Introduction to the well-architected data lakehouse
As a cloud architect, when you evaluate a data lakehouse implementation on the Databricks Data Intelligence Platform, you might want to know “What is a good lakehouse?” The Well-architected lakehouse articles provide guidance for lakehouse implementation.
At the outset, you might also want to know:
- What is the scope of the lakehouse - in terms of capabilities and personas?
- What is the vision for the lakehouse?
- How does the lakehouse integrate with the customer’s cloud architecture?
Articles about lakehouse architecture
The scope of the lakehouse
The first step to designing your data architecture with the Databricks Data Intelligence Platform is understanding its building blocks and how they would integrate with your systems. See The scope of the lakehouse platform.
Guiding principles for the lakehouse
Ground rules that define and influence your architecture. They explain the vision behind a lakehouse implementation and form the basis for future decisions on your data, analytics, and AI architecture. See Guiding principles for the lakehouse.
Downloadable lakehouse reference architectures
Downloadable architecture blueprints outline the recommended setup of the Databricks Data Intelligence Platform and its integration with cloud providers’ services. For reference architecture PDFs in 11 x 17 (A3) format, see Download lakehouse reference architectures.
The seven pillars of the well-architected lakehouse, their principles, and best practices
Understand the pros and cons of decisions you make when building the lakehouse. This framework provides architectural best practices for developing and operating a safe, reliable, efficient, and cost-effective lakehouse. See Data lakehouse architecture: Databricks well-architected framework.