Data Management: Monolithic Architecture Vs Distributed Data Mesh

The technology world is evolving every day and organizations are relying on data. A data management architecture handles how organizations collect, store and use data. While a good management architecture offers clarity about every aspect for maximum business profitability, a bad data management architecture leads to inconsistent data set and data quality issues that can make the data worthless and reduce the ability to run analytics, business intelligence, and Big data activities. 

Earlier most organizations would prefer monolithic architecture as it is easy to set and can handle small-scale data analytics, however, it is proving to be challenging over time. Today more and more companies are associating themselves with a distributed data architecture. Today, in this article we will discuss one of the most important debates: Monolithic architecture vs distributed data mesh.

Monolithic Architecture

A monolithic architecture is a framework that stores, transforms, manipulates, manages, etc from a single centralized data store. Monolithic architecture is usually managed by one platform which is suitable for smaller organizations that have a relatively simple business. However, monolithic architecture poses several challenges-

• Monolithic architecture can’t scale indefinitely and also they don’t auto-scale as well. A monolithic architecture becomes slow, expensive, and hard to maintain as soon as the workload and data volume. 
• Also, the ability to consume data in a single place also reduces for those organizations which rapidly change use cases, data sources, and data consumers.
• Monolithic databases have high latency and throughput. With monolithic architecture, it is also not possible to respond to new needs without having to alter the entire data pipeline.
• Monolithic architecture lacks modularity and is hugely affected by the homogenization of technology. When any monolithic architecture becomes faulty or unresponsive, it corrupts the entire architecture and halts all activities. Also, there won’t be much space for innovation due to the adoption of a single database.
• It will result in inpatient data consumers, disconnected data producers, and backlogged teams burdened by technical debts and will struggle to survive in a world where changes are inevitable and businesses are in a challenging position to innovate themselves.

Distributed Data Mesh

Distributed data mesh is decentralized and viewed as “data-as-a-product”, and supports domain-specific data owners that are responsible for handling their own products in a user-friendly way. It is a platform version of microservices.

Organizations can achieve scalability by breaking the whole architecture into smaller and decentralized components and helping in building data-driven applications. Data can be effectively used to improve marketing, reduce costs and make more informed decisions.

It reduces the read/write rates, facilitates data innovation, and diminishes the burden to fulfill the needs of every customer using a single data pipeline. Here every team is free to decide how to collect, store and use data.

Distributed data mesh has an infrastructure that works as a platform and enables domain autonomy and offers a universal approach to product monitoring and governance, logging, altering, and many more. The impacts of failure are reduced here and everyone can make changes without altering other data stores.

Why should you make the transition from monolithic to distributed management?

Before going towards this transition, one must be sure of their decisions and standing in the market. Distributed data mesh is good but not suitable for all organizations and monolithic architectures pose some challenges but are not out of existence yet. 

Some useful tips to consider before transitioning-

• Team size- Organizations need to analyze the size of the team. 
• Data size- Evaluate the rate at which data is growing.
• Data variety- Check the use cases and sources.
• Bottlenecks in data– See whether the data team is occupied too much with resolving debts.
• Lead times- Check whether all the members of the team are disconnected or do they lack domain knowledge?
• Data domain numbers- Evaluate the data-driven products and features
• Data governance- Data governance should be given preference.

The bottom line

Organizations must adopt new technologies for managing big data. Although the advancements in the past have addressed the scale of data, they have failed in addressing the changes in other dimensions. A data mesh perfectly addresses these dimensions and bridges the gap between IT and leaders offering a platform to take a business forward. By transitioning from monolithic to distributed data mesh, organizations can reduce lead times and can efficiently use data for several use cases to build highly scalable and data-driven applications.

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