QuestDB
QuestDB provides a unified SQL engine for time-series data that ingests millions of events per second, computes on live data in milliseconds, and keeps years of history queryable across hot and object storage tiers. The platform supports time-series-specific SQL primitives like SAMPLE BY, ASOF JOIN, and HORIZON JOIN, along with materialized views for precomputed aggregates. It is used in capital markets, aerospace, energy, and retail banking for workloads ranging from tick-data analytics to telemetry ingestion.
- Data & Analytics
- Enterprise Software
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Traditional time-series databases struggle to handle both high-velocity live data ingestion and long-term historical analytics in a single system, forcing organizations to stitch together separate databases, stream processors, and storage tiers. This fragmentation adds operational complexity, increases latency, and makes it difficult to query across real-time and historical data with consistent performance.
Solution
QuestDB provides a unified SQL engine that ingests millions of events per second, computes on live data in milliseconds, and keeps years of history queryable across hot storage and object storage tiers. The platform supports time-series-specific SQL primitives like SAMPLE BY, ASOF JOIN, and HORIZON JOIN, along with materialized views for precomputed aggregates. Built-in durability, deduplication, and out-of-order correction ensure data integrity at scale. QuestDB also integrates with open formats like Apache Parquet and Arrow, enabling seamless interoperability with dataframes and external analytics tools.
Target Audience
Primary users are engineering and data teams in capital markets, crypto, aerospace, retail banking, and energy sectors that need to process high-velocity time-series data with low latency and retain long history for analytics.
Features
- Column-oriented, time-partitioned storage engine with memory-mapped files and vectorized (SIMD) execution for high-throughput ingestion and millisecond-level queries
- Time-series SQL extensions including SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN, and HORIZON JOIN
- Materialized views for precomputed candles and intraday aggregates with sub-millisecond reads
- Multi-tier storage with cold storage tiering to S3, GCS, Azure Blob, or NFS, keeping partitions queryable in place
- Native Parquet and Arrow support for open data lake integration and dataframe interoperability
- High availability with restart-free failover, durable acknowledgments, and automatic replica promotion
- QWP binary protocol with a single client for both writes and reads, including buffering during server failover
- Web Console with notebooks for dashboards and interactive analysis