QuestDB GTM brain

QuestDB provides an open time-series database and enterprise platform for engineering teams running high-volume, real-time and historical analytics.

category

Real-time time-series database

market profile

  • text: Market data, telemetry and infrastructure workloads must ingest fast event streams while retaining queryable historical records.
  • text: The time-series market includes specialized databases and general analytical engines, with buyers comparing ingest, query latency, SQL support and operational resilience.

personas

  • title: Market data or trading infrastructure engineering lead
    pains:
    • Cannot drop ticks while ingesting large market data streams
    • Needs low-latency live risk and historical transaction analysis on the same data
  • title: Data platform engineer
    pains:
    • Maintaining separate streaming, analytics and archival systems increases complexity
    • Must handle out-of-order events and production failover
  • title: CTO or head of infrastructure
    pains:
    • Must ensure availability and governance for critical workloads
    • Needs predictable architecture and expert support at large scale

icp

  • text: Investment banks, exchanges and trading firms with high-frequency market data, risk and surveillance workloads.
  • text: Energy, aerospace, robotics and infrastructure organizations processing high-volume time-stamped operational telemetry.
  • text: Engineering-led organizations that can evaluate open source and need enterprise availability, security and support in production.

message pillars

  • text: Combine live events and long-term history.
  • text: Use SQL across the data lifecycle.
  • text: Capture market data without gaps.

objections

  • objection: Can it handle production availability and security requirements?
    response: Assess enterprise replication, failover, access control and the SLA against the workload's recovery and governance requirements.
  • objection: Will historical data be locked into a proprietary format?
    response: Test the documented Parquet and open-format workflow on the buyer's own retention and query requirements.
  • objection: Will it outperform our existing database?
    response: Benchmark representative ingestion, out-of-order data and queries on the buyer's hardware and dataset.

competitors

  • name: InfluxData
    why: InfluxDB is a competing time-series database.
  • name: Timescale
    why: TimescaleDB offers PostgreSQL-based time-series analytics.
  • name: KX
    why: kdb+ is established in financial market data.
  • name: ClickHouse
    why: Real-time analytical database that competes for event analytics.

keywords

  • time series database SQL
  • real time market data database
  • tick data database
  • financial time series analytics
  • high throughput telemetry database
  • QuestDB vs InfluxDB
  • time series Parquet

voice

  • Technical and performance-led
  • Direct
  • Developer-oriented

signals

  • text: A trading team is rebuilding market-data capture, surveillance or intraday risk analytics.
  • text: An engineering team is encountering ingest latency, retention cost or failover limitations in a time-series stack.

stack

  • SQL
  • Apache Parquet
  • Apache Arrow
  • Apache Iceberg

pricing

model: contact_sales

motion

hybrid

channels

  • GitHub
  • developer community
  • SEO
  • LinkedIn
  • sales

confidence

high

Founders

  • name: Nicolas Hourcard
    title: Founder
    role: Founder
    bio: Co-founder/CEO at QuestDB.
  • name: Tancrede Collard
    title: Founder
    role: Founder
    bio: Co-founder and head of product at QuestDB. Before that, I was running algorithmic trading desks at banks and cryptocurrency firms.
  • name: Vlad Ilyushchenko
    title: Founder
    role: Founder
    bio: Founder, CTO, QuestDB 1997 - 2019 Software Engineer, Electronic Trading

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