What Is Data Mesh and Should You Use It Too?

What Is Data Mesh and Should You Use It Too

If you are working in the field of data analytics or even if you are doing anything related to it, you must have heard about the phrase “Data Mesh”. For sometimes now, the buzzword is spreading like wildfire in the world of data analytics. However, as the theme is quite new, most people do not have any idea about exactly what the data mesh is. Keep on reading the following section of this article if you want to know more about it.

What Is Data Mesh?

As we have told you earlier, the term data mesh is right now the hottest topic in the world of data analytics. The technical experts across the world agree that the data market this year is going to be dominated by data mesh. But, what exactly is data mesh and why more and more companies are trying to implement it?

We are living in the age of data right now. Every company these days are trying to become data-driven. They are pouring incredible amounts of money into the data platforms and the people who are working in this field.

However, the ever-growing demands of the data sources is absolutely no match for the traditional data lakes or data warehouses that had limited real-time streaming capacity. The scalability requirements and the democratization underpins the faults in the business priorities, as well as legacy systems.

That is where the data mesh becomes relevant. The incredible architecture of the system creates a new field which solves the problems of the fragile yet bulky data pipelines. The data mesh enables the developers as well as the users to perceive data not as the by product, but as the self-contained decentralized data product.

Software development is, actually, the first transition point from the monolith application to the micro-service architecture. The data mesh is moving the massive data items from the centralized and cumbersome data lakes to a structure that priorities the data products and the domains.

Data mesh brings a paradigm shift in data architecture. It creates a situation, where the data teams have to offer publishing, sharing, discoverability and interoperability of the data assets when it is in the mesh. This system helps the experts to focus their attention on the products and outcomes they promised to the clients as they do not have to obsess over the used stacks or the underlying technology.

The Premise and Promise of Data Mesh

Even though data mesh is rapidly gaining a lot of attention from the techies across the world, the fundamental idea behind this technology is not that new. Some of the forward-thinking organizations have already used this technology.

According to the experts, the data mesh becomes even more crucial for the companies that want to scale quickly. Scaling out the data management team by hiring more engineers with exceptional skill-set is the alternative to implementing data mesh if the companies still want to maintain the scaling speed.

However, everyone knows that finding a data engineer with the right skills is extremely high. So, investing in distributed data architecture seems to be the only solution that the companies have in their hands.

Challenges Of Using Data Mesh

Data mesh is not the common plug-and-play solution that you are so familiar with now. If you are thinking about implementing it in your business, you must acknowledge the problems that come with this technology. The challenges would be different for each company. You need to understand that and navigate through it to reap the benefits that this technology hands out. That is where most people lose confidence. They often find themselves out of the depth about the ways they should implement it.

One of the most challenging parts about implementing this technology is, perhaps, setting up contracts about the methods of ingesting data. Unlike most other technologies, data mesh can not be separated from your business procedures. You can not implement the data mesh while isolating it from your business procedures. It is not something that goes perfectly from the first try. Instead, you have to give the data mesh time and grow along with it.

Surprisingly, the biggest challenge of implementing the data mesh for your business is not technical. You will see that the biggest obstacle is securing the data maturity that is important for an organization if it wants to perform well in a data mesh set up. It sometimes requires a change of perspectives for the companies. The product owners should consider data as their product as treating it in the likewise manner. It is a big step away from traditional software engineering, which can explain why it is not that easy to practice.

Data mesh is also one step closer to democratizing the data. It enables the whole business set up to treat data as valuable as a strategic asset. To make sure that happens, companies need to change their working style in different areas. That is where the cultural values and soft traits come in handy. If you want to become a data-driven company, you cannot let data be the main concern of one or two teams of your business. You need a paradigm-shifting view to make sure each of your workers treats data with due respect.

When Should You Start Thinking about Data Mesh

Even though companies have to encounter technical challenges while employing data mesh, that should not stop them from embracing the new technology. With the current rate of growing data sources, companies of every sphere should consider their options for scaling. If you are dependent on cloud migration or have experienced a domain driven development, then it is the right time for you to consider data mesh.

There is no doubt that there will be quite a few bumps in the road when you embrace the data mesh technology in your business. But always remember, there is a chance to find the perfect solution to your problem if you try hard enough. However, if you lack the technical know-how, then Matrix Media Solutions is always here to guide you through it. Just contact our technical experts, and they will help you to understand the data mesh system better.



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