Data Mesh Use Cases: A Journey from Monolithic to Distributed Data

Monolithic data architecture has long been the backbone of your organization’s business intelligence and reporting activities. However, the rise in data volumes and complexities across several departments can lead to overburdening of data management resources. If your organization’s current data architecture is slowing down the timely delivery of meaningful insights, it is time to reassess your infrastructure.

‍Data mesh is a new data architectural paradigm that is gaining widespread acceptance against traditional solutions. It brings you more data scalability and democratization, helping you enhance your real-time data analytical requirements.

Let’s quickly look at the concept and then understand data mesh use cases in detail.

What is Data Mesh?

Data mesh is a decentralized data architecture where you treat data as a product. The ownership and management of the data lies with several departments in your organization that create and consume it.

The concept of a data mesh was introduced in 2019 by Zhamak Dehghani, who emphasized adopting a domain-specific approach that helps organizations manage data better. This architectural style shifts from a monolithic data infrastructure with a centralized repository to distributed and autonomous data units.

Each unit has its own tools to manage data independently, reducing reliance on IT teams. It aids you to build accountability and data literacy at the grassroots of your organization.

Five Use Cases of Data Mesh

Implementing a data mesh architecture enables you to promote data democratization throughout your organization. Take a look at some of its prominent use cases to understand its application better:

Handling Large Scale Data Growth

Data mesh allows you to handle voluminous data by decentralizing the control over datasets. You can prevent data silos by prompting each domain to take charge and create meaningful data products within a central governance framework.

As data volumes grow, you can use data mesh to reduce operational bottlenecks and technical strain on the system. This not only boosts data visibility, prompting teams to access and modify data directly, but it also gives enhanced visibility into resource allocation and budgeting.

Creating & Managing Data Products in Data Mesh

Data as a Product is an approach that ensures every domain under the data mesh architecture has clear ownership to curate and manage datasets. By leveraging this approach, domains can create and handle data products to solve specific business problems.

For instance, the insights obtained from data products can be used for training and deploying machine learning models. These models can be utilized to create recommendation systems that rely on complex algorithms to study user behavior and provide personalized suggestions to consumers.

Another use case is building dashboards to track KPIs and performance metrics. This allows each domain to identify critical areas that require attention, contributing to better strategic decisions.

Autonomous Data Domains

Through data mesh, you can prevent overloading data management responsibilities through autonomous data domains. These domains share common attributes, allowing the concerned team to access and analyze them in real-time. The domain experts can also enhance and share the required data with stakeholders instantaneously, resulting in better data usability and business agility.

Federated Data Governance

A primary concern with data domain ownership is the risk of data duplication and a lack of interoperability across departments. To mitigate such issues, you can establish a federated data governance model in which all autonomous data domains operate within a unified framework. You must also create central data guidelines that dictate how every domain under the data mesh must extract, categorize, manage, and access data.

One important outcome of this data mesh use case is that it helps you create a balance domain autonomy and overarching organizational goals. Centralized governance ensures domain data owners adhere to industry-standard governance guidelines, making data monitoring and auditing processes easier.

Common Access Interfaces

In a data mesh architecture, teams must create common access interfaces that allow them to manage their data products end-to-end. This self-serve infrastructure must simplify the complexities of managing a data product's entire lifecycle.

The interface must be designed in a way that is universally accessible and domain-agnostic, promoting cross-functional teams to collaborate and share data effectively. By automating the access interface for data products, domain controllers can focus on delivering high-quality results.

Enhance Data Mesh Use Cases with Airbyte

If you plan to adopt the data mesh architecture in your organization, you must ensure that each domain receives relevant data for further processing. Since data can be spread across multiple sources and systems, you cannot provide complete and verifiable data for your data products. This can affect their performance and lead to poor insights and missed opportunities.

To enhance the performance of your data products, each domain should identify all data sources and consolidate them before processing. Consider incorporating a robust data integration and replication platform like Airbyte in your data mesh architecture.

Airbyte provides a vast connector library, offering 550+ connectors to most databases, data warehouses, and popular destinations. Even domain experts with limited technical expertise can use Airbyte’s no-code connectors to build a data pipeline in just a few minutes.

If some domain teams cannot find a connector for their specific data products, Airbyte offers the flexibility to build custom connectors in several ways. These are no-code Connector Builder with an AI-assistant feature and low-code CDKs.

To keep the results of the data products accurate and consistent, every domain team can configure Airbyte sync modes according to their data requirements. Through its Change Data Capture (CDC) feature, each pipeline under the data mesh gets directly updated with the changes in the source data with the destination.

Let’s consider an instance where two domains under the data mesh architecture wish to collaborate. The marketing team of the organization wants the finance team to share the revenue margins for a particular product campaign.

The finance team can first create an ETL pipeline through Airbyte’s Python environment, PyAirbyte. Here, they can use authorized source connectors to extract relevant revenue data from different customer touchpoints. Then, using various Python libraries, they can standardize the data and even mask some columns to avoid sharing personally identifiable information.

Once all data transformation is done, the dataset can be moved into a data destination that the marketing team can access. Here, PyAirbyte acts as a common interface, enabling two autonomous data domains to manage their data products effectively.

Overall, incorporating a data integration platform with numerous features into your data mesh helps you maximize its use cases to the full extent.

Conclusion

In this article, you have explored the major data mesh use cases that contribute to a data-driven culture in your organization. However, implementing a data mesh architecture goes beyond a simple technological change. Successful adoption requires a thorough identification of data domains and products, as well as the development of common access interfaces and federated data governance frameworks.

While considering the data mesh, it is important to redesign your organization’s data movement processes. A robust data integration platform like Airbyte can help each domain streamline its data collection functions. It will also enable you to maintain consistency across monitoring and logging operations and facilitate easy collaboration throughout the organization.