This approach can lead to high implementation costs and typically does not provide the same user experience as a native lakehouse platform: Users are limited by inconsistencies between tools and a lack of collaboration capabilities, and often have to go through complex processes to gain access to the system and thus to the data.Īn integrated lakehouse on the other side provides a consistent user experience across all workloads and therefore increases usability. However, these tools must be properly assembled to provide all the functionality, with each service offering a different user experience. Azure already has a variety of data tools that perform most tasks a data-driven enterprise might need. By minimizing the copies of your data and moving to a single data processing layer where all your data governance controls can run together, you improve your chances of staying in compliance and detecting a data breach.Īnother important tenet of the lakehouse is to provide a great user experience for all the personas that work with it, and to be able to interact with a wide ecosystem of external systems. To simplify data governance, the lakehouse offers a unified governance solution for data, analytics and AI. This simplifies the modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science, and machine learning.
One of the fundamental aspects of a lakehouse is centralized data governance: The lakehouse unifies data warehousing and AI uses cases on a single platform. The pillars “ Data Governance” and “ Interoperability and Usability” cover concerns specific for the lakehouse.ĭata governance encapsulates the policies and practices implemented to securely manage the data assets within an organization. Data Governance and Interoperability & Usability in lakehouse architectures The well-architected lakehouse extends these with principles and best practices that are specific for the lakehouse and important to build an effective and efficient lakehouse. The well-architected lakehouse extends the Microsoft Azure Well-Architected Framework to the Databricks Lakehouse Platform and shares the pillars “ Operational Excellence”, “ Security” (as “ Security, privacy, compliance”), “ Reliability”, “ Performance Efficiency” and “ Cost Optimization”.įor these five pillars, the principles and best practices of the cloud framework still apply to the lakehouse. Managing costs to maximize the value delivered. The ability of a system to adapt to changes in load. The ability of a system to recover from failures and continue to function. Protect the Azure Databricks application, customer workloads and customer data from threats. The ability of the lakehouse to interact with users and other systems.Īll operations processes that keep the lakehouse running in production. The oversight to ensure that data brings value and supports your business strategy. The well-architected lakehouse consists of 7 pillars which describe different areas of concern for the implementation of a data lakehouse in the cloud:
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