Database
Time Series Databases
Specialized storage and querying for time-stamped data — metrics, sensor readings, financial tick data, logs, and events. We design and operate time-series infrastructure that handles millions of data points per second.
Why Time Series Databases?
General-purpose relational or document databases are poorly suited for time-series workloads. Insert rates of thousands of rows per second, retention policies that automatically expire old data, downsampling for long-term storage, and specialized time-window aggregations all require databases designed specifically for temporal data.
Time-series databases achieve 10x–100x better performance than general-purpose databases for these workloads — through compression, columnar storage, and time-based partitioning.
Databases We Work With
InfluxDB
Purpose-built time-series database with Flux query language. Ideal for IoT, infrastructure metrics, and real-time analytics.
TimescaleDB
PostgreSQL extension that adds hypertables, continuous aggregates, and compression. Best-of-both-worlds for teams with existing PostgreSQL expertise.
Prometheus
Pull-based metrics collection with PromQL. The standard for infrastructure monitoring and Kubernetes metrics.
QuestDB
High-performance TSDB for financial data and high-frequency analytics with SQL interface.
What We Deliver
- → Schema and tag/field design optimized for your query patterns
- → Ingestion pipelines from IoT devices, application metrics, or financial feeds
- → Retention policies and downsampling rules for cost-efficient long-term storage
- → Grafana dashboards for real-time and historical data visualization
- → Alerting on metric thresholds and anomaly detection
- → High availability setup with replication and automated failover