Real Time Data in Japan: Why CDC + Unified OLTP/OLAP Is Winning

The traditional data pipeline moves data from transactional systems (OLTP) to a separate analytical warehouse (OLAP) via batch ETL jobs, typically running overnight. This worked when business decisions could wait 24 hours. It does not work when a fraud detection system needs to flag a suspicious transaction in 50 milliseconds, or when a real time personalization engine needs the latest user behavior from the last 5 seconds. Every layer of the legacy stack, from the batch window and the staging database to the transformation logic and the load into the warehouse, introduces latency and reconciliation complexity. By the time analytics run, the data is already stale.

The modern alternative uses change data capture (CDC) to stream every transaction from the source database to a unified analytical engine in subsecond latency, without affecting the source system's performance. Striim reads the transaction logs of Oracle, SQL Server, PostgreSQL, and other enterprise databases, streams those changes through an inflight transformation pipeline, and delivers them to the target in real time. SingleStore then serves as that target, a distributed SQL database that handles both transactional and analytical queries on the same engine, eliminating the OLTP/OLAP split entirely.

Consider a Japanese bank running fraud detection. In the legacy model, transactions write to Oracle, nightly ETL moves data to a Teradata warehouse, and fraud analytics run against yesterday's data, meaning fraud is discovered hours or days after it occurs. In the modern model, Striim captures the Oracle transaction log, enriches each transaction with customer context and historical patterns inflight, and writes to SingleStore in subsecond latency. A fraud detection query runs against live data continuously. Suspicious transactions are flagged before they complete. The difference between 24-hour detection and 50 millisecond detection is the difference between losing the money and stopping the fraud.

Two factors make this architecture especially relevant for Japanese enterprise workloads. First, Japan's transaction volumes are among the highest in the world. Japanese financial services firms routinely need throughput that crashes less demanding databases, and both SingleStore and Striim have been battle tested at this scale. These are not theoretical benchmarks: Dell cut inventory reporting from 30 minutes to 1 minute, Siemens queries billions of rows in under 100 milliseconds for 100,000 concurrent users, and the carrier Optus surfaced more than 10 million dollars in revenue leakage after moving to this architecture. Second, Japanese enterprises have strict data residency requirements that favour on premise or private cloud deployments, and both platforms run on premise with full feature parity to their cloud offerings, which most modern data tools lack.

The same architecture pays off again for AI. Because a unified engine already holds the live operational data, it can serve vector search and retrieval from the same place, with no copy into a separate vector database. SingleStore stores SQL, JSON, full text, and vectors together and runs vector search alongside transactional queries, which is what makes retrieval-augmented generation practical on current data instead of a stale nightly snapshot. For a Japanese enterprise standardising its data platform, this folds the real-time analytics stack and the AI-application stack into one system, and it is the reason a data-engineering decision and an AI-readiness decision are now the same decision.