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KAFKA DEVELOPER PATH

Make real-time decisions on your Kafka streams

Connect Volt to your existing Kafka cluster. Write a stored procedure. Get fraud detection, personalization, or IoT decisions in under 10ms. No re-architecting your pipeline.

<10ms

Decision latency

0

Custom services to build

99.999%

High availability

Start the Quick Start → See architecture

How it works: your app sends SQL/JDBC requests to the Volt Active(SP) engine — stored procedures with in-memory ACID — returning results in under 10 ms end to end. No cache layer, 99.999% uptime, predictable p99.

Performance Proof

Kafka alone vs. Kafka + Volt

Stream processors handle data in motion. They don't handle transactional decisions against shared state — which is where real-time fraud, charging, and personalization actually live.

Capability	Kafka + Flink / Spark	Kafka + Volt
Decision latency (p99, stateful)	Flink: 20–100 ms · Spark: 200 ms–2 s	<10 ms — state on the hot path
State semantics	Exactly-once stream processing (per event)	Serializable ACID across multi-record decisions
Stateful read-modify-write	Read state → decide → write back, across hops	Single transaction, co-located with data
Architecture	Kafka + processor + cache + state store + DB	Kafka + Volt — state is the DB
Elastic scaling	Rescale parallelism, typically requires job restart	Online repartition, no job restart
Disaster recovery	Checkpoint + external replication tier	Triple-active XDCR built in
Operational surface	Multiple tiers to monitor, tune, and reconcile	One system for state + decisions

10–30×

Lower p99 decision latency

Measured against tuned Kafka + Flink on stateful read-modify-write workloads (fraud scoring, charging, real-time eligibility).

0 tiers

No cache. No state store. No bolt-on DB.

Volt holds the state, runs the decision, and persists the result — removing the cache invalidation and dual-write problems entirely.

99.999%

Production uptime

Triple-active XDCR replication proven in production at global telcos and tier-one banks.

Flink and Spark Structured Streaming are excellent stream processors. They are not transactional databases. The comparison above is for workloads that require both — low-latency stream ingest and ACID decisions against shared state.

WHAT YOU CAN BUILD

Kafka + Volt use cases

Real-world applications that developers are building today with Volt and Kafka.

Volt Active Data Architecture

◷ 4:00 Architecture Overview Volt Active Data

WATCH & LEARN

Kafka Decisioning

Collapse your pipeline into a single transactional layer by mapping Kafka topics directly to Volt stored procedures. This architecture handles ingestion, storage, and logic in one atomic step, delivering predictable <10ms latency at 1 million+ TPS while eliminating complex consumer-side state and "read-modify-write" cycles.

Enforcing strict ACID compliance at the point of ingestion, preventing race conditions like double-booking or over-provisioning. Achieve exactly-once semantics and total consistency across your Kafka streams without the overhead of external caches or manual offset management.

Kafka Documentation

VoltDB Connector Setup

Installation and configuration

Topic-to-Table Mapping

Schema and data flow

Stream Processing

Patterns and best practices

VoltSP Connector Setup

Streaming pipeline configuration

Performance Tuning

Throughput optimization

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