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When it comes to data management, there are various approaches and architectures for storing, processing, and analyzing data. In this article we’ll discuss three of the more popular approaches in the market today – the data warehouse, data mesh, and data lakehouse.
Each approach has its own unique features, advantages, and disadvantages, and understanding the differences between them is crucial for organizations to make informed decisions about their data strategy. We’ll take you through each one and help you determine which approach is best suited for your organization’s data needs.
Data Warehouse: Centralized but Inflexible
A Data Warehouse is a centralized repository that stores structured data from various sources for analysis and reporting. Typically it’s a relational database and optimized for read-heavy workloads with a schema-on-write approach.
Advantages of a data warehouse are that it’s a single source of truth for structured data, it provides high performance querying and dashboarding/reporting capabilities, and it supports business intelligence and analytics use cases.
On the other hand, some of its disadvantages are that it requires data to be pre-processed and structured, it has limited flexibility in handling unstructured data and new data types, and it can be expensive to implement and maintain.
Learn more about choosing between data warehouse and data lake.
Data Mesh: Flexible but Complicated
A Data Mesh is a distributed and decentralized approach to data architecture that focuses on domain-driven design and self-service data access. Key features include decentralized data ownership and control, data that’s organized by domains rather than centralized by function, data is emphasized as a product that is discoverable and reusable, and data access is self-service for domain teams.
Advantages of a data mesh are that it offers agility and flexibility in handling complex and evolving data environments, it facilitates collaboration between data teams and domain teams, and it promotes data democratization and data-driven culture.
Disadvantages are that it requires a cultural shift and new ways of working to implement, distributed data ownership involves data governance and security challenges, and it requires strong data lineage and metadata management to ensure data quality and consistency. Performance can also be a problem if you’re doing joins across many data sources, because your query will only be as fast as your slowest connection.
Data Lakehouse: Hybrid Approach
A Data Lakehouse is a hybrid approach that combines the best features of data warehouses and data lakes. Those features include support for both structured and unstructured data, support for both read and write-heavy workloads, and a schema-on-read approach.
Advantages of a data lakehouse are that it offers flexibility in handling both structured and unstructured data, it supports real-time analytics and machine learning use cases, and it’s cost-effective compared to traditional data warehouses. They’re designed to handle both batch processing and real-time processing of data.
Disadvantages are that it requires data governance and management policies to prevent data silos and ensure data quality, complex data integration and transformation may require specialized skills and tools, and there may be performance issues for ad-hoc queries and complex joins.
Picking the data architecture that’s best for your use case
Below is a matrix we’ve put together that lists which of these approaches best fits specific requirements and use cases.
| Data Warehouse | Data Mesh | Data Lakehouse | |
| Structured data | ✅ | ✅ | ✅ |
| Unstructured data | ❌ | ✅ | ✅ |
| Fast access to data | ✅ | ❌ | ✅ |
| Real-Time Data Processing | ❌ | Possible with additional tools | ✅ |
| Data Governance | Centralized | Decentralized | Centralized or Decentralized |
| Cost-effective | ❌ | Depends on specific use case | ✅ |
| Scalability | ✅ | ✅ | ✅ |
| Self-service data discovery | ❌ | ✅ | ✅ |
| Data Integration | ✅ | Requires specialized work | ✅ |
| Analytics capabilities | ✅ | Evolving | ✅ |
As shown in the matrix, each architecture has its own strengths and weaknesses across different key capabilities.
A data warehouse architecture is well-suited for structured data, offers strong data governance, and mature analytics capabilities, but may be limited in its scalability and ability to handle unstructured data and real-time processing.
A data mesh architecture offers highly scalable and decentralized data management, high developer productivity, and flexible data governance, but may require additional tools for real-time processing and careful planning for data integration.
A data lakehouse architecture is well-suited for unstructured data, offers good scalability and data integration capabilities, and is reasonably cost-effective, but may be limited in its ability to handle highly structured data and may require varied data governance strategies.
The Open Data Lakehouse
At Ahana, we’re building the Open Data Lakehouse with Presto at its core. Presto, the open source SQL query engine, powers the analytics on your Open Data Lakehouse. We believe the data lakehouse approach strikes the best balance between flexibility, scalability, and cost-effectiveness, making it a favorable choice for organizations seeking a modern data management solution.
