Community Alerts

Blogger Tiana Warner just posted a new article to the FME Blog.Title: 5 WAYS FME’S NEW ARCGIS CONNECTOR STREAMLINES DATA MANAGEMENT AND PUBLISHINGSummary: Introduction: Key takeaways:Would you like to know more? Click here to read this new article!

GIS (StackExchange) user Lay just made a new post with the tags: fme-form, autocad, dwg, autocad-xref-status, fme-reader.Would you like to know more? Click here to find out more details!StackExchange content licensed as: CC BY-SA 4.0

Safe Software just uploaded a new FME version to their website. Version: 2025.1.3Build: 25643Release Date: 07-10-2025 (7 Oct 2025)Would you like to know more? Click here to visit the downloads page!

FME Hub user antoine just uploaded a new template to the FME Hub.Translate Word Docs (EN→FI) with FME + Poro (Ollama)Category: AI / NLP • Type: Workspace Template (.fmwt)FME Version: 2024.2+Tags: translation, Word, DOCX, Finnish, LLM, Ollama, PoroOverviewThis template translates Microsoft Word (.docx) documents from English to Finnish using the FME Word Reader/Writer and a small Finnish LLM (Llama-Poro-2-8B-Instruct) served locally via Ollama.It keeps element order and layout by translating text-bearing features in place, then writing a new Finnish DOCX.Why this template?- No XML wrangling—works directly with the Word Reader/Writer.- Runs locally (good for sensitive docs).- Chunked context per block for more consistent translations.When to UseYou have DOCX files with standard paragraphs, lists, tables, captions.You want local inference and decent coherence without cloud services.You don’t need enterprise-scale throughput (see Notes & Limits).FeaturesWord Reader → filter text elemen

Blogger Tiana Warner just posted a new article to the FME Blog.Title: HOW TO DESIGN SCALABLE SYSTEMS WITH FMESummary: Actionable tips on how to future-proof your organization, including choosing an FME Flow architecture, schema design, and high-volume image processing.Introduction: Designing with scalability in mind is no longer optional. As organizations accumulate more data, expand into cloud and hybrid environments, and bring AI into production, the ability to architect future-ready systems is critical.Would you like to know more? Click here to read this new article!

GIS (StackExchange) user Jon just made a new post with the tags: qgis, postgis, fme-form.Would you like to know more? Click here to find out more details!StackExchange content licensed as: CC BY-SA 4.0

Blogger Irene Munoz just posted a new article to the FME Blog.Title: FME AROUND THE WORLD: REAL SUCCESS STORIES IN DATA AUTOMATION AND EFFICIENCYSummary: FME is making an impact around the world by helping governments, utilities, and organizations work smarter with their data. From Asia-Pacific to Europe and the Americas, teams are using FME to automate workflows, modernize infrastructure, and improve public serviceIntroduction: While Summer may be a slower season for some, we love to see organizations continue to move forward with FME. Across industries and regions, teams are using data to solve problems, improve how they work, and deliver better services to the people who rely on them.Would you like to know more? Click here to read this new article!

FME Hub user antoine just uploaded a new template to the FME Hub.ExprAgent_Ollama Generate & Run FME Expressions with an LLMType: Template workspace (Form)Purpose: Convert a plain-English task into an Arithmetic Editor expression using a local LLM (Ollama), then evaluate that expression on every feature and write the numeric result to _result.What it doesScans the input schema and builds a compact JSON description of attributes.Calls an Ollama tools-enabled model via /api/chat with a single tool (eval_expression) so the model returns only an expression.Cleans the returned textEvaluates the expression for each feature and stores the value in _result.Why it’s usefulTurn a request like:add 5 to _creation_instance and multiply by 3check if the id of my instance is eveninto a valid expression:(@Value(_creation_instance) + 5) * 3…then apply it across your data—no manual expression authoring needed.Inputs & outputsInput: Any feature type with attributes referenced by your task.Output

Blogger Luca Raso & Gianmarco Corvino just posted a new article to the FME Blog.Title: From the Field to the Cloud with FME and AISummary: Learn how GeneGIS GI uses Pangea Cloud, QField, and FME to build a digital twin of an asbestos mine, improving monitoring accuracy, streamlining remediation, and enhancing project management.Introduction: Would you like to know more? Click here to read this new article!

GIS (StackExchange) user Josh just made a new post with the tags: lidar, fme-form, iteration.Would you like to know more? Click here to find out more details!StackExchange content licensed as: CC BY-SA 4.0

Blogger Tiana Warner just posted a new article to the FME Blog.Title: How to Build Scalable APIs and Apps using Data Virtualization in FMESummary: Build secure, scalable APIs and real-time data services with no-code workflows, reusable templates, and advanced configuration.Introduction: Building secure, scalable APIs and apps doesn’t have to involve complex code. With the right software, you can deploy robust data services that power dashboards, internal tools, and customer-facing applications through configuration alone. In a recent webinar hosted by Safe Software and con terra, we explored how to use FME to scale real-time data services using templates, asynchronous execution, and advanced governance strategies.Would you like to know more? Click here to read this new article!

FME Hub user vhruska just uploaded a new transformer to the FME Hub.Coda Table ReaderThis transformer lets you read data from a Coda table into FME.👉 Generate your Coda API key hereHow It WorksDoc Id: The unique ID of your Coda document.Table Id: The unique ID of the table inside that document.API Token: Your personal Coda API key for authentication.OutputReads rows from the specified Coda table. Optionally outputs the column schema (table structure) if you enable it in settings.Because Coda tables can have dynamic structures, you will need to use an AttributeExposer transformer after this to make sure all fields are visible and usable in your workflow.Use CasesPull Coda data into FME workflows for analysis, transformation, or integration. Retrieve both data and schema for validation or mapping to other systems.Would you like to know more? Click here to find out more details!

FME Hub user vhruska just uploaded a new transformer to the FME Hub.Coda Table WriterThis transformer lets you connect FME to Coda and update table rows using Coda’s Upsert API.👉 Generate your Coda API key hereHow It WorksKey Columns: Choose one or more fields that uniquely identify a row in your Coda table (e.g., ProjectID).Value Columns: Specify which fields should be updated.Upsert Logic: If a row with matching key values exists → it is updated with new values. If no match is found → a new row is created in the table.PerformanceFeatures are processed in batch mode by default, reducing the number of API calls and improving performance for large datasets.Would you like to know more? Click here to find out more details!

Blogger Tiana Warner just posted a new article to the FME Blog.Title: How to turn unstructured data into decisions with FMESummary: Unstructured data accounts for 80% of the world's information. Here's how to use AI and FME to process unstructured text, images, documents, and more, and gain actionable insights in an automated, scalable way.Introduction: Unstructured data—meaning any information that doesn’t have a predefined model or format, such as text, images, media, documents, and much more—is everywhere. In fact, IDC estimates 80% of the world’s data is unstructured. Scanned forms, satellite imagery, field reports… It’s all incredibly valuable information, but it’s difficult to automate, organize, search, or glean insights from these datasets—until now.Would you like to know more? Click here to read this new article!

FME Hub user crystalwang just uploaded a new transformer to the FME Hub.AzureAIFoundryConnector Transformer DocumentationThe AzureAIFoundryConnector is a custom FME transformer that enables integration with Azure OpenAI's Responses API. This transformer allows you to send prompts to large language models deployed in Azure AI Foundry and receive responses directly within an FME workspace. This transformer also supports a variety of tasks: text, reasoning (only available for some models), image (Vision), and file search.PurposeThis transformer is designed for content generation, structured information extraction, summarization, reasoning, and other AI-driven tasks using Microsoft Azure’s hosted OpenAI services. It supports advanced reasoning models and file-based document processing via Azure AI's Files API.Example Use CaseImagine you have a dataset with a column of customer support inquiries, and you'd like to auto-generate a response or classify the sentiment. You can:Use AttributeCrea

Blogger Tiana Warner just posted a new article to the FME Blog.Title: How to Turn Unstructured Data into Decisions with FMESummary: Unstructured data accounts for 80% of the world's information. Here's how to use AI and FME to process unstructured text, images, documents, and more, and gain actionable insights in an automated, scalable way.Introduction: Unstructured data—meaning any information that doesn’t have a predefined model or format, such as text, images, media, documents, and much more—is everywhere. In fact, IDC estimates 80% of the world’s data is unstructured. Scanned forms, satellite imagery, field reports… It’s all incredibly valuable information, but it’s difficult to automate, organize, search, or glean insights from these datasets—until now.Would you like to know more? Click here to read this new article!

Blogger Tiana Warner just posted a new article to the FME Blog.Title: Top 5 tips for FME newbies: Advice from expertsSummary: Read the top 5 pieces of advice from FME experts around the world, voted on by the FME Community.Introduction: Every week in the FME Community, we pose a question to our users. Recently, we asked FME experts around the world: “What advice would you give a new FME user just starting out?” We received some fantastic answers that we want to share! This advice is actually great for users of all skill levels.Would you like to know more? Click here to read this new article!

Blogger Tiana Warner just posted a new article to the FME Blog.Title: Inside the first Airports & Aviation SIG virtual meetupSummary: Safe Software's Airports & Aviation Special Interest Group (SIG) brings together aviation professionals to share use cases, technical challenges, and ideas.Introduction: Safe Software’s Airports & Aviation Special Interest Group (SIG) held its first virtual meetup, and it was packed with innovation, inspiration, and real-world problem-solving. The goals of the SIG are to connect professionals in the aviation industry, foster collaboration, and share technical insights in a more informal setting than traditional conferences. Here’s a recap of the highlights from the event.Would you like to know more? Click here to read this new article!

Blogger Tiana Warner just posted a new article to the FME Blog.Title: Inside the first Airports & Aviation SIG Virtual MeetupSummary: Safe Software's Airports & Aviation Special Interest Group (SIG) brings together aviation professionals to share use cases, technical challenges, and ideas.Introduction: Safe Software’s Airports & Aviation Special Interest Group (SIG) held its first virtual meetup, and it was packed with innovation, inspiration, and real-world problem-solving. The goals of the SIG are to connect professionals in the aviation industry, foster collaboration, and share technical insights in a more informal setting than traditional conferences. Here’s a recap of the highlights from the event.Would you like to know more? Click here to read this new article!

FME Hub user siennaatsafe just uploaded a new transformer to the FME Hub.The MapillaryConnector custom transformer provides access to Mapillary's API to retrieve geospatially referenced imagery and data for use in FME. This transformer enables automation of street-level imagery analysis and extraction of computer vision features directly into your FME workflows.You can use this transformer to:- Retrieve images captured at or near specific geographic locations.- Access detected objects (e.g., traffic signs, poles, crosswalks) via the Object Detection API.- Extract Map Features for spatial analysis and visualization.WARNINGThis tranformer does not support pagination and may not return all results if the record count exceeds 2000. Would you like to know more? Click here to find out more details!

FME Hub user buckel just uploaded a new transformer to the FME Hub.Ce transformeur récupère les données relatives aux entreprises et établissements répertoriés dans le registre administratif français SIRENE, géré par l’INSEE, y compris les entités fermées.Le service est limité à 30 requêtes par minutes.Nécessite FME version 2024.1.0.0 (Build 24612) ou une version plus récente.L’utilisation de ce transformateur nécessite un compte sur le portail de l’INSEE, permettant de créer une application et de s’abonner à l’API SIRENE.Les services actuellement disponibles interrogent :Unités légales (siren)Etablissements (siret)This transformer retrieves data related to businesses and establishments listed in the French administrative SIRENE registry, managed by INSEE, including closed entities.Requires FME version 2024.1.0.0 ( Build 24612 ) or newer.Using this transformer requires an INSEE portal account, which allows you to create an application and subscribe to the SIRENE API.The service is limi

Blogger Tiana Warner just posted a new article to the FME Blog.Title: AI and FME use cases: Generating metadata and automated workflow QASummary: Watch these standout conference presentations to learn how users are leveraging AI and FME to generate synthetic metadata and perform FME Workspace quality checks.Introduction: With the help of AI, tedious and manual data processing tasks have become more efficient and scalable than ever. At this year’s Peak of Data & AI conference, we had two standout presentations about applying AI to automate key parts of data workflows. Watch the webinar Peak of Data & AI Encore: AI for Metadata and Smarter Workflows to see these sessions, and read on for an overview.Would you like to know more? Click here to read this new article!

FME Hub user buckel just uploaded a new transformer to the FME Hub.Ce transformeur utilise le service de géocodage inverse de l’IGN pour retourner l’entité ou les entités géolocalisées les plus proches, correspondant à une ou plusieurs des catégories suivantes : adresses, toponymes, parcelles cadastrales et unités administratives.Le service est limité à 50 requêtes par seconde.Nécessite FME version 2024.1.0.0 (Build 24612) ou une version plus récente.This transformer uses the IGN reverse geocoding service to return the nearest geolocated entity or entities matching one or more of the following categories: addresses, place names, cadastral parcels, and/or administrative units.It is limited to 50 requests per second.Requires FME version 2024.1.0.0 ( Build 24612 ) or newer.INPUTLe Transformer à pour but de retourner, à partir d’un ou plusieurs points géographiques indiqués en latitude/longitude, la ou les entités géolocalisées les plus proches correspondantes, parmi les adresses, toponyme

FME Hub user peter_s just uploaded a new web_connection to the FME Hub.This FME Web Service can be used for creating FME Named Connections for the DAF GraphQL EndpointWould you like to know more? Click here to find out more details!

FME Hub user alexbiz just uploaded a new transformer to the FME Hub.The DateTimeFilter custom transformer allows you to filter features based on a date/time attribute.It supports multiple filtering modes to test whether an entity’s date value falls before, after, within, or around specified ranges.FunctionalityThe transformer evaluates the value of a selected DateTime attribute (in FME Date/Time format) against user-defined conditions.Depending on the chosen filter type, features are routed to either:Passed: if the feature’s date meets the filter condition. Failed: if the feature’s date does not meet the filter condition. Filter TypesAfter Date: features with dates strictly after the specified date. Before Date: features with dates strictly before the specified date. Between Dates: features with dates between two specified dates (inclusive). From Date + Duration: features with dates occurring after a given start date, for a specified duration (in minutes). Until Date - Duration: