Modern architecture patterns and frameworks for building reliable, scalable data-intensive applications in the AI era.
Stateless workers/jobs/functions still requires stateful orchestration. A swarm of AI agents demands on meaningful and reliable context among each handover. Data dependencies and expectations are the crtical building blocks in the landscape of AI-generated transformations and analytics.
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.
Why data type conversion is a real problem the moment data crosses a system boundary — a cross-engine type matrix covering ClickHouse, Arrow, Parquet, Vortex, Spark, Flink, Iceberg, Delta, Hudi, Paimon, Protobuf, Avro, Thrift, Postgres, Snowflake, StarRocks, DuckDB, and Trino.
The Clock Is Not a Dependency: Orchestration for the Agentic Data Era. Airflow 3.x has finally added more senors to incorporate the pattern of triggers. Let's evaluate the practical trend which the data-intensive workflows and jobs need to orchestracted with (some level of) knowledge: data content (esp. watermark, completeness/uniquness, quality, MV status), compute resource, SLA defined by downstreams, failure clues/metadata. How to fuse dependencies across Airflow, Temporal, Lakeflow, BPM, N8N? Or pushing all kinds of workflow into Airflow?
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.
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