QuestDB announced that HDFC Bank, one of India’s largest banks, is using QuestDB to support real-time transaction monitoring and large-scale analytics across its businesses. The deployment enables low-latency, event-driven decision-making at scale, allowing HDFC Bank to evaluate high-volume transactional data in real time while meeting strict performance and resilience requirements.
HDFC Bank relies on QuestDB’s high-performance time-series engine to evaluate transactions as they occur, supporting event-driven applications for transaction monitoring and risk analytics. In this deployment, a single QuestDB instance can comfortably sustain more than 5,000 queries per second. A primary and replica setup combined with object-storage-based replication provides resilience and fault tolerance. With full separation of compute and storage, HDFC Bank can scale capacity as demand grows without redesigning the architecture.
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HDFC Bank’s transaction monitoring teams continuously refine and operationalize their models using complex time-series transformations executed on the fly in QuestDB. Because QuestDB is built on open standards, the same data can be reused across real-time monitoring, analytics, and model training workflows without proprietary lock-in.
QuestDB’s SQL-first interface enables engineering, risk, and fraud teams to collaborate on a single platform using familiar tools while meeting strict latency requirements and operating at national-scale data volumes.
“HDFC Bank’s use case around real-time transaction monitoring and analytics is exactly the kind of high-throughput, low-latency workload QuestDB was designed to support,” said Nicolas Hourcard, CEO of QuestDB. “By combining open formats, a decoupled storage and compute architecture, and a simple SQL interface, QuestDB helps teams operationalize event-driven applications while keeping data portable.”
This deployment highlights how modern time-series architectures built on open standards can support mission-critical banking workloads, bridging real-time monitoring and analytics while maintaining performance, resilience, and architectural flexibility.
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