There’s a lot of technical jargon in the world of big data management. For instance, what’s the difference between data mesh vs data lake?

Though they sound as if they could be similar, they’re actually very different. But both may play a role in your data operations and data governance initiatives, depending on your stakeholders, data flow, and organizational structures.

In this blog post, we’ll define both a data mesh and a data lake, explain how each works, then highlight the important differences between the two.

What Is a Data Mesh?

First things first, a data mesh is not a technology. Instead, as we explained in our blog post “Data Mesh vs Data Fabric: The Main Differences to Know,” Zhamak Dehghani coined the phrase “data mesh”in 2019, describing it as a paradigm shift away from a centralized data architecture to a modern, distributed architecture. A data mesh is a socio-technical organizational framework for collecting, managing, and sharing data assets across the enterprise. It eliminates silos by empowering domain experts to own data products they create and make them available to data consumers across business lines.

How Does a Data Mesh Work?

A data mesh approach to dataops — a methodology intended to improve communication, integration, and automation of workflows, and based on the devops approach created for software development teams —  marries product thinking with a move towards domain-driven data management, where the people who work most closely with the data are called upon to both optimize its quality and serve as subject-matter experts for the data products they “own” throughout the entire data lifecycle.

A data mesh methodology is defined by four pillars:

  1. Domain Ownership: Decentralizing data ownership gives business domains (Sales, Marketing, Finance, etc.) control of the data they create. The domain’s consequent familiarity with the data provides deeper insight into where, why, and how it should be used.
  2. Data as a Product: Treating data as a product makes data discoverable, understandable, and usable in the same way you search for and purchase products using your favorite search engine and/or e-commerce platform. When data is considered a product by the domain that publishes it, domain owners are empowered to become wholly responsible and accountable for their data products, including data quality, representation, and cohesiveness.
  3. Self-Serve Data Infrastructure as a Platform: Self-service data and analytics makes data readily accessible to members of the organization who need it to make informed business decisions. It simplifies data discovery and enables data democratization, making it quick and easy for anyone to surface relevant insights.
  4. Federated Computational Governance: Federated computational governance establishes a governance policy that’s standardized across each decentralized domain, ensuring all domain owners operate within a consistent framework.

Because of these pillars, implementing a data mesh architectural approach gives data and business teams the best of both worlds: a centralized database with domains responsible for handling their own pipelines. This agile methodology allows for greater autonomy and flexibility for data owners, eliminates data bottlenecks, facilitates greater data experimentation and innovation, and lessens the burden on data analytics teams that would otherwise be attempting to meet the needs of every data consumer in the enterprise through a single pipeline.

What Is a Data Lake?

Unlike a data mesh — which, as we’ve already described, is a socio-technical organizational framework for collecting, managing, and sharing data assets across the enterprise — a data lake is a technology stack that creates a storage repository for raw data, all tagged with a set of extended metadata tags so that the data can be queried by data analysts working to solve business questions.

Built to handle inputs from all types of data sources, a data lake is a secure platform that empowers organizations to store any volume of data in full fidelity, perform data processing in real-time or batch mode, and allow data scientists and dataops teams to analyze data using SQL, Python, or any other relational database management language. 

How Does a Data Lake Work?

Data lakes use a flat architecture and object storage to store the data with metadata tags and a unique identifier, which makes it easier to locate and retrieve data regardless of providers or origin data pipeline.

Data lakes are used to consolidate an enterprise’s big data in a single, central location and save it in whatever format it arrives in without the need to impose a schema. Data of any form or structure can be stored in a data lake, which is not that case with most databases and data warehouses.

Data Mesh vs. Data Lake; What’s the Difference?

As explained above, a data lake is a technology stack built to store all your enterprise’s data in any format and tag it with metadata to empower your data teams to search for what they need. 

A data mesh, on the other hand, is a socio-technical framework for collecting, managing, and sharing data assets across the enterprise; it’s a type of orchestration that improves communication, integration, and automation of IT workflows.

Data Mesh vs. Data Lake; Which One is Right For You?

If your enterprise is just scratching the surface in terms of a digital transformation, a data lake is a great place to start. It gives your organization a place to store and query all your data, however it is structured. 

If your enterprise has already taken steps to develop a data infrastructure, implementing a data mesh approach will help your data teams accelerate data retrieval, improve data integration, and enhance analytics.

But it doesn't have to be one or the other; If your enterprise is already utilizing a data lake for data storage, a data mesh architecture can connect that data lake with any other databases you may be using. In this respect, a data mesh architecture would be a terrific complement to your pre-existing data lake.

If you’d like to learn more about a data mesh approach to data governance, read our whitepaper, The Data Mesh Governance Framework You Can Implement Today.