Recap: What is a Data Lakehouse?

To establish a technically advanced platform for a Data Mesh, constructing a Data Lakehouse is advisable. A Data Lakehouse combines raw data storage in Data Lakes with selective loading into Data Warehouses for functions such as Self-Service Business Intelligence (BI) or Machine Learning (ML) services. This hybrid model merges the strengths of both Data Lakes and Data Warehouses into a cohesive system, rather than operating them independently. The benefits of a Data Lakehouse include [1]:

• Separation of data storage from processing, leading to improved scalability.
• Use of open, standardized storage formats and interfaces.
• Capability to handle various data types, encompassing both structured and unstructured data.
• Support for diverse workloads, including data science, machine learning, SQL, and analytics.
• End-to-end streaming: Streamlined support negates the need for separate systems for real-time data applications.
• Reduced time-to-value in comparison to traditional Data Warehouses.

Data Lakehouse on GCP — Source: Google [2]

The Role of Data Mesh in Enhancing Data Lakehouses

Adopting a Data Mesh framework can significantly enhance the effectiveness of Data Lakehouses as the primary architectural model. It's crucial to recognize that the Data Mesh concept emphasizes a fresh organizational viewpoint rather than solely focusing on technical solutions. Consider these four principles when developing a Data Mesh organization [3]:

1. Domain-oriented decentralized data ownership and architecture: A Data Mesh should cater to the specific needs of individual business units, potentially leading to the establishment of one or more Data Lakehouses.
2. Data as a product: The Data Lakehouse framework facilitates the management of data as a product, granting domain-specific teams comprehensive control over the data lifecycle.
3. Self-serve data infrastructure as a platform: Users can independently access data through self-service BI tools, while data scientists can utilize the same data for model development.
4. Federated computational governance: Data should be secured and disseminated with a defined role structure, supported by data catalogs for better accessibility.

For insights on the technical and organizational requirements for building a data mesh, check out my recent article on implementing a data mesh in the Azure Cloud.

Building a Data Mesh on Microsoft Azure

The Benefits for Businesses

Data Lakehouses play a pivotal role in ensuring that data genuinely adds value to business operations. The following three advantages might drive the ongoing evolution of data platforms in enterprises:

1. Rapid and Scalable Development: Such platforms can be swiftly developed and tailored to meet business needs, without requiring extensive timeframes of one or two years. Major cloud providers offer SaaS solutions that facilitate quick deployment, along with integrable data marts and self-service BI tools that empower businesses to operate independently.
2. Enhanced Data Quality: Effective data governance, monitoring, and clear accountability are essential for maintaining high data quality. The right data is crucial for informed decision-making in business environments.
3. Elimination of Data Silos: Frequently, independently managed data platforms lead to the creation of data silos, which can result in duplicate data storage and increased costs, as well as restricting access to the right individuals. A comprehensive data platform, complemented by tools like data catalogs, is vital for employees to discover and utilize the appropriate data effectively.

Sources and Further Reading

[1] Stefan Koch, Können Lakehouses einen Paradigmenwechsel anstossen? (2021)

[2] Google, Open data lakehouse on Google Cloud (2022)

[3] Michael Armbrust et al., Frequently Asked Questions About the Data Lakehouse (2021)