Decision-Time Analytics

Your streaming stack is fast. Your control systems still disagree.

The problem

Kafka ingests in milliseconds. Flink computes continuously. ClickHouse answers fast — but lags mutations. Actions based on analytics are often based on state that never fully coexisted.

Where drift appears:

Aggregates lag the event — your dashboard shows 5 orders, there are 8 in flight
Serving reads from a replica — the API returns one value, enforcement sees another
Reverse ETL propagates later — the warehouse computed it, serving hasn't synced
Multiple consumers see different snapshots — analytics, APIs, and control systems never agree

At 20K RPS, a 200ms coordination gap admits thousands of actions before downstream systems react. The stack is fast — the state isn't unified. This is fine for dashboards. It fails when analytics drive automated decisions.

How Tacnode solves it

Tacnode collapses ingestion, transformation, and serving into one transactional boundary. No separate stream processor. No warehouse copy. No reverse ETL.

What this means:

Data ingested, transformed, and queryable — in one system
All consumers read from the same committed state
Mutations are transactional — no coordination gaps
No pipeline orchestration — transformations execute as data arrives

Not a Kafka → Flink → warehouse chain. One transactional boundary. No reverse ETL. This is the operational substrate.

Key Capabilities

Incremental Materialized Views

Define transformations declaratively in SQL. They execute continuously as data arrives — no external orchestration, no batch scheduling.

Data Lake Integration

Query Iceberg tables directly alongside streaming data. Unify your data lake and real-time analytics without moving data.

Tiered Storage

Hot data stays in high-performance storage; cold historical data moves automatically to cost-effective object storage. Query both seamlessly.

PostgreSQL Compatible

Use existing tools, drivers, and ORMs. Standard SQL queries work out of the box — no proprietary syntax to learn.

How it works

Streaming Ingest

Continuous Ingestion

Incremental Transforms

Live Queries

Streaming in. Always fresh. Always queryable.

Architecture Highlights

Ingestion, transformation, and serving in one transactional boundary
All consumers read from the same committed state
No reverse ETL — no sync loops back to operational systems
Schema evolution handled automatically — no migration scripts

When you need this

Streaming + warehouse stack feeds real-time decisions
Reverse ETL introduces lag or complexity
Multiple systems must agree at decision time
Sub-second freshness matters beyond dashboards

When you don't

Analytics is primarily historical
Decisions happen offline
Seconds of propagation delay are acceptable
Dashboards don't drive automated action

Common Patterns

Streaming ETL consolidation

Replace Kafka + Flink + warehouse pipelines with one system. Ingest, transform, and serve in a single boundary.