Understanding the Difference Between Data Lake and Data Mesh

Data lakes and data meshes are two popular but very different ways that organizations can manage and process data. Learn which option you should use and when.
Last updated
April 11, 2024
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Data lakes and data meshes are two popular but very different ways that organizations can manage and process data. But which option should you use for your business? To make the best decision possible, it’s important to understand the key differences between these two solutions. In today’s blog, we’ll cover the pros and cons of each option and some use cases. Let’s dive in!

A brief overview

Data lakes and data mesh are both data architectures businesses can employ to handle large amounts of data, enabling better analytics and reporting across teams. That simplifies things, as both approaches are quite different. Let’s start by defining a data lake, which is the simpler and older architecture.

What Is a Data Lake?

In its simplest definition, a data lake is an organization’s centralized repository that stores all of its data. Data in a data lake is typically unstructured. Data lakes also don’t require data to be transformed or cleaned before storing, allowing it to be stored in its raw form. This gives organizations some flexibility, as the data can be transformed or analyzed as needed — reducing the technical burden of data storage.

Data lakes can store large volumes of data with little friction and little cost. They’re also highly scalable. Along with making data storage less resource-intensive, data lakes are primarily used for data analysts and other stakeholders to easily and quickly access data for data-driven decisions. The flexibility and scalability of data lakes give organizations more options for leveraging data than traditional data storage methods.

Use Cases

Let’s take a look at some of the use cases for data lakes.

Use Cases for Data Lakes:

  • Data storage and warehousing — Data lakes enable organizations to store large volumes of data from numerous data sources. This data can be stored in its raw and unstructured form, giving it an advantage over other storage methods. It also makes it easier for businesses to centralize their data.
  • Machine learning and AI training — Data lakes can store data for organizations that want to leverage machine learning and AI. Organizations can train their models using the data stored in their data lake.
  • Analytics — Data lakes are used for reporting and analytics by various data team members.

Pros and Cons

Of course, data lakes come with their own set of advantages and disadvantages. Let's take a closer look:

Pros:

  • Flexibility — Data lakes make it easy to store large volumes of data in an unstructured format. 
  • Scalability — Data lakes can handle a lot of data, so big data companies can scale up as needed.
  • Centralization for data teams — The data team will have a centralized repository of data, making it easier for them to pull analytics and metrics as needed
  • Cost-effective — Data lakes are far less resource-intensive than traditional data warehousing methods, making them a cost-effective architecture and storage method
  • Straightforward governance — Since data lakes are managed primarily by only the data team, governance is more straightforward and simplified.

Cons:

  • Technical complexity — Data lakes store raw and unstructured data, which is primarily used by the data team. This makes it more difficult to enable self-service analytics for non-technical users.
  • Data bottlenecks — Since the data team is in charge of the data lake pipelines, it can lead to bottlenecks as the data team becomes inundated with requests from other teams.
  • Quality control — Since the ownership of the data pipeline is solely managed by the data team, there can be a lot of data being added that may be irrelevant or inaccurate. Data meshes divide up the data pipelines by team, which can lead to more quality control.

Overall, data lakes offer flexibility and scalability, making them an attractive option for large enterprises or organizations with big data needs. However, they may not be ideal for organizations that rely on quick self-service analytics or organizations that have an overworked data team.

What Is a Data Mesh?

Data mesh is a newer approach to data architecture that has quickly grown in popularity over recent years. While data lakes and other traditional warehousing methods centralize data, data mesh takes a more distributed, decentralized approach to data management. This essentially means that data is owned and managed by individual teams and departments rather than the data team being the sole owner of the data pipelines.

In other words, the responsibility and management of data are put in the hands of the ones producing the data. The intention is that the data producers know the data best, and using a data mesh ensures better quality, improved data literacy organization-wide, improved collaboration and less load on the data team.

Use Cases

Now that we understand data mesh better let’s take a look at some use cases.

Use Cases for Data Mesh:

  • Microservices architecture — The decentralized nature of data mesh architecture allows teams to work efficiently on their part of the data system.
  • Self-service analytics — Ultimately, a data mesh should foster self-service analytics, giving stakeholders access to the data they need when they need it. 
  • Easing bottlenecks — When the data team isn’t the sole owner of data pipelines and when self-service analytics are enabled for non-technical users, data teams won’t get backlogged or bottlenecked as often.

Pros and Cons

Data mesh architecture is uniquely built to address several challenges that traditional data architectures pose, but that doesn’t mean there aren’t some drawbacks. Let’s take a look at some of the pros and cons.

Pros of data mesh:

  • Decentralization — A data mesh decentralizes data so that each team can independently manage its data sources and systems. This empowers each team and makes sure the data producers have control over the data they’re generating.
  • Data Ownership — Data mesh encourages each team to take responsibility and ownership of data. This can improve the accuracy and quality of data overall.
  • Data Literacy — When each team is responsible for data, team members will become more data literate and feel empowered to make more data-driven decisions without turning to the data team.
  • Flexibility — Data mesh allows teams to use their own data tools and technologies, giving the data management process more flexibility on a team-by-team basis.

Cons of data mesh:

  • Complexity — Data mesh is still fairly new and complex. It can sometimes be difficult for organizations to implement, maintain and get employee buy-in.
  • Governance — Governance becomes much more expansive when implementing a data mesh. Each team is responsible for its own data and governance, rather than the data team taking the lead on this front.
  • Data knowledge — Teams that aren’t used to the technical challenges of data will have to learn new processes, which can be a barrier to adoption.

Overall, a data mesh can be a flexible and scalable architecture that enables self-service analytics and cross-functional data management. However, it may not be right for every organization, especially if there aren’t many departments that produce data.

Data Mesh vs. Data Lake

With all that being said, should you implement a data mesh in your organization? Or is a data lake sufficient? To answer that, let’s do a quick recap of some of the key differences between the two.

Key Differences

While data lakes and data mesh share similarities, there are several key differences between the two. These differences include:

  • Architecture — Data lakes are typically centralized, while data mesh uses a decentralized architecture.
  • Governance — Data lakes have centralized governance, with data owners controlling access to the data. Data mesh decentralizes governance, with each team needing its own policies and standards.
  • Data Ownership — Data lakes are managed and owned by the data team. Data mesh requires each team or business unit to take ownership and responsibility for the data it produces.
  • Agility — Data mesh promotes agility by breaking down data silos, making it easier for organizations to share and collaborate across teams. This also enables self-service analytics, whereas data lakes still run most data queries through the data team.

Understanding these differences can help you choose the right architecture for your organization. Let’s break this down further by diving into the most common scenarios where you would use each option.

When To Use Each

Ultimately, the choice between a data mesh or data lake will depend on the unique needs of your organization. 

Data lakes are a common data warehousing and management architecture that allows organizations to centralize large volumes of data from multiple sources. Data lakes may be sufficient for organizations that have a data team that can handle the demands of data requests and ownership easily. Also, if you don’t have many teams generating or producing data, there may not be a pressing need to implement a data mesh.

Implementing a data mesh architecture is usually a good idea for organizations with many different data producers. These distributed organizations can harness a data mesh to empower these producers to take ownership of data and manage it more efficiently and effectively. Organizations that want to create a more seamless path to self-service analytics may also want to consider a data mesh.

How They Work Together

It’s worth noting that data lakes and data meshes don’t have to be mutually exclusive. Implementing some data mesh best practices can be a good idea for organizations that are better served by a data lake architecture. Organizations that implement a data mesh may also still use data lakes to help organize unstructured and raw data.

When working in tandem, these two approaches can complement each other to strengthen your data architecture. For example, a data lake can provide a centralized location for storing all of your organization’s raw data. This data can then be distributed to each data domain for formatting, transforming, cleansing and analysis. This can improve sharing and collaboration in your organization while still maintaining the decentralized nature of data mesh.

How To Choose What Is Right for Your Company

Whether you choose a mix of both approaches or you want to go all in with a data mesh or data lake architecture, it can be helpful to consider these factors when making your decision:

  • Data sources — Consider the data sources you want to integrate with your architecture. If you have few sources or most of your data is produced by the data team, a data lake may be sufficient. However, if you have many sources or disparate data types, you may want to consider a data mesh.
  • Data analytics — Consider your data analytics needs. Do you want all of your team to be empowered to use self-service analytics, or would you prefer data requests to be routed through the data team?
  • Data team workload — If your data team experiences frequent bottlenecks and backlogs, a data mesh might be able to ease their workload and give them more bandwidth and flexibility.
  • Data governance — Governance is usually more straightforward and easy to manage with a data lake. If you’re prepared to decentralize your governance and implement standards and regulations across teams, your organization may be able to handle a data mesh.

Implement a Data Mesh on Snowflake

Before wrapping up, it’s worth touching on one last point if you’re considering a data mesh architecture for your business. As mentioned earlier, a data mesh does allow different teams to utilize different data tools that they prefer. However, it can be helpful to use a single data platform across each self-contained domain to simplify the data mesh implementation and usage.

Implementing a data mesh with a platform like Snowflake can help reduce the likelihood of data silos and make your data architecture more cohesive overall. Using numerous different tools can make data sharing and collaboration more difficult, whereas a single platform can circumvent these issues.

A single platform also helps to solve some of the data governance challenges that arise from using a data mesh since everyone adheres to the same platform. It can also help make data more consistent, accurate and clean.

If you decide on a data mesh, it’s well worth looking into a platform like Snowflake to solve some of the key obstacles that organizations face when implementing a data mesh.

Try Secoda for Free

Regardless of what data architecture you use, it’s essential to have a data management platform in your data stack. Secoda offers a suite of data management features such as data lineage, data catalog, data sharing, data analysis and more. If you need an all-in-one data discovery solution, schedule your demo or try Secoda for free today.

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