Data has become the lifeblood of modern businesses, and organizations are constantly looking for ways to extract more value from it. While there isn’t a one-size-fits-all solution for data management, organizations tend to take some common approaches. Two popular approaches to managing data are Data Mesh and Data Lake. Data meshes and data lakes have recently become popular strategies for groups that want to avoid silos so they can make data-driven decisions. 

Data Mesh is a relatively new paradigm that emphasizes decentralization and domain-driven architecture, while Data Lake is a more established concept that focuses on centralized storage and processing of raw data. While both have their advantages, choosing the right approach can be crucial for a company's success.

Some important points to keep in mind include:

  • Data mesh is a decentralized, domain-driven approach to data management, breaking down silos and granting access to authorized users.
  • Data lakes take a monolithic approach to storing data in a centralized location, whereas data meshes use a decentralized domain ownership strategy.
  • Data mesh pros: domains own their data, choose models to improve quality, integrate data products into all business processes, and enable scalability.
  • Data lake pros: the central repository for any type of information, supports advanced big data technologies, and low-cost storage options from cloud providers.
  • Both have different use cases and can be used simultaneously. Choosing between the two depends on an organization's specific needs; You will need a reliable ETL solution regardless of the approach you take.

Which should you choose? The answer depends on your needs. In this article, we will explore the pros and cons of Data Mesh and Data Lake, helping you make an informed decision on which one is right for your business.

Table of Contents

What Is a Data Mesh?

Data mesh is a new way of managing data within large organizations. It involves organizing data around business domains and enabling each domain to manage its own data and provide it as a product or service to other domains.

Data mesh architecture takes a decentralized, domain-driven approach to data management. As developed by data researcher Zhamak Dehghani, it follows four core principles that emphasize the importance of:

  • Giving data ownership to the teams that create or gather the information.
  • Treating data as a product.
  • Relying on a self-service data platform so users can access, share, and use information easily.
  • Using federated computational governance that establishes data governance standards while letting local teams decide how they will meet those standards.

By following these principles, you can build a data ecosystem that puts independent business domains in charge of their data but breaks down silos so all authorized users can access the data sources they need.

What Is a Data Lake?

A data lake is a single location that can store raw data as well as structured, semi-structured, and unstructured data. Consolidating all data in one place does more than eliminate silos. It also creates a data infrastructure that supports the implementation of artificial intelligence and machine learning technologies.

Source: Qlik

Image Source: Qlik

Comparing Data Meshes and Data Lakes

Before exploring the pros and cons of data meshes and data lakes, you might want to gain a deeper understanding of their key differences.

  • Data meshes require stricter standards than data lakes, so teams should expect to use specific formats and metadata that make information more discoverable.
  • Data lakes take a monolithic approach to storing data in a centralized location, whereas data meshes use a decentralized domain ownership strategy.
  • Data lakes treat information as something to collect. Data meshes treat datasets as products for all authorized accounts to use.
  • Data meshes have a largely non-hierarchal governance structure compared to the top-down governance of data lakes.

Pros and Cons of Data Meshes

The phrase “data mesh” was only introduced in 2019 and wasn’t well defined until 2020. Its recent development means data scientists still have a lot to learn about the pros and cons of using it to store and manage enterprise data. Still, there are some pros and cons you should consider before deciding whether to pursue a data mesh strategy.

Data Mesh Advantages

When you want teams to own and share information, you will likely find these data mesh advantages appealing.

  • Since domains own their information, they can choose data models that enhance overall data quality within their departments and projects.
  • While enterprises might choose to employ specific data teams that oversee operations, the data mesh strategy makes everyone responsible for quality, access, and storage at the local level.
  • Data mesh moves beyond the data pipelines commonly used by today’s organizations, which makes it easier to integrate data products into all business processes and decisions.
  • A data mesh doesn’t require you to store all data in a single location, making scalability easier and faster when needed.

Data Mesh Disadvantages

Although data meshes have several benefits, the approach can also create some concerns.

  • Since independent teams manage the data they create, organizations could find that they have multiple instances of similar datasets, making it difficult for data analytics to discover business intelligence trends accurately.
  • An organization-wide data mesh could take weeks, months, or years to build. It often makes sense for one department to test data mesh strategies to determine their usefulness. Until everyone uses the data mesh, though, it could impede decision-making and the analysis of large amounts of data.
  • Organizations need robust security standards to prevent shared data from falling into the hands of unauthorized users, an issue that becomes potentially even more problematic when a data mesh strategy encourages sharing.
  • As a new concept, you might struggle to find data science professionals familiar with data meshes.

Pros and Cons of Data Lakes

Data lakes can work well for an organization that wants a single location for data consumers to find the information they need. Still, you should expect to encounter advantages and disadvantages when embracing this approach.

Data Lake Advantages

If you want a central place to consolidate data, consider the benefits of using a data lake.

  • Since data lakes can store any type of information, they act as central repositories where all authorized users can find the data they need.
  • Artificial intelligence, machine learning, deep learning, and other advanced big data technologies can take advantage of the data lake’s ability to hold massive amounts of information.
  • Most cloud database providers, including Azure and AWS, offer relatively low-cost options for data lake storage.
  • Loose requirements make it simple to add new data in real-time, a critical feature for many automations that meet today’s business needs.

Data Lake Disadvantages

Many organizations encounter the following data lake challenges.

  • Although loose requirements make data lakes fast and simple to use, they can lead to poor data quality, data duplication, and other issues that interfere with reliability.
  • Diverse data formats can make it nearly impossible to uncover valuable insights.
  • Some data engineers familiar with data warehouses and other conventional data storage strategies might not know how to implement and manage data lakes.
  • Organizations that use data lakes will need reliable ETL solutions that can connect to diverse data sources and destinations.

Data Mesh vs. Data Lake: Which Should You Use?

Data meshes and data lakes have different use cases, so it’s impossible to say whether your organization should rely on one rather than the other. In some instances, businesses use data mesh and data lake strategies simultaneously. Doing so means domains get to own data without hoarding it. Instead, all of the information they retain gets added to a data lake, where machine learning solutions and other tools can mine them for insights.

Regardless of whether you choose a data mesh, data lake, or both, you will need a reliable ETL solution that empowers everyone to use data without learning to write complicated code.

How Can Help empowers companies to sync data with data meshes and data lakes without learning to code or build complicated big data pipelines. With, you get a no-code ETL and reverse ETL solution with an intuitive drag-and-drop user interface. also offers instant read/write REST API generation that works well with data mesh architecture. When you want to treat data like a product, it makes sense to adopt an API-generation tool that practically anyone can use.

Schedule a demo to learn more about how can help with your data integration needs.