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Data Mesh Architecture: How to Drive Data Adoption

Data Mesh Architecture: How to Drive Adoption
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As the amount of data and data applications skyrocket, data has been transformed into far more than just a tool for supporting decision-making processes, data has become a product in itself. As a result, data productivity is turning into one of the most important KPIs for companies that manufacture data and want to implement data mesh principles and with it a data mesh architecture. 

The quest for higher productivity is intensifying, and with it, so is the need for smooth and harmonious procedures for data adoption. This includes a true collaboration between all data functions, flexibility, and the ability to easily and quickly scale, but, there’s another important feature of the changing to a data mesh landscape – integration. 

True data products integrate other data products and data expertise (analytics and ML). This integration challenges the big data ecosystems that currently consist of central teams, linear development, and highly coupled data pipelines that create “one-way street” monoliths.

What is Data Mesh Architecture 

Distributed data mesh architecture is a well-known concept and may well be the next enterprise data architecture as well as the key to data relevance. However, utilizing data mesh principles and shifting to a data mesh architecture doesn’t just require a technical process – it also requires a cultural change within the industry.

The Datorios Data Platform

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Based on the core principles of distributed data mesh architecture, including data product thinking, distributed domain-driven architecture, and self-serve platform designDatorios’ platform was built to be an enabler of data mesh principles using a data mesh architecture. Using the Datorios platform, you’ll be able to start shifting to hybrid data mesh implementation and grow as you go.

Datorios serves as the ideal foundation for data mesh implementations.

The main building block of Datorios is a domain that is owned by independent cross-functional teams of data engineers and data product owners. Each domain consists of blueprints upon which specific domain-oriented data pipelines are created and operated, with emphasis on data transformation abilities.   

Step-by-Step Pipeline Building

The Datorios concept of step-by-step pipeline building focuses on business logic, decoupling the design phase from implementation offering faster TTM and the opportunity for true collaboration. The domain is an end-to-end data playground where teams can produce and enrich data from any source to any destination without the limitations of perceptions like ETL, ELT, and reverse ETL.

Produce Data Products

In addition to designated products (dashboards, analytical data, and operational data) the domain can easily produce data products for other domains to use. 

Datorios offers a variety of tools for the creation of these data products, all accompanied by detailed documentation, aligned with federated governance, and highly compatible with other domains to use. All our data products are discoverable in our data store and can be used by other domains according to access management.

A High-Verbosity Framework Offering High Visibility and Distributed Analytics

When ingested, any data along the blueprint is represented in a unified way, allowing for integrative correlation and transformation of different operational data sources (internal and external to the domain) or data products, of any type or schema. This grants the transparent usage of any data sources and motivates other domains to use and create data products.

One of the concerns regarding the distribution of ownership to the domains is the overpower required to operate the data pipeline infrastructure in each domain. The Datorios framework consists of a unique backend that effectively functions as “data infrastructure-as-a-platform” for all domains.

The platform is both domains – and data-agnostic, self-serve and as-code operated, saving the need (and time) for deep technological understanding and handling of the different layers of the infrastructure stack. The backend controller automates the allocation of resources to the different domains and auto-scales them according to predefined policies.

Datorios is a high-verbosity framework that offers high visibility and distributed analytics of all layers in the platform (including infrastructure level, domains, blueprints, single data transformers, and single data events). This feature allows monitoring from the domain level down to the ETL data platform team level, enabling true responsibility for operation and data quality.

At Datorios we challenge the perception of data pipelines, emphasizing the design phase over implementation for higher productivity and better business alignment. Our infrastructure essentially functions as a platform, which serves as the foundation for data mesh implementation. 

Data mesh is the future of enterprise data platform architecture

This is why Datorios is extraordinarily compatible with existing data environments, providing a means of reducing data integration risk and allowing you to move towards data mesh architecture with the opportunity to manage your data infrastructure in a traditional way.


What is the difference between a data lake and data mesh architecture?

A data lake is a centralized repository for storing raw data, while a data mesh architecture is a decentralized approach that focuses on data ownership, domain-driven design, and making data more discoverable and accessible.

What are the 4 pillars of data mesh?

The four pillars of data mesh are: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure, and federated governance.

What is data mesh used for?

Data mesh is used for managing complex data ecosystems by providing a decentralized approach to data ownership, architecture, and governance, empowering domain teams, and improving data infrastructure scalability and reliability. It’s particularly useful for large organizations that need to manage diverse data sources and enable data-driven decision-making.

What is adoption and implementation?

Adoption and implementation refer to the process of accepting and integrating a new technology, system, or process into an organization or a business. It involves the steps of planning, testing, deploying, and integrating the new solution into the existing infrastructure, and ensuring that it meets the organization’s goals and objectives.

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