Introduction to Data Mesh

Last Updated on November 30, 2022 by Andrew

So your organization wants a data mesh – awesome.

But now what? Below is the complete primer introduction to data mesh with its challenges, and viability so your data infrastructure doesn’t turn into a hot mesh – pun intended.

The decentralized architectures are flourishing as more engineering teams are looking to unlock the true potential of their systems and resources. From microservices to Git, and to cryptocurrencies –all are looking for decentralization to break free from the centralized bottlenecks.

This is where the data mesh comes in;

Table of Contents

Data mesh is an approach to organizational data management revolving around the decentralization control of data itself. In this post, we shall discuss data mesh in detail, with its need, challenges, and uses.

Need for Data Mesh

With businesses and companies becoming hugely data-driven, data mesh comes for help with three basic elements of the modern data organization, including;

The hunger for more and more data leveraged and ingested by varying stakeholders spanning the organization in contrast to a nuclear team of data wranglers.
The ever-increasing complexity of the data pipelines with the teams seeking to perform more and more intelligent operations with their data resources.
Skyrocketing standardized data discoverability and observability layers for understanding the health of data assets, all across the lifecycle.

The power of data mesh is both intimidating and exciting and similar to the microservices architectures before it, data mesh has gone on to stir the discussion pot quite a lot. With discussions on what it needs to bring data in operations at massive scales.

Why use a data mesh at all?

In the recent past, many organizations capitalized on a single warehouse that was connected to plenty of intelligence platforms. The said solutions were only maintained by a small group of specialists and effectively burdened the technical debt.

Beginning in 2020, the platform du jour was a data lake with real-time stream processing and data availability. All with the goals of ingesting, transforming, enriching, and serving data via a centralized platform. However, such architecture fell short for many businesses because;

Central ETL pipelines offer less control over the increasing data volumes
With every company becoming a data company, the rise of different use cases for varying types of transformations leaned in a heavy load on central platforms.

This kind of data lake lead towards disconnected data producers, edgy consumers, and the worst – the backlogged data team that struggles to keep speed according to the demands and needs of the business.

Enter the “Data Mesh”, the domain-based architecture of the data mesh leveraged the teams with the best of both worlds;

A centralized database or a distributed data lake with business areas being responsible independently for handling the data pipelines. Data architecture can be effortlessly scaled when broken into smaller components that are domain-oriented.

Challenges Faced by Modern Organizations

Organizations have come far from where they began decades ago with their data journey.

But still, there are some inhibitors preventing enterprises from being able to leverage the full value and scope of data.

Let’s look into some of these below;

Discoverability

Only a few entities were able to mature their data estates to reach a level where they can set up a marketplace. A marketplace where the data consumers can understand, search, and then move on to make an informed decision regarding the datasets they want to use.

Ownership

Establishing ownership of data sets is difficult.

Who owns or who can certify that a dataset is trustworthy? Mostly the IT teams owning the data platform ends up being the custodian of data even when they may not understand what the data entails.

Productivity

Business analysts in addition to data analysts end up spending 30 to 40 percent of their time looking for the perfect dataset. While data engineers spend so much time determining how to join disparate sources of data for building semantically uniform datasets.

Agility

In big organizations – changes are the only constant.

Data estates can’t keep up with the pace of fast changes and become a solid inhibitor for enterprise agility. A simple report generation takes weeks of time and resources, which is too much in the fast-paced evolving world.

Skills and Expertise

All data workforce requires specialized skills. Maintaining which is quite expensive and then missing the key skills become the bottleneck itself very often.

Dependability

Traceability, quality, and observability are facets that call for robust implementation.

Can you trust the data?
Is this the latest file?
Or is it complete?
Do I know if it is coming from the correct source?

All of these are complicated questions, and one doesn’t get easy answers to them normally.

Self Services

Capabilities that complement self-services include datasets, platforms, and tools –which are either not available or if they are available, they end up being not enough in many business enterprises.

Data mesh sees all of the above-mentioned challenges from a different lens and works to solve and reduce the sheer severity of these issues.

Clearly,

Data mesh is a New Paradigm

The basic philosophy of the data mesh states that the current facing data estate challenges faced by organizations can’t be solved by adding yet another technology into the mix.

The prime solution lies in the reorganization of the three primary players in an enterprise;

People
Resources
Tools

Memphis is a repository of all three, should you need any information base, tools, resources, or expert-based assistance, get in touch with us.

Introduction to Data Mesh – Challenges and Its Viability as the New Paradigm