Modern architecture patterns and tools for building reliable, scalable data-intensive applications.
Build systems that react to changes in real-time with robust event sourcing and CQRS patterns.
Manage distributed state with consistency guarantees across your entire system.
Optimize for throughput and latency with battle-tested patterns and implementations.
Describe your domain events, aggregates, and projections using our intuitive schema language.
Deploy to your preferred cloud provider with our infrastructure-as-code templates.
Use our SDKs to integrate with your existing codebase in minutes, not months.
Materialized View that can be Incrementally Updated every x hours can replace the corresponding ELT pipelines. With Transparent Query Rewrite, such MVs can play an important role in AI/BI.
AI/BI-powered Analytics have 3 assumptions: [1] dimensional data model [2] transformation pipeline [3] data integrity & quality. However, the realistic gaps still exist in all these areas which lead to lower-than-expected accuracy of AI-for-analytics. This article explains the WHY and HOW to `shift left` and mitigate this from the ground truth level by investing into the true data engineering paradiams.
Do we need to build k8s4ai to replace k8s or build stateful patterns on top of k8s? What should happen in the next 2~3 years.
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