Case Study

LiveAction leverages QuestDB for AI-driven network security

LiveAction uses QuestDB as a time-series database for storing massive traffic flow and encrypted traffic metadata. Afterwards, it facilitates efficient analysis of their real-time threat detection offering.

Cost reduction
QuestDB is efficient. Reduce resource consumption and better performance? Yes, please!
RESTful API, and more
QuestDB offers seamless integration with existing tech stacks.
SQL compatibility
Simple onboarding with time-series extended SQL for developers.
Visualizing data in a Jupyter notebook querying data from QuestDB.
Avg ingested rows/sec
3M+
Write speed vs InfluxDB
10x
Compression ratio
6x
Cloud up-time
99.99999%
Overview of encrypted traffic analysis

QuestDB enables threat detection

AI-driven network security

LiveAction integrates advanced security technologies into a streaming machine learning pipeline to identify network faults, anomalies, and threats in real-time. QuestDB keeps pace with massive volumes of traffic. Without the ballooning hardware requirements.
Massive throughput analytics
QuestDB handles millions of network events per second.

"QuestDB is impressive and stands out as a superior option. We use it as the basis of our time-series analytics for network threat detection."

Randy Caldejon
VP Product Development, LiveAction

Peak performance analytics

Network Threat Detection at scale

LiveAction leverages QuestDB's high throughput for efficient security analytics.

A diagram showing six patterns of network traffic.
Cost Reduction
Better performance with less resource consumption.
RESTful API
Seamless integration with existing tech stacks.
Clean data-in
Deduplication, out-of-order indexing. Less cleaning, more insights.

Cutting-edge Network Security

QuestDB powers LiveAction's analytics

LiveAction relies on QuestDB for its high-throughput performance and seamless integration with their network security analytics. With native SQL support, QuestDB helps LiveAction detect network threats with ease.
Read the docs
Running ML tooling via Jupyter notebooks to detect outliers.

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