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Understanding Data Mesh Architecture

The term “data mesh” has been gaining much traction lately, but what is it exactly? In short, data mesh is a new architecture for data management that seeks to solve many problems plaguing traditional data architectures. 

In a traditional data architecture, data is often siloed into different departments or business units. This can make it challenging to get a holistic view of the data and lead to duplication of effort as various departments try to access the same data sets. If you’re interested in learning more about data mesh software and how it can benefit your organization, continue your read here.

What Data Mesh Architecture Seeks To Do

Data mesh architecture seeks to address these issues by creating a single, centralized data store that all departments and business units can access. This centralized store is then divided into smaller “data slices” that specific departments or business units can use.  

The data mesh architecture also makes use of event-driven data flows. This means that data is only copied or moved when an event triggers it. For example, if a new customer is created in the CRM system, that event would start a copy of the customer’s data to be made in the centralized data store.  

This event-driven approach to data management can help reduce duplication of effort and keep data up-to-date across all departments and business units.

What Data Mesh Architecture Entails

There are a few key components that make up data mesh architecture.

Data Governance

Federal computational governance is one of the key features of data mesh architecture. This is the procedure of controlling how data is processed and utilized in the data mesh. It covers topics such as establishing data format standards and assuring that data is kept safe.

Computational governance helps to ensure that data is consistently formatted and of high quality. This makes it easier to work with and analyze. It also helps to protect sensitive data from being accessed by unauthorized individuals.

Deconcentrated Data Ownership

Another critical aspect of data mesh architecture is distributed data ownership. This implies that each department or business unit has its own data sets and is in charge of managing them. This can aid in preventing duplicate efforts while ensuring that data is always up to date.

Data slices are also used in data mesh architecture. These are small data portions used by specific departments or business units. Data slices can help to improve performance and make it easier to work with large data sets.

Self-Service

Data mesh architecture also contains a self-service component. This would enable departments and business units to access their data without going through IT or other central departments. Consequently, this could help speed up the process of acquiring data and enhance efficiency within the organization.

Data as a Product, Not a By-Product

Data mesh architecture has many benefits, one of which is that it views data as a product instead of a by-product. In other words, the focus is on using data to improve business results rather than merely storing and managing it. This can help organizations tap into previously hidden values in their data and be more competitive overall.

How Data Mesh Works 

Data mesh architectures typically use two key components: a shared data store and multiple independently-operated “data slices.” The shared data store is a central repository for all of the organization’s data. This data store can be either on-premises or in the cloud. 

The multiple independently-operated “data slices” are small subsets of data that are extracted from the shared data store and used by specific departments or business units. These “data slices” are often kept in separate databases or cloud storage accounts so that they can be updated independently from each other. 

One key advantage of using “data slices” is that it allows different departments or business units to access only the data they need, reducing the amount of duplicate work required. Another advantage is that if one “data slice” becomes corrupt, it does not affect the rest of the organization’s data. 

Organizations typically use an ETL (extract, transform, load) process to populate their shared data store and generate their “data slices.” ETL tools extract data from multiple sources, convert it into a standard format, and then load it into the shared data store. Once the shared data store has been populated, the “data slices” can be generated. 

The Benefits of Data Mesh Architecture

There are many benefits to using data mesh architecture within an organization. These benefits include:

  • Improved data quality: Data mesh architectures can help advance an organization’s data quality by ensuring that it is consistently formatted and high-quality.
  • Increased efficiency: Data mesh architectures can help improve an organization’s efficiency by allowing departments and business units to access their data without going through IT or any other central departments.
  • Improved performance: Data mesh architectures can help enhance an organization’s performance by allowing different departments or business units to access only the data they need, reducing the amount of duplicate work needed.
  • Enhanced competitiveness: Data mesh architectures can help improve an organization’s competitiveness by viewing data as a product instead of a by-product. This can help organizations tap into previously hidden values in their data.

Final Thoughts

Here in the data engineering industry, organizations are increasingly turning to data mesh architectures to solve big data problems. Data mesh provides many benefits over traditional architectures, including improved data quality, better decision-making, and reduced duplication of effort. If you’re looking for a more flexible way to scale your significant data architecture, consider implementing a data mesh solution.

 

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