# Building the Modern Enterprise Historian: TimescaleDB Enterprise Is Now in Public Beta > Source: > Published: 2026-11-03 22:29:38+00:00 A new foundation for industrial history: TimescaleDB performance, enterprise-grade reliability, and integrated operations across sites, on infrastructure you control. We’re announcing the public beta of TimescaleDB Enterprise today. It provides the database foundation for the new Ignition Enterprise Historian, available in Ignition 2027 from Inductive Automation, which also enters public beta today. We’re in the midst of a massive industrial buildout: new factories, expanding energy infrastructure, AI data centers, and existing facilities being modernized and retooled. Operators want greater visibility into what’s happening, tighter control, and better ways to optimize operations. That means more sensors, more software, and a lot more data. We think it’s time to rethink the modern historian. Many legacy proprietary historians brought storage, industrial protocol connectors, analytics, and visualization into one tightly coupled system. That made them convenient to deploy as a single package, but left operators constrained by the system’s performance limits, proprietary interfaces, data formats, and high costs. As AI and new applications change what teams want to do with operational data, architectural decisions made decades ago increasingly limit how quickly they can adapt. Standard relational databases offered a more open foundation. Teams could store sensor history alongside asset information and operational context, then query it with SQL. But as tag counts, sampling rates, and retention grew, those deployments often struggled with ingest throughput, storage costs, and historical query performance. Scaling and operating those deployments reliably often required database expertise beyond the core work of controls and automation teams. That created a hidden operational tax, with gaps that might only become visible during a server failure or an urgent restore. Modern industrial systems need that openness together with a database built for time-series scale. TimescaleDB brings both to PostgreSQL, while TimescaleDB Enterprise adds the reliability and operational capabilities needed to run it as critical infrastructure. Underneath Ignition Enterprise Historian, this provides a foundation for familiar historian applications and makes the same operational data available to new analytics, enterprise applications, and AI. An open data layer with integrated operations Running a historian across a critical plant, a large data center, or an entire fleet requires visibility across the data layer: which deployments are healthy, where data is stored, whether backups are current, and how sites fit together. Management should span those deployments while each site keeps ingesting data and answering queries independently, even if the central management service becomes unavailable. TimescaleDB Enterprise brings these capabilities together on infrastructure you control. Its data plane runs independent TimescaleDB and PostgreSQL clusters that store and query your data. Its control plane provides a shared console for deployment, high availability, backup and recovery, monitoring, and upgrade management. Teams can see the health, topology, and backup status of deployments across sites. You can run the platform at the edge, on-premises, or in your own cloud environment. Secure installation bundles include the software and dependencies needed to install or upgrade in air-gapped environments without access to public package repositories. This foundation supports three priorities: (1) scaling time-series workloads efficiently, (2) providing enterprise-grade high availability and disaster recovery for critical data services, and (3) operating the whole data layer, managing independent database deployments and selectively replicating operational data, aggregates, and context across sites and enterprise systems. Scale and performance: Keep more history and query it faster Scale starts with the database engine. TimescaleDB changes how PostgreSQL stores and queries time-series data. Four capabilities do much of the work: Hypertables: Automatically partition time-series data while presenting a normal PostgreSQL table. Ingest stays efficient as history grows, and queries skip irrelevant time ranges. Hypercore: Combines row storage with compressed columnar storage that remains directly queryable and writable. Teams can retain more detailed history at lower cost and run faster analytical queries. Continuous aggregates: Maintain incremental rollups, refreshing affected time windows as new, late, or corrected data arrives. Dashboards and enterprise KPIs stay fast without repeatedly scanning raw history. Hyperfunctions: Provide specialized SQL functions for irregular sensor readings, counters, and state changes. Teams can calculate time-weighted averages, track equipment states, and analyze operational data without building that logic themselves. Ignition Enterprise Historian puts these capabilities to work automatically. It creates the database schema and configures advanced partitioning, compression, and retention policies for scalable tag history, without requiring users to design and tune the database themselves. Unlike Ignition’s existing SQL Historian, the Enterprise Historian pushes filtering and aggregation into TimescaleDB, reducing the cost and latency of transferring raw history to the Ignition server for processing. Integrated visualization and analytics take advantage of TimescaleDB’s query capabilities and continuous aggregates without requiring users to build that database logic themselves. The performance gains are substantial. Benchmarks performed by Inductive Automation engineers comparing SQL Historian on PostgreSQL with Enterprise Historian on TimescaleDB show improvements of 10× or more across key performance metrics. Mission-critical reliability: High availability and disaster recovery A historian can become mission-critical well before it reaches a million tags. When applications and operational decisions depend on it, teams need confidence that the database will remain available through failures and that they can recover their data when something goes wrong. In conversations with system integrators and OT engineers, we’ve seen how easily database reliability can become a hidden risk. Writing to two separate databases can look like redundancy, and taking disk snapshots can look like a database backup strategy. But without the right coordination, consistency guarantees, and recovery procedures, these approaches can leave gaps: divergent databases, unusable backups, or silent failures that only become visible when recovery is needed. Teams may believe their data is protected until an incident reveals otherwise. TimescaleDB Enterprise brings coordinated high availability, backup, and recovery into the platform. High-availability clusters use database-level replication to keep replicas consistent, with automated failover and consensus-based leader election when a node fails. This avoids the consistency gaps of application-level dual writes, where a write can succeed in one database and fail in the other. Database backups and continuous write-ahead-log (WAL) archiving support point-in-time recovery, including to a point before an accidental deletion or bad application write. Integrated monitoring and the console expose cluster health, topology, and backup status, giving teams visibility into how those protections are operating. We’ve spent years operating highly available databases in Tiger Cloud at fleet scale. TimescaleDB Enterprise brings that experience to your infrastructure: It takes responsibility for installing, configuring, tuning, monitoring, and upgrading the full software stack. That includes PostgreSQL, TimescaleDB, and additional database extensions for advanced time-series analytics, graph analytics, geospatial, and hybrid search. It also manages HAProxy and pgBouncer for database connectivity, Patroni and etcd for HA orchestration and coordination, and pgBackRest for backup and recovery. LiveSync, secure networking, and key exchange support data replication across deployments. Your team gets one integrated database service, with the components managed together throughout their lifecycle. Controls engineers and system integrators should be able to focus on industrial applications without also becoming experts in mission-critical database operations. Tiger Data supports the database platform alongside Inductive Automation’s support for Ignition, reducing the custom database engineering that customers and integrators need to own and maintain. Whole-system operation: Manage deployments and connect data across sites Industrial systems rarely stop at one database. A data hall may need its own local history, a campus needs a broader view, and a fleet needs selected data from every site. TimescaleDB Enterprise lets one control plane manage these independent deployments, while LiveSync provides replication between database deployments and CloudSync connects deployments to Tiger Cloud. Both capabilities deploy and are managed alongside the databases. These capabilities replicate selected database data, including raw history, summaries maintained by continuous aggregates, and relational asset context. That complements Ignition’s existing MQTT and Gateway Area Network tag-sharing workflows, letting teams move data that isn’t naturally represented as tags. Together, these capabilities support three common patterns: Up the hierarchy: Aggregate local data for broader visibility. A data center can consolidate alarms and history from individual data halls into a campus database. A solar fleet can retain detailed history at each site while replicating selected data and minute-level aggregates to a central database for enterprise KPIs. Continuous aggregates summarize data locally, so central applications don’t need every raw sample. Down the hierarchy: Distribute shared context to local applications. A central enterprise database can distribute asset identifiers, equipment hierarchies, and other unified-namespace context to site databases. Local applications can join that shared information with their telemetry, using a consistent set of asset identifiers and definitions while each site operates independently. Across the OT/IT boundary: Give analytics and AI their own copy. Replicate selected operational data into a separate IT database, where applications and agents can explore it without direct access to production OT databases or adding analytical query load to them. Try TimescaleDB Enterprise for free today Together, Inductive Automation and Tiger Data are rethinking the modern historian: an open data foundation for industrial applications, with the performance to retain and analyze detailed history and the operational capabilities to run across critical sites and fleets. With Ignition 2027 and TimescaleDB Enterprise now in public beta, you can start building on that foundation today. TimescaleDB Enterprise extends beyond Ignition and industrial automation. It supports software for the physical world, including energy, oilfield services, logistics, and robotics, alongside financial and crypto analytics, SaaS applications, and developer tools. For teams whose compliance, data residency, or infrastructure requirements call for running databases in specific regions, private environments, or their own facilities, it brings integrated database operations to the infrastructure they choose. Try demo mode on your local machine, with database clusters deployed in local containers. Or start an extended free trial to develop and test across servers in your own environment. We’d love to hear what you’re building, how it performs, and what you need as we work toward general availability. Adaptive Integer Compression in TimescaleDB: Built for the Read Path DeltaDelta suits time-series-shaped integers. Real tables also hold status codes, foreign keys and interleaved sources, often in small batches. Every query that reads that data has to decompress it first, so AIC picks the encoding per block, and only from encodings that decompress fast. How Meshtastic Metrics Exporter Turned Eight Prometheus Queries Into One with Tiger Data Meshtastic Metrics Exporter reduced 500K+ Prometheus metric series to one Postgres database: consolidated eight queries into one with Tiger Data for 10,000 mesh radio nodes.