SingleStore + Confluent

Every hop between the stream and the query costs you freshness.

In most cloud warehouse setups, Kafka data doesn’t go straight to somewhere you can query it. It passes through connectors, a stream-processing tier, a staging layer in object storage, then batch processing before it’s finally queryable – often reshaped into Iceberg and worked over by Flink, Spark or dbt on the way. Every step is defensible on its own. Added up, they turn “the instant it happened” into “sometime in the last few hours.” Confluent was built to deliver freshness. By the time the data lands somewhere useful, most of that freshness is gone.

There’s a much shorter path from stream to query.

Paired with Confluent, SingleStore acts as the live serving layer: it ingests Kafka directly into distributed tables, with no middleware, no staging files, no batch reprocessing in between. Kafka partitions map straight to ingest threads, so throughput scales sideways as your data grows. Delivery is exactly-once and schema-aware, and row-level upserts and deletes update state in place – so what you query is the current picture, not last night’s snapshot. Data is queryable in sub-second time, and the same live copy serves operational, analytical and AI workloads at once.

Confluent, now an IBM company, moves the data. SingleStore makes it instantly usable. Neither replaces the systems you already run.

Shift the work left. Serve the context right.

Under the hood it’s two moves. Confluent shifts the work left – validating, enriching and governing data on the stream itself, close to the source, so it arrives trusted and ready instead of reshaped five times on the way down. SingleStore then serves that live data right into your applications, with current context – the dashboard, the risk decision, the AI answer all working from what’s true this second, not last night’s snapshot.

Where this actually lands.

One engine, instead of four.

The usual way to cover all of that is to stitch systems together: an operational database here, an analytics engine there, a bolt-on vector store, a separate full-text index – four systems, four copies of the data, four things to keep in sync, and a standing bill in engineering time to stop them drifting apart. SingleStore runs transactional, analytical, vector and full-text workloads side by side in a single engine, on the same live data: HTAP without replica sprawl, vector retrieval and RAG as first-class SQL, full-text and structured filters in one query, JSON and relational data living together. One place, one copy, one answer.

Proven where the numbers have to be right.

SingleStore runs in production behind streaming infrastructure across banking, industrial and B2B SaaS environments – its own deployments include Goldman Sachs, Millennium bcp, Siemens and Cognism. It’s available fully managed on AWS, Azure and GCP, or self-managed, and exports natively to Apache Iceberg, so the fast serving layer stays fast while the wider estate stays connected and consistent.

The bit that isn’t on the datasheet.

Direct-to-table ingest is elegant on a diagram. In practice, a real-time layer still has to be built into a live estate – Kafka pipelines wired up properly, schema evolution handled cleanly, offsets and exactly-once delivery behaving under retries and failures, the whole thing slotted alongside the mainframe or Oracle systems without taking anything offline mid-day.

Getting that right is the craft, and it’s where Dot Group lives. This real-time serving layer is the backbone of our Accelerate work, turning streaming data into sub-second answers for live analytics, risk and operational intelligence. We implement SingleStore alongside Confluent for streaming ingestion and IBM watsonx.data for governance across the estate.