Tiger Data & Ignition
More data in.
Faster queries out.
Pair Ignition with TimescaleDB for high-throughput ingestion and fast queries as your industrial data grows.
We help you configure the floor to database route, plan historian migration, and test ingestion rates and query latency against your workload.
Time-series performance for Ignition
Historian workloads need sustained writes and fast reads over growing datasets. TimescaleDB extends PostgreSQL for time-series workloads, with an emphasis on ingestion throughput and query performance while retaining SQL access. We test both writes and reads against your sampling rates, retained history, hardware, and concurrent load.
In April 2026, Inductive Automation and Tiger Data announced a collaboration focused on industrial historians, with Tiger Data joining the Ignition Technology Ecosystem as a Gold Technology Provider. The collaboration spans TimescaleDB Enterprise for self-managed deployments and Tiger Cloud for managed analytics.
Read the official partnership announcement →
How we help
What we validate before rollout
- Baseline. Record tag counts, sampling rates, retention requirements, database size, and current query timings.
- Load test. Run representative writes and reads together. Compare ingestion throughput and query latency against the baseline, and check data completeness and query results.
- Rollout checks. Test recovery procedures, define rollback criteria, and document monitoring and operational responsibilities.
The configuration depends on your Ignition version, historian setup, and chosen Tiger Data deployment.
Faster queries. Less waiting.
A full day of history across 60 tags, queried in 3.3 seconds instead of 52.7. For the four-hour window, the query dropped from 9.4 seconds to just 0.6. That is the difference between waiting for results and getting on with the work.
We built an Ignition 8.3 Perspective dashboard connected to two tables, each containing approximately 500 million rows: one optimized with TimescaleDB, the other a standard PostgreSQL table created by Ignition Tag Historian. We queried 60 tags over four-hour and 24-hour windows, returning averages at one-minute intervals.
| Query window | With TimescaleDB | Standard PostgreSQL |
|---|---|---|
| 4 hours | 0.6 seconds | 9.4 seconds |
| 24 hours | 3.3 seconds | 52.7 seconds |
And that is without continuous aggregates. These results came from querying the underlying history directly. Continuous aggregates could reduce query times even further.
For people investigating a process, checking a trend, or exploring historical data in Ignition, this makes a substantial difference: a query that took nearly a minute returned in a few seconds in our tests.
4-hour query comparison
24-hour query comparison
Let’s talk about your historian
Send us your Ignition version, current historian, approximate write rate, retention period, and any queries that are too slow. We can use those details to scope an integration or performance evaluation.