The Data Mesh approach

The Data Mesh is a data architecture that simplifies collaboration and data self-service. This new paradigm is being increasingly adopted in the enterprise because of its many benefits.

Organizations are increasingly using big and small data to make better decisions. However, an organization's data architecture is not always optimized.

To unlock the full potential of data, data professionals must be able to seamlessly query and explore data. Often, a siloed data warehouse or data lake offers limited capabilities and fails to meet these needs.

The Data Mesh architecture paradigm addresses these issues and promises to transform the way organizations manage their data.
That's why Data Mesh is being adopted at lightning speed across all industries.

What is Data Mesh?

The term Data Mesh was first coined by Zhamak Dehghani, a consultant at ThoughtWorks. This type of data platform architecture embraces the ubiquity of data by leveraging a self-service, domain-oriented approach.
In the world of software development, teams have moved from monolithic applications to microservice architectures. Simply put, Data Mesh is the equivalent of microservices for data.

The general idea is to associate code structure and language with the business domain.
Traditional monolithic data infrastructures combine data consumption, storage, and transformation in a central data lake. This is not the case with the Data Mesh, where each domain is responsible for its own data pipeline. A universal interoperability layer, using the same syntax and data standards, connects data from different domains.

The Data Mesh is based on several key concepts. First, "data ownership" is shared among different "data owners" in each domain. Each is responsible for his or her data as a product. They must also facilitate communication between data distributed across different locations. The RACI model lends itself well to such an organization.

The data infrastructure is responsible for providing each domain with the solutions it needs to process data, but the domains are responsible for managing the ingestion, cleansing, and aggregation of data to produce elements that can be used by business intelligence applications.

Each domain is responsible for its own ETL pipelines, with the exception of certain capabilities that are common to all domains, such as storing, cataloging, and managing access rights to raw data. Once data has been processed within a sector, data owners can use it for their own specific analyses.

One of the outstanding features of the Data Mesh is self-service. Domain-based design principles are used to provide a "self-service" platform that frees users from technical constraints and allows them to focus on their own use of the data.

A central system coordinates data pipeline engines, storage management, and all streaming-related infrastructure. Each business unit uses these resources to deploy ETL pipelines according to its own needs. This approach limits the redundancy of tasks and expertise required to manage pipelines and infrastructure, giving each group greater independence.

Finally, interoperability is ensured by a set of universal standards that facilitate collaboration between domains. Data formats, governance, discoverability, and metadata fields must be standardized to enable collaboration between different domains around data.

Why use a Data Mesh?

For a long time, companies favored a single data warehouse connected to multiple business intelligence platforms. A small team of specialists was responsible for maintaining these solutions.

Today, the trend is toward data lake architectures that provide real-time data availability and streaming processing. The goal is to ingest, enrich, transform, and deliver data from a centralized platform.

However, this type of architecture has its weaknesses. A centralized ETL pipeline offers less control over growing data volumes, and this approach does not take into account the specifics of different data types

Thanks to its domain-oriented structure, the Data Mesh combines the advantages of a centralized data lake with the autonomy of the different departments of the company. As a result, it offers :

Scalability : The decentralized approach enables better management of growing data volumes.

Flexibility : By enabling each domain or department to manage its own data, organizations can be more agile and adapt quickly to changing needs.

Accountability and ownership : Each domain owns its data as a product. This reinforces the importance of data quality, governance, and communication.

Self-service : The Data Mesh architecture makes it easy for users to access and use data, reducing technical complexity.

When should you adopt the Data Mesh approach?

Data Mesh can be particularly relevant for teams that need to manage and rapidly process large volumes of data sources.
The choice of data architecture depends on a number of factors, including the number of data sources, the size of the team, the number of data domains, the barriers faced by the data engineering team, and the importance of data governance within the organization

The larger and more complex the data infrastructure requirements within the organization, the more likely it is that a data mesh will be beneficial. This architecture also improves self-service data observability.
Not all organizations are ready or need to adopt a data mesh architecture immediately. Its value lies in its ability to manage large volumes of data sources quickly and efficiently. Organizations with a complex data infrastructure, an increased emphasis on data governance, or a need for self-service observability will find the most value in adopting this approach.

Data Mesh is the next major architectural shift in the world of data. With its emphasis on decentralization, accountability, and self-service, it offers a robust solution to the current data management challenges many organizations face. As data is now ubiquitous and essential, adopting the right architecture like Data Mesh can be the key to a successful data strategy for many organizations. At data IQ, we help organizations develop an effective data strategy. From simple workshops to comprehensive support, we offer a range of services tailored to your data management needs.