distinct applications’ data points by identifying and connecting data from various sources. The insight allows for re-engineered decision-making that generates greater value through fast access and comprehension than traditional data management techniques would allow.

In a typical setup, a data fabric begins as a passive observer of the data pipeline. With time, it begins to use artificial intelligence (AI) and machine learning (ML) capabilities to suggest more efficient data handling methods. 

The data fabric is not a data pipeline, data warehouse, or data lake. It is the next logical step in data management that builds on those concepts.

A data mesh and data fabric are both approaches to managing data that emphasize governance, discovery, and reuse. However, there are some key differences between the two.

A data mesh is a network of data products, each of which has a well-defined owner and is accessible via APIs (application programming interfaces). In contrast, a data fabric is a centralized platform that enables the creation of data products and provides a single point of access for consumers.

However, as mentioned at the outset, the two concepts are complementary and can coexist. For example, a data fabric can be used to catalog assets, transform them into products, and follow federated governance policies. This will enable data consumers to use the data products in several ways, such as publishing data products to the catalog, searching for them, and querying or visualizing data product information via APIs.

Data fabric metadata can also be used to discover patterns in the data product production process or while monitoring the data product and then automate these activities.

As organizations strive to keep up with the ever-increasing volume of data, augmented data management has become increasingly important.

Augmented data management helps organizations to manage their data estates better, improving data quality and reducing the need for manual intervention. Additionally, augmented data management can provide a unified view of an organization’s data, automata metadata management, and the generation of master data models.

In an age where data is growing at an unprecedented rate, augmented data management provides a much-needed solution for keeping up with the demand.

Utilizing these data management concepts can help to improve decision-making, increase operational efficiency, and prevent data silos. All of these factors are important in today’s business landscape and can help give organizations a competitive edge.

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