Data mesh is a decentralized approach to managing and distributing data. Meanwhile, data lakehouses serve as centralized repositoriesthat combine the best of data lakes and warehouses.
In this post, we discuss data mesh vs data lakehouse to understand the differences and how you can use a centralized data lake within a distributed data architecture, with an emphasis on data products.
TL;DR
- Data mesh is an organizational methodology that decentralizes data ownership and views data as a product.
- A data lakehouse is an architecture that centralizes storage and management.
- Data mesh and data lakehouses work together by combining centralized platforms with decentralized domain-owned data products
What is a data lake?
A data lake is a centralized repository for storing large amounts of raw data in structured, semi-structured, or unstructured formats. Unlike traditional databases, data lakes prioritize flexibility and cost-effectiveness.
Data lake architecture
In response to the challenges of data warehouses, the data lake architecture emerged. Many were thrilled with this new option for its access to data-driven, machine-learning model training workflows and its support for parallelized data access.
The data lake architecture is similar to that of a data warehouse, in that data is extracted from operational systems and loaded into a central repository.
However, unlike data warehousing, a data lake stores vast amounts—terabytes and petabytes—of structured, semi–structured, and unstructured data in their native formats until they’re needed. Once the data becomes available in the lake, the architecture is extended with elaborate transformation pipelines to model higher-value data and store it in Lakehouse marts. Essentially, we moved from ETL to ELT processing.
The data lake architecture is often described in the following way:
- Data is extracted from operational databases
- Data is raw and minimally formatted
- Data is accessed through the object storage interface
- Data lakes are designed to handle enterprise-grade analytics
- Data lakes also answer big questions such as: “How is your business doing?” and “What investments and opportunities should you be making?”
You can see from the visual below that a data lakehouse architecture results in complex, unwieldy data pipelines, leading to unmanaged, untrustworthy, and inaccessible datasets. Also, as data lakes grow in size and usage, they become expensive to scale and meet the business’s performance demands. Unfortunately, we still relied on a centralized team to perform the ELT, so again, as business users request a change, they have to wait for the central team to respond. Similar to data warehouses, this approach limits the value of data to data analysts, ultimately restricting the business from making informed, data-driven decisions.
Related reading: Data mesh architecture
What is a data lakehouse?
A data lakehouse is a hybrid architecture that combines the flexibility and scale of data lakes with the management, reliability, and performance of data warehouses.
Key Capabilities
- ACID Transactions & Schema Enforcement: Data lakehouses leverage table formats such as Delta Lake, Apache Iceberg, or Hudi to support ACID-compliant transactions. Schema rules are enforced to maintain data integrity.
- Unified Support for Structured & Unstructured Data: In a single platform, data lakehouses handle everything from CSV, JSON to images, logs, and more.
Open Table Formats: Open table formats like Apache Iceberg provide a metadata abstraction layer on top of files in storage, enabling reliable updates, deletes, and time-travel querying.
Data lakehouse vs data mesh: centralized architecture vs. decentralization
Data lakehouses are largely centralized repositories designed to store vast amounts of data in a scalable and cost-effective manner while introducing structured governance, ACID transactions, and schema enforcement
In contrast, data mesh is not about technology but about organizational design. Data mesh decentralizes data ownership, distributing responsibilities to individual domains or business units, fostering a more collaborative and scalable approach.
Data governance: data lake vs data mesh
Data lakehouses improve governance compared to traditional data lakes by introducing schema enforcement, ACID transactions, and support for fine-grained access controls. However, governance in a lakehouse is still largely centralized, managed by a core data platform team.
Data mesh integrates federated computational governance, enabling each domain to have autonomy over its data while ensuring overall compliance. Data mesh ensures that each domain or business unit owns and manages its data, promoting self-serve data infrastructure and accessibility.
Data lakehouse vs data mesh: Scalability
Data lakehouses offer similar scalability to data lakes for large volumes of structured and unstructured data. However, scalability in a lakehouse is still primarily technical, centralized, and managed by a core data platform team.
Data mesh addresses scalability by distributing data ownership, allowing each domain to independently optimize storage and compute, resulting in a more scalable solution.
Can data lakehouses and data mesh work together?
Data lakehouse and data mesh are not competing ideas; they complement each other. A lakehouse provides the technical backbone, combining the scalability of data lakes with the reliability and transactional integrity of data warehouses. Organizations can apply data lakehouse technologies and data mesh principles, such as domain-oriented ownership and federated governance, to decentralize responsibility and treat data as a product. This hybrid approach delivers the best of both worlds: the performance and consistency of a unified architecture with the agility and autonomy of a distributed organizational model.
How Starburst helps with your data lakehouse and data mesh strategy
“Data Mesh is certainly the future for our business, and probably for many others, particularly those that have a legacy of acquisitions, and the need for merging of different data sets to form a new, larger entity. Having the ability to query data where it resides using Starburst is enormously powerful and makes a huge impact on the ability for data to provide answers.” Richard Jarvis, CTO, EMIS Group.
FAQs about data lakehouse vs data mesh
What is the main difference between a data lakehouse and a data mesh?
The primary difference lies in their approach to architecture and organization. A data lakehouse is a technological solution, and a data mesh is an organizational methodology. A data lakehouse unifies the flexibility of a data lake with data warehouse management in a centralized system, whereas a data mesh decentralizes data ownership to specific business domains. While the lakehouse solves storage and processing technicalities, the mesh solves the organizational bottlenecks of centralized data teams.
Can a data lakehouse and data mesh be used together?
Yes, these two concepts are often complementary rather than mutually exclusive and can be integrated into a hybrid data strategy. Organizations can utilize data lakehouse technology as the underlying infrastructure to store and process data while applying data mesh principles to manage how that data is owned and shared across business domains. This combination allows companies to leverage robust storage performance while fostering the agility and domain autonomy of a mesh architecture.
How does data governance differ between these two approaches?
In a traditional centralized environment, such as a data lakehouse, a single team typically enforces governance policies uniformly across the entire data repository. In contrast, a data mesh implements federated computational governance, establishing global standards for interoperability and security while granting individual domains autonomy to manage their specific data compliance needs. This distributed model in a data mesh prevents the governance bottlenecks often associated with large, monolithic data platforms, such as data lakehouses.
Why is data mesh considered more scalable for large enterprises?
Data mesh improves scalability by removing the dependency on a central IT team to manage all data ingestion, transformation, and quality assurance. By distributing these responsibilities to domain-specific teams who treat their data as a product, organizations can parallelize data work and reduce the backlog that often plagues centralized data lakes, lakehouses, or warehouses. This decentralized approach allows the data infrastructure to grow organically alongside the business units without hitting performance or process limits.
