{"id":30246,"date":"2025-12-24T19:27:30","date_gmt":"2025-12-25T03:27:30","guid":{"rendered":"https:\/\/tdengine.com\/?page_id=30246"},"modified":"2026-01-29T07:21:00","modified_gmt":"2026-01-29T15:21:00","slug":"pi-connector","status":"publish","type":"page","link":"https:\/\/tdengine.com\/pi-connector\/","title":{"rendered":"TDengine PI System Connector"},"content":{"rendered":"\n
\n
\n
\n

TDengine PI System Connector<\/h1>\n\n\n\n

Modernize one step at a time with TDengine’s PI System connector. Stream real-time data or backfill historical data into TDengine TSDB without impacting your existing PI System deployments: start with edge sites or critical assets, keep current workflows running, and expand at your own pace as performance, scale, and cost benefits become clear.<\/p>\n\n\n\n

\nRequest a Demo<\/a>\n\n\n\nVideo Demonstration<\/a>\n\n\n\nDocumentation<\/a>\n<\/div>\n<\/div>\n\n\n\n\"\"<\/a>\n<\/div>\n<\/div>\n\n\n\n
\n
\n

How It Works<\/h2>\n\n\n\n

TDengine’s data connector for PI System uses the AF SDK to query historical data from PI Data Archive, set up PI and AF data pipes for streaming data, and connect to PI AF to query the AF structure. It also creates the required tables and writes this data over a secure REST API to TDengine TSDB.<\/p>\n\n\n\n

\n\n<\/figure>\n\n\n\n

To use the PI System data connector, you create data ingestion tasks in TDengine TSDB — real-time tasks for streaming data and backfill tasks for historical data. You can ingest data from PI Data Archive, mapping individual PI Points to tables in TDengine TSDB, and from AF Server, mapping AF elements to tables in a multi-column model.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n
\n

What the Connector Enables<\/h2>\n\n\n\n
\n
\n\"\"\n\n\n\n
\n

Real-Time Data Streaming<\/h3>\n\n\n\n

Continuously ingest live time-series data from your PI Data Archive or AF Server, establishing real-time pipelines so new operational data is synchronized from PI System to TDengine Historian.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n\"\"\n\n\n\n
\n

Historical Data Backfill<\/h3>\n\n\n\n

Extract historical data from PI System with backfill tasks that let you populate TDengine TSDB with past records — either during initial setup or after connection interruptions — ensuring your dataset in TDengine is complete and no data is left behind.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n\"\"\n\n\n\n
\n

Flexible Data Modeling<\/h3>\n\n\n\n

Transform data from PI Points or AF elements into TDengine TSDB tables using single-column (Data Archive) or multi-column (AF) models. This lets you mirror PI structures in TDengine tables in a way that fits your analytics needs, with tags and metrics aligned to your asset model.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n\"\"\n\n\n\n
\n

Advanced Options<\/h3>\n\n\n\n

Fine-tune connector behavior with options like batch sizing, log levels, automatic backfill on reconnection, and controls for syncing new elements or attribute changes. These settings help optimize performance and operational control.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

\n
\n

Try Free Today<\/h2>\n\n\n\n

Download TDengine TSDB-Enterprise or deploy in TDngine Cloud and automatically receive a 15-day free trial during which you can validate the PI System connector in your environment.<\/p>\n\n\n\n

\nDownload<\/a>\n\n\n\nCloud<\/a>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

TDengine PI System Connector Modernize one step at a time with TDengine’s PI System connector. Stream real-time data or backfill historical data into TDengine TSDB without impacting your existing PI System deployments: start with edge sites or critical assets, keep current workflows running, and expand at your own pace as performance, scale, and cost benefits … Read more<\/a><\/p>\n","protected":false},"author":102,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"content-type":"","footnotes":""},"categories":[270],"tags":[],"ppma_author":[181],"class_list":{"0":"post-30246","1":"page","2":"type-page","3":"status-publish","5":"category-fwp"},"_links":{"self":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/pages\/30246","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/users\/102"}],"replies":[{"embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/comments?post=30246"}],"version-history":[{"count":6,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/pages\/30246\/revisions"}],"predecessor-version":[{"id":30649,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/pages\/30246\/revisions\/30649"}],"wp:attachment":[{"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/media?parent=30246"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/categories?post=30246"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/tags?post=30246"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/tdengine.com\/wp-json\/wp\/v2\/ppma_author?post=30246"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}