Connect your database. Build real-time features without touching your app.
Your database already knows when things happen. Every INSERT, UPDATE, and DELETE is an event. TypeStream turns your transaction log into a stream of opportunities. without you writing CDC code or operating message queues.
The Problem
You need to send a Slack message when a high-value order is placed. Or fire a webhook when a user churns. Or call an API when inventory drops below threshold. Today, you'd write a cron job that polls the database every minute. It's wasteful, laggy, and yet another thing to monitor.
With TypeStream
Connect your database. Drag a filter node ("order_total > 1000"). Connect it to a Slack node. Deploy. Now, within seconds of that row being written, the message fires. No polling. No queue to operate. No code.
Why It Matters
This is the simplest demonstration of the core value. If your database could always do this, you'd have done it years ago. Now it can.
The Problem
Your sales team lives in Salesforce. Your product data lives in Postgres. Keeping them in sync means brittle integrations, scheduled jobs, and data that's always slightly stale.
With TypeStream
Changes flow from your database to Salesforce as they happen. When a customer upgrades their plan, the Salesforce record updates within seconds, not overnight.
Why It Matters
"Real-time sync" sounds like table stakes, but most companies don't have it because the plumbing is annoying. TypeStream makes it a drag-and-drop operation.
The Problem
Your checkout flow calls Stripe, then your fraud provider, then inventory, then email, all synchronously. One slow response and the whole thing hangs. One outage and customers can't buy. You know you should make these async, but building a queue, managing retries, handling failures. It's a quarter of engineering work.
With TypeStream
Your checkout writes an order row and returns. TypeStream picks up the change and fans out to Stripe, fraud, inventory, and email in parallel, with automatic retries, backoff, and dead-letter handling.
Why It Matters
This is the architecture everyone draws on whiteboards but few actually ship. TypeStream makes it the default, not a six-month project.
The real power isn't just reacting to changes, it's transforming them. TypeStream gives you a library of enrichment nodes that add intelligence to your data before it lands anywhere.
TypeStream includes a library of enrichment nodes for common transformation patterns. Chain them together to build powerful pipelines without writing code.
GeoIP Node
Convert IP addresses to country, city, and region. Every event arrives geo-tagged and ready for location-based analytics.
Map Node
Add derived fields like days_since_signup or order_value_tier. Your data arrives shaped for the questions you'll ask.
OpenAI Transformer
Categorize, summarize, or score with a prompt. Auto-tag support tickets, classify leads, or extract entities in real time.
Embedding Generator + Text Extractor
Build RAG pipelines in 3 nodes: extract text from documents, generate embeddings, and sink to Weaviate for semantic search.
StreamSource (unwrapCdc)
Extract the "after" payload from Debezium CDC envelopes. Work with clean row data instead of complex change event structures.
Count, WindowedCount, Group
Compute real-time metrics like events per minute, rolling averages, or grouped counts. Stream aggregations without batch jobs.
Once your data is flowing and enriched, the last mile is exposing it. TypeStream makes your transformed data instantly queryable: REST endpoints, GraphQL, or semantic search.
The Problem
Frontend needs an endpoint for "active users with their recent orders." Backend has a two-week backlog. So frontend builds a workaround, and now you have two sources of truth.
With TypeStream
Point at a materialized view or a transformed stream. Get a REST or GraphQL endpoint. Frontend unblocked, backend backlog unchanged.
Why It Matters
This isn't about replacing your backend. It's about not making frontend wait for read-only endpoints that are just projections of your data.
The Problem
Product wants "search that understands what users mean." Engineering hears "provision a vector database, set up embedding pipelines, build a query layer, and oh by the way keep it in sync with production."
With TypeStream
Your data is already flowing through embedding nodes. Expose it as a semantic search endpoint. Done.
Why It Matters
Semantic search is the feature everyone wants and few ship because the infrastructure lift is too high. TypeStream makes it a configuration, not a project.
The Problem
You want live updates in your UI: new comments appearing, dashboards refreshing, notifications popping. WebSockets are fiddly. Polling is wasteful.
With TypeStream
Subscribe to a stream from your frontend. Changes push to connected clients as they happen via Server-Sent Events.
Why It Matters
Real-time UI feels premium. TypeStream makes it easy enough that you'll actually ship it.
When you need to move data somewhere else, do it without the 3am pages. Migrations and replications are terrifying because they're all-or-nothing. TypeStream makes them incremental, pausable, and testable.
The Problem
You're moving from one database to another. The cutover plan involves a maintenance window, a prayer, and someone's finger on the rollback button.
With TypeStream
Start streaming changes to the new database. Let it catch up. Validate. Pause if something looks wrong. Resume when you're confident. Cut over when you're ready.
Why It Matters
Migrations become deployments, not events.
The Problem
You're syncing data to a warehouse. A column type changes in production. The sync breaks at 2am.
With TypeStream
Schema mismatches are caught before they hit the destination. You get an alert, not a page.
Why It Matters
Schema drift is inevitable. Silent failures shouldn't be.
The Problem
You want to test a new transformation against real data, but you can't safely replay production traffic.
With TypeStream
Capture a slice of production changes. Replay them through your staging pipeline. Compare outputs.
Why It Matters
Confidence in changes comes from testing against reality, not synthetic data.
The difference between a demo and production is error handling, observability, and operational maturity. TypeStream bakes these in.
OpenAI returns a 429. TypeStream handles it automatically: rate limits, retries, and queueing are built in. Your pipeline adapts; you don't get paged.
Your pipelines are code. Version control them. Review changes in PRs. Deploy through CI/CD. No clicking around a UI in production.
See what's flowing. See what's stuck. See why. Metrics, logs, and traces are built in. When something goes wrong, you'll know where to look.
TypeStream runs entirely in your environment. Your data never leaves your infrastructure.
Deploy to your Kubernetes cluster or run with Docker Compose. You control the infrastructure.
No data leaves your VPC. Meet compliance requirements for HIPAA, SOC 2, and GDPR by default.
Inspect the code. Contribute improvements. No vendor lock-in. Your pipelines are portable.
Built on battle-tested open source. Managed so you don't have to be.
PostgreSQL or MySQL. Both have Change Data Capture (CDC) logs recording every INSERT, UPDATE, and DELETE.
We use Debezium (open source) to listen to your CDC and emit every change as an event onto a Kafka topic.
Events land on Kafka, the industry standard for streaming. Durable, scalable, and battle-tested.
Your visual transformations compile down to efficient Kafka Streams applications. No JVM tuning required.
We use Kafka Connect with hundreds of pre-built connectors to push data to any destination.
PostgreSQL, MySQL, Weaviate vector database, or Kafka topics. More connectors coming soon.
All of this is managed by TypeStream. You don't configure Debezium, operate Kafka, or tune Kafka Streams. You draw pipelines. We handle the infrastructure.
Connect your Postgres or MySQL and ship your first real-time feature in an afternoon.