Data lake and data mesh are two different architectures for data management. While data lakes have been around for some time, data mesh is relatively new. Note that they have their respective advantages as well as disadvantages. Also, they have different use cases. As per Grand View Research, the global data lake market size was estimated at USD 13.62 billion in 2023 and is projected to grow at a CAGR of 23.8% from 2024 to 2030. According to marketsandmarkets the global data mesh market size was valued at USD 1.2 billion in 2023 and is expected to grow at a CAGR of 16.4% from 2023 to 2028. The revenue forecast for data mesh in 2028 is projected to reach $2.5 billion. 

This particular blog post empowers organizations with the knowledge about the pros and cons of 2 distinct modern data architectures namely data lake and data mesh. Enterprises get to know which architecture best matches their infrastructure, business culture and operations. This includes the details of each architecture and helps organizations make an informed and timely decision that they can leverage for maximum benefit.

Introduction to Data Lakes

Data lake is an entity’s centralized repository where all its data is stored. Usually, the data is raw and unprocessed. In this model, large amounts of data can be stored at relatively low cost. Data lakes can store structured, semi-structured as well as unstructured data.  The data can be collected from different and disparate sources. Data lakes are simpler when compared to data mesh. The former is also older. This architecture does not need the data to be cleaned or transformed prior to storage. Entities can train AI (Artificial Intelligence) models utilizing the data present in their respective data lake(s).  A data lake is perfect for initial data exploration and experimentation in AI projects.

As a single data team manages a particular data lake, governance is relatively simple. A disadvantage of data lake is that users having limited or no technical knowledge find it difficult to perform self-service analytics. If there are too many requests from the other data teams present in a particular organization the performance of the single data team at the helm can suffer. In the data lake model, data quality can suffer because of the addition of irrelevant or inaccurate data. Pros of data lakes are ease of scalability as well as flexibility. Big businesses or entities with big data requirements would favor data lakes over data mesh. In the data lake architecture, most data queries are delegated to the central data team.

Introduction to Data Mesh

For your information, Data mesh is a decentralized architecture. The former has a distributed approach to data management. Different domains of an organization own, control as well as manage their respective data. Each business domain manages respective data infrastructure and processes. Data Mesh is more complex than data lakes. Also, the former is a relatively new and emerging data architecture. For your information,its popularity has been rising fast in recent years. In this architecture, every domain has to take care of respective data products. Utilize data mesh to achieve faster data access in real-time AI applications. The right training has to be imparted and necessary resources made available to all the members of various business domains to effectively use as well as implement data mesh. Using the latter needs a change in organizational culture to persuade employees to adopt this useful technology. 

An advantage of the data mesh architecture is that each business domain has the freedom to use its very own tools and technologies. As a result, each business domain is more effective, efficient as well as productive. More innovations can be achieved and the performance is better as well as faster. Here, the management as well as responsibility of data are delegated to the entities generating the data. The logic behind this is that the generators of data comprehend the data far better than other parties. Benefits include superior data quality as well as ramped up data literacy throughout the enterprise. Other benefits include superior collaboration and minimizing the workload on the pertinent data team. Data mesh can sometimes be complex for entities to adopt. The complexity of data mesh may result in resistance to adoption by some organization’s employees. Using this technology requires learning new concepts which may take time or face resistance from individuals preferring the status quo.

In the data mesh model, each business team determines its own policies as well as standards. This modern architecture breaks down data silos so that entities can more easily share as well as collaborate across business units.

When to Utilize Data Lakes

This architecture is suitable if you are a relatively small entity not having many business domains. If your organization prefers a centralized approach then data lakes is the right choice. In the event your organization does not want to take risks, then data lakes may be the better choice as it has been existing before the emergence of the data mesh architecture.  If your organization wants cloud-based data storage then go for data lakes. Entities having vast amounts of data and needing a centralized repository should utilize data lake.

When to Utilize Data Mesh

This particular architecture is suitable when there are many business domains or teams producing or generating data.  In the event your organization desires a decentralized approach then adopt the data mesh architecture. Go for data mesh if each business domain in your organization has the technical expertise to effectively manage data as well as make data driven decisions. If the data team of your organization is overloaded then adopting data mesh architecture will decrease the load to manageable levels. Data mesh is ideal for organizations requiring real-time reporting and analysis. Choose data mesh architecture if your organization wants to quickly scale its operations.

Choosing Between Data Lake and Data Mesh

Note that data mesh is unlikely to completely replace data lakes. Rather the two can be combined for the purpose of synergy. Organizations can have the best of both architectures by combining them. The right choice for AI workloads is based on your organization’s structure, type of data requirements as well as expected level of agility in data management. It is imperative to understand the two architectures, be aware of each’s benefits and drawbacks as well as the differences between them. These steps and measures will surely help your organization make a decision whether to choose data lake, data mesh or combine both architectures for synergy. If you have the expertise to make a decision on which architecture to use then do it. Otherwise, it is recommended to consult experts aware about both the architectures. 

Possessing relevant experience and expertise in implementing data lakes and data mesh, CoffeeBeans is well positioned to meet your organization’s goals as well as objectives.  We have a pool of professionals proficient in implementing data lakes as well as data mesh. Our clients can vouch for our transparency, real-time response as well as adherence to stipulated deadlines. We provide stellar quality at competitive rates. The team at CoffeeBeans will help your organization decide which architecture is best for it. Reach out to us at [email protected] to know how we can help your organization obtain its desired requirements as well as preferences.