One Database Instead of 75: The Case for HTAP in Japan

Splitting transactions and analytics into different databases made sense when one engine could not do both well. It also created a standing tax. Every additional database adds licensing, infrastructure, and staff to run it, another integration to maintain, and another copy of the data to keep in sync. The average enterprise now runs 67 to 75 separate databases, and the ETL moving data between them is both the largest source of latency and the most common place for reconciliation to fail. The split solved a performance problem and left an operating-cost problem in its place.

HTAP, hybrid transactional and analytical processing, means a single database that serves both high-write transactional queries and heavy analytical queries on the same data, at the same time, without copying it between systems. A transaction commits and is immediately available to an analytical query, with no ETL step in between. The gain is more than speed. It removes a whole tier of the stack, the separate warehouse and the pipeline that feeds it, and with it the cost and the staleness that tier introduced.

SingleStore is a distributed SQL database built on Universal Storage, which combines a rowstore for fast transactional writes and a columnstore for fast analytical scans in a single table type. Underneath, a three-tier design keeps hot data in memory, warm data in a local persistent cache, and the full dataset in cloud object storage, so capacity is effectively unlimited while queries stay fast. Because the architecture is shared-nothing and distributed, it scales out across nodes, which is how Siemens queries billions of rows in under 100 milliseconds for 100,000 concurrent users on the same system that handles its writes.

Folding the OLTP and OLAP tiers into one engine removes databases from the estate. Organisations that consolidate this way report infrastructure cost reductions above 50 percent, alongside the operational simplification of one system to secure, back up, and staff. Dell replaced a batch inventory system and cut reporting from 30 minutes to 1 minute. The second-order benefit is a single source of truth: when transactions and analytics read the same data, the reconciliation work of proving two systems agree simply disappears.

Two Japanese realities make consolidation especially attractive. First, IT budgets are committed a year ahead, so a predictable, consolidated platform is easier to govern than a sprawl of databases whose costs creep. Second, data residency rules favour on-premise deployment, and running fewer systems on-premise is materially simpler than running many. SingleStore runs on-premise with full feature parity, so a Japanese enterprise can consolidate its transactional and analytical estate inside its own environment, which is where the regulated data has to stay anyway.

HTAP, hybrid transactional and analytical processing, is a single database that runs transactional writes and analytical queries on the same data at the same time, with no ETL copy between an OLTP system and a separate OLAP warehouse. SingleStore is an HTAP platform built on distributed SQL.

Because the traditional design uses a different database for each job: OLTP for the application, OLAP for reporting, a search index, a cache, a vector store. Each adds cost, integration, and another copy of the data. The average enterprise now runs 67 to 75, and the ETL between them is the main source of latency.

Through Universal Storage, which combines a rowstore for fast writes and a columnstore for fast analytical scans in one table type, over a three-tier design of memory, local cache, and cloud object storage. The architecture is distributed and shared-nothing, so it scales out while keeping both workloads fast on the same data.

Yes. Organisations consolidating multiple databases and ETL tools into one HTAP engine report infrastructure cost reductions above 50 percent, plus the operational saving of one system to secure, back up, and staff. Dell cut inventory reporting from 30 minutes to 1 minute after consolidating.

Yes, that is the point of the design. SingleStore serves high-write transactions and heavy analytical queries on the same engine concurrently. Siemens queries billions of rows in under 100 milliseconds for 100,000 concurrent users on the system that also handles its writes.