What is Data Mesh? Architecture, Working & Benefits

Table of Content

• What is data mesh?
• How does a data mesh work?
• The new concept of data
• Data mesh benefits
• A data mesh is a mindset
• How does data mesh democratise data management?
• Core principles of data mesh architecture
• How to implement data mesh?
• Conclusion

Technology is constantly changing, and data mesh is a new advancement in enterprise software with a new approach to thinking about data based on a distributed architecture for data management. The primary idea behind this concept is to make data more accessible and available to business owners, data producers, and data consumers. With data science training, you will be able to grasp more knowledge about the latest software and accelerate your career by learning relevant topics such as data mesh and working with professionals in real-time scenarios.

What is Data Mesh?

A data mesh is a decentralised form of data architecture that helps organise data with the help of certain business domains. It gives more ownership to producers in a given dataset. The producer’s understanding of these domains and data positions also helps them set policies for data governance with a focus on access, quality, and documentation.

Data meshes help eliminate many bottlenecks that are usually associated with monolithic centralised systems. It also promotes adopting cloud platform services and cloud-native to allow you as a user to scale and achieve any data management goal you may have set. This is a familiar concept among most microservices as it helps audiences understand the use of this software within its landscape.

How does a data mesh work?

A data mesh requires the companies that want to adopt it to pivot their way of thinking about data. It is a kind of cultural shift that involves thinking of data as the product instead of a by-product of a process. Here the data producers are the owners of the product.

The producers are the subject matter experts in a data mesh; if you are the producer, then your understanding of the primary data consumers and how they leverage a domain’s operational and analytical data allows you to design APIs in the best interest of the user in mind. The domain-driven design of a data mesh makes the data producers responsible for

• Documenting semantic definitions
• Setting policies for permissions and usage
• Cataloguing metadata

But the central data governance team will ultimately enforce the standards and procedures around this data.

The new concept of data

As mentioned, the data mesh approach redefines the way you think about data, it is geared towards considering it a product or tool. It includes introducing changes at the organisational and process levels that you or your company must manage data as a capital asset for your business.

Data mesh directly links data producers to business users to the most significant degree possible. This removes IT personnel, who were often the middlemen from the project, along with processes that help ingest, transform, and process data resources.

Some data meshes also help provide customers with a platform to address their requirements for emerging technology, including the tools for data products, decentralised event-driven architectures, and streaming patterns for the data in motion.

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Data Mesh Benefits

Learning more about a data mesh and ultimately using one can yield myriad benefits, such as

• Deeper clarity into the data’s value by applying the best practices for thinking of data as a product.
• Over 99.99% operational data availability using a microservice-based data pipeline for data migration and consolidation.
• Ten times quicker innovation cycles that help you shift from manual batch-oriented ETL to continuous transformation and loading (CTL).
• Over 70% less data engineering, self-serve and no-code data pipeline tooling, agile development, and gains in CI/CD.

Why choose data mesh?

Most monolithic data architectures of the past are cumbersome, inflexible, and expensive. This has made it clear that anyone in the field of data science or those conducting data science training needs newer approaches like the data mesh that help reduce the time and money it takes for integration into digital business platforms that sometimes fail.

While a data mesh may not be the sole solution to this problem but it is designed to solve some of the most pressing issues that have been left unaddressed like

• 70-80% of all digital transformation fails
• The cost of operational data outages is constantly rising
• Cloud lock-in is a real problem, and it can become costly
• A data lake rarely succeeds and only focuses on analytics
• The rise of distributed data forces a need for a more effective, economical, and efficient architecture
• An organisational silo worsens data-sharing issues
• Data is a catalyst for a competitive edge and needs to be managed well

A data mesh is a mindset

Data mesh is in its initial stages of market maturity. And even though there is marketing content available about solutions that claim to be a data mesh, they only sometimes fit a data mesh's core principles or approach.

A good data mesh is more of a change in mindset. It is an approach that involves enterprise data architecture and an organisational model with the help of supportive tools. A proper data mesh solution combines thinking of data as a product, domain-oriented data ownership, decentralised data architecture, self-service access, strong data governance, and domain-oriented data ownership.

While you may have understood what a data mesh is, it is also essential to know what a data mesh isn’t.

• Vendor Product: A data mesh does not provide a singular mesh software.
• Data lake-house or data lake: Data lakes are complementary products that could be part of a large data mesh that spans numerous ponds, lakes, and operational systems of record.
• A data graph or catalogue: The data mesh needs to be physically implemented
• A consulting project: a data mesh is a long-term project as opposed to a one-time product
• Data fabric: while it may be conceptually related, data fabrics include various data integrations and management styles, while a data mesh is associated with a decentralised domain-driven design pattern.
• A self-service analytics product: products for self-service analytics, data wrangling, and data preparation are more a part of a data mesh and other forms of data architecture.

How does data mesh democratise data management?

Data architects are the ones who are responsible for constantly evolving and improving to fit your data management needs. However, centralising data can be complex, no matter where you decide to store it. A data lake is a cost-effective data architecture, but it has its drawbacks, including

• It is slow and can be challenging to access the data you need
• The data is usually locked into proprietary formats, which tack on limiting access control and additional fees.
• It requires storage, software, and data teams to make and copy data as well as maintain the pipeline, which can make it very expensive very quickly
• It is usually not manageable if you want to have all your data on a single platform as it has ingestion limitations.

A data mesh, on the other hand, is agile and scalable, improving time to market. It is also very flexible and independent, preventing enterprises from being locked into a data platform or product. It allows for dater access to critical data from a centralised infrastructure with a self-service model. Data meshes decentralise data ownership and distribute it among multiple cross-functional domain teams.

Core principles of data mesh architecture

Most data science certification courses will focus on explaining the core principles of most aspects of data science. As data mesh architecture is one such aspect of data science, it is best to understand its core principles before moving ahead and understanding how it is implemented.

Domain-driven data ownership and architecture

A domain is the aggregation of people who are organised around a common functional business purpose. A data mesh states that this domain is responsible for the management of data related to and created by the business function of this domain. It is also responsible for transforming, assimilation, and provisioning the data to an end user. Ultimately, the domain exposes its data as products whose lifecycle is owned by the respective domain.

Data as a product

A data product is produced by the domain and consumed by other downstream domains or users, creating business value for the producer. These data products are different from traditional data marts because they are self-contained and responsible for the provenance, security, and infrastructure concerns to ensure the data is always up to date. These data products also ensure there is a clear line of ownership and responsibility that the end user can directly consume to support the business.

Self-Serve data platform

It is vital to understand that this concept of self-serve data infrastructure contains numerous capabilities that domain members can use to create and manage their data products. This platform is also supported by an entire data engineering team whose primary objective is to manage and operate the various technologies that are in use. This highlights the separation between the concerns, mainly that domains are concerned with data production while the self-serve data platform team takes care of the technology used.

Federated computational governance

A data mesh allows a different approach by embedding governance concerns into the workflow of different domains. When concerned with data mesh, these aspects of data governance ensure that usage metrics and reporting become part of its definition. The amount of usage of data as well as how the data is being used, are some of the main data points needed to understand the value and success of the data products by an individual.

How to implement data mesh?

Data mesh is an overhaul of the entire data team's technology, people, and processes. Because of the ambitious scope of this project, it is hard to know where to start. Here are a few tips that you may learn during your data science training that will help you successfully implement a data mesh.

Choose the right pilot project

Working with teams one at a time allows you to learn valuable lessons about implementing data mesh on the go. This will help you as you adopt this architecture throughout the organisation. Choose a data product with a clear and quantifiable business value for the pilot project.

Don’t wait for a perfect platform

Implementing a data mesh is like trying to remodel your home while still living in it. Do not demolish the entire structure, and try to start from the ground up. Instead, you should approach it room by room and update your existing data architecture in increments while highlighting a path for other domain teams to follow.

Define the meaning of self-service for you

Before implementing a data mesh in your enterprise, defining what domain-oriented architecture and self-service data infrastructure will mean to you and your organisation is best. This will depend on your business needs, e.g., one organisation may provide self-service by helping their data producers ingest data through a Fivetran. At the same time, another will make giving domains control over who can access the data their top priority.

Define domains to encourage independence

While there will always be a bit of cross-domain or shared-domain data that must be controlled centrally by the data platform teams, these individuals serve enterprise use cases across two or more domains. As soon as these domains are determined, it may be a good idea to staff the domain team with a member who possesses the relevant cross-functional talent and domain expertise that will allow these domains to thrive on their own.

Create trustworthy data products

Data organisations may favour clear standards over a heavy governance framework. This being said it is best to create data products that are trustworthy and discoverable.

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Conclusion

This is just the tip of the iceberg when it comes to learning more about data mesh. As it grows and evolves, it is exciting to see where the technology may lead and how it can be used in different enterprises. For a strong base, it is best to enrol in a data science certification course to help you grow in your career as a data scientist and reach great heights. We at StarAgile Consulting provide various basic and specialised data science courses so you can quickly achieve your goals. So, get in touch with us today!

Also Read: What is Data Wrangling?

FAQs

1. Should you adopt a data mesh?

In truth, right now, a data mesh may not be the right fit for every organisation. Larger organisations that encounter uncertainty and constant change in their operations and environment are the primary targets for this kind of infrastructure. Suppose you have a smaller organisation whose data needs do not change too much over time. In that case, waiting for a while before implementing a data mesh may be beneficial, as it will be an unnecessary overhead.

2. Is data mesh a tool? 

Although data mesh is not a tool, it uses various tools to enforce global policies, such as GDPR enforcement or access management. There could also be local policies where a domain sets its own guidelines for its data products, further needing tops to regulate data control and retention.

3. What is the difference between a data lake and a data mesh?

A data mesh is a distributed data architecture in which the domain organises the data to make it more accessible to an organisation's users. On the other hand, a data lake is a low-cost storage environment that houses petabytes of unstructured, semistructured, or structured data for broad applications like machine learning and business analytics. A data lake can be a part of a data mesh but is usually a dumping ground for data frequently used to ingest data that does not have a defined purpose. As a result, a data lake does not have the appropriate data quality and governance practices that are needed for insightful learning.