The modern historian for industrial scale
Ignition Enterprise Historian and TimescaleDB. Built to work together.
Designed for the performance and reliability that mission-critical industrial workloads demand, with the native Ignition experience operators and engineers already know.
Changing historian workloads
Industrial data is outgrowing traditional historian architectures
The workload changed.
The historian architecture needs to change with it.
More tags
Thousands → millions
More connected equipment means more time-series streams to ingest and manage.
Higher frequency
Minutes → sub-second
Higher-resolution operations create dramatically more writes.
Longer retention
Weeks → years
History stays valuable for troubleshooting, optimization, and reporting.
More demanding apps
Charts → analytics → AI
The same data now serves more users and more complex queries.
Ignition Enterprise Historian
Ignition Enterprise Historian, powered by TimescaleDB


Purpose-built for Ignition
Use a dedicated historian with simplified setup, not a general-purpose database you have to design and optimize yourself.
Powered by enterprise-grade TimescaleDB
Store and query historian data on a production-ready time-series database built to handle high-volume industrial workloads.
Built and supported together
Developed by Inductive Automation and Tiger Data, with support from both vendors.
Up and running in minutes
Historian setup, integrated into Ignition

01
Deploy
Create a TimescaleDB database cluster in the TimescaleDB Console.
02
Select
Choose TimescaleDB as the historian backend in Ignition.
03
Connect
Provide the connection information for your TimescaleDB Console.
04
Validate & Create
Ignition validates the deployment and configures the historian database.
No separate database design and tuning workflow
Deployment choices
One historian. Three ways to deploy it.
Every deployment includes:
No tag or user limits
Enterprise-grade HA and backups
Operational tooling
Vendor support
TimescaleDB for Ignition
For standard Ignition historian deployments
Single or multiple Ignition gateways
Designed for medium-scale deployments
One standard configuration
Procurement
Through Inductive Automation
Pricing model
Fixed price perpetual license
TimescaleDB Enterprise
For larger, more complex industrial architectures
Multi-level or complex gateway architectures
Flexible sizing and greater deployment control
Can serve as a LiveSync destination
Procurement
Through Tiger Data
Pricing model
Fixed price by server configuration
Tiger Cloud
For fully managed historian deployments in the cloud
Database infrastructure operated by Tiger Data
HA, backups, and upgrades handled for you
Available on AWS, Azure, and GCP
Procurement
Through Tiger Data
Pricing model
Usage-based pricing
A different historian architecture
Built for a different class of workload
Criteria | Ignition SQL Historian | Ignition Enterprise Historian |
|---|---|---|
Database | SQL Server or PostgreSQL | PostgreSQL + TimescaleDB |
Scale | Small → medium | Small → very large |
Performance | Moderate ingest and query workloads | High-rate ingest and fast queries across large histories |
Data scope | Tag history + relational data | Tag history + full operational context |
Storage | General-purpose row storage | Relational row storage + compressed columnar tag history |
Query processing | Raw history pulled into Ignition for processing | Filtering and aggregation executed in TimescaleDB |
Database Operations | Your team configures and operates the database | Built-in HA, backup, monitoring, and lifecycle management |
Performance at industrial scale
Performance benchmarks
| Metric | SQL Historian + PostgreSQL | SQL Historian + TimescaleDB Community (OSS) | Enterprise Historian |
|---|---|---|---|
On-Disk Footprint | 217 GB | 17 GB | 21 GB |
Sustained Ingest | 77,500 pts/sec | 86,400 pts/sec | 1,040,000 pts/sec |
1 Year Aggregation (Raw) | 131 sec | 78 sec | 9.8 sec |
1 Month Aggregation (Raw) | 8.3 sec | 6.2 sec | 0.7 sec |
Aggregate execution | Computed in Ignition from raw history | Computed in Ignition from raw history | Executed natively in TimescaleDB |
Identical test configuration: 16 vCPU / 128 GB RAM. 2.1 billion record dataset.
Why it's faster
Automated partitioning
Incoming data is automatically partitioned by time, and irrelevant partitions are pruned from historical queries.
Compressed columnar storage
Tag history is compressed in columnar form, allowing queries to read only the columns they need.
Native historian ingest
History is written through a historian-specific data path designed for sustained, high-rate ingest.
Database-side analytics
Filtering and aggregation run inside TimescaleDB, while continuous aggregates maintain common rollups.
// Private preview
Interested in Ignition Enterprise Historian or TimescaleDB Enterprise?
Talk to Tiger Data about architecture, deployment, and private-preview access.