Generate Streaming Architecture Diagrams with AI

Visualize how events flow through your streaming platform. Describe your Kafka topics, Flink jobs, Kinesis streams, and CDC pipelines in plain English and get a professional streaming architecture diagram ready for data engineering reviews, incident runbooks, or capacity planning.

The challenge

Streaming architectures built on Kafka, Kinesis, Flink, or Pulsar are hard to document because producers, brokers, consumers, and stream processors all evolve independently. The topology of topics, consumer groups, partitions, and stream processors changes with every deployment. Without a current diagram, new engineers can't understand data flow, incident responders can't trace event paths, and capacity planners can't reason about throughput bottlenecks. The real-time nature of these systems means a stale diagram is often worse than no diagram at all.

The solution

Describe your streaming pipeline the way you'd explain it to a new data engineer:

"Events from 3 microservices publish to 3 Kafka topics. A Flink job joins the streams in a 5-minute tumbling window and emits enriched events to an output topic. A downstream consumer writes to PostgreSQL for OLTP and Snowflake for analytics. A dead letter queue captures malformed events for reprocessing. A schema registry validates all producers on publish."

From that description, you get a complete streaming architecture diagram showing producers, topic layout, stream processor logic, sink destinations, error paths, and schema enforcement. Use chat-based editing to add partition counts, consumer group labels, throughput annotations, or replication topology.

Streaming diagrams we support

Kafka topic topology diagram
End-to-end view of producers, topics with partition counts, consumer groups, and committed offsets. Shows schema registry integration and dead letter queues for error handling.
Event streaming pipeline diagram
Full pipeline from event source to sink — including the stream processor (Flink, Kafka Streams, Spark Structured Streaming) in between. Useful for onboarding and design reviews.
Change Data Capture (CDC) architecture
How Debezium captures row-level changes from PostgreSQL or MySQL, publishes to Kafka, and fans out to downstream consumers including search indexes, caches, and data warehouses.
Lambda architecture diagram
Three-layer view — speed layer for real-time processing, batch layer for historical recomputation, and serving layer that merges both views for queries. Shows where each layer reads and writes.
Real-time ML feature pipeline
How streaming events feed a feature store — transformation logic, feature computation, online store writes, and the serving path from feature store to model inference.
Multi-cluster Kafka replication diagram
Cross-region or cross-datacenter replication using MirrorMaker 2 or Confluent Replicator for disaster recovery, geo-redundancy, and active-active cluster setups.

Perfect for

Data engineers designing and documenting real-time pipelines
Platform engineers evaluating Kafka vs Kinesis vs Pulsar for a new system
SREs tracing event paths during incident response and postmortems
Architects designing CDC and event sourcing systems across multiple databases
Technical writers documenting streaming data contracts and consumer responsibilities
Engineering managers presenting real-time analytics architecture to stakeholders

Start Creating - Free

2 free credits. No credit card required.