What's the Best Big Data Architecture for You? • Christoph Windheuser • GOTO 2024

Modern data stacks are cloud-based collections of tools and technologies designed to gather, store, process and analyze data with scalability and versatility

Three major architectural patterns dominate big data:

• Lake house (combines data lake and warehouse capabilities)
• Modern data stack (cloud-based tools stitched together)
• Data mesh (decentralized organizational approach)

Data mesh represents a business transformation approach rather than just a technical pattern:

• Treats data as a product
• Distributes data ownership across business domains
• Requires product owners and clear governance
• Focuses on data democratization

Key requirements for modern big data architectures:

• Support for all types of data (structured, unstructured, streaming)
• Scalability in both storage and compute
• Cost effectiveness through cloud-based consumption pricing
• Data governance and metadata management capabilities
• Support for multiple use cases (analytics, ML, AI)

Lake house architecture provides:

• Single source of truth with data lake at core
• ACID transaction support
• SQL query capabilities directly on data lake
• Simplified architecture compared to separate lake/warehouse

Important considerations for implementation:

• Data quality and trustworthiness
• Clear data ownership and governance rules
• Proper compute resource allocation
• Cost management of cloud resources
• Change management across organization

Future trends point toward:

• Increased AI/ML integration in data architectures
• Simplified management through AI assistance
• Greater focus on business domain-driven approaches
• Continued evolution toward serverless and autonomous optimization