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FME Hub user antoine just uploaded a new template to the FME Hub. ExprAgent_Ollama Generate & Run FME Expressions with an LLM Type: 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 does Scans 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 text Evaluates the expression for each feature and stores the value in _result. Why it’s useful Turn a request like: add 5 to _creation_instance and multiply by 3 check if the id of my instance is even into a valid expression: (@Value(_creation_instance) + 5) * 3 …then apply it across your data—no manual expression authoring needed. Inputs & outputs Input: Any feature type with attributes referenced by your tas

Blogger Luca Raso & Gianmarco Corvino just posted a new article to the FME Blog. Title: From the Field to the Cloud with FME and AI Summary: 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 FME Summary: 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 Reader This transformer lets you read data from a Coda table into FME. 👉 Generate your Coda API key here How It Works Doc 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. Output Reads 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 Cases Pull 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 Writer This transformer lets you connect FME to Coda and update table rows using Coda’s Upsert API. 👉 Generate your Coda API key here How It Works Key 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. Performance Features 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 FME Summary: 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 Documentation The 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. Purpose This 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 Case Imagine 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 Attrib

Blogger Tiana Warner just posted a new article to the FME Blog. Title: How to Turn Unstructured Data into Decisions with FME Summary: 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 experts Summary: 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 meetup Summary: 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 Meetup Summary: 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. WARNING This 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 serv

Blogger Tiana Warner just posted a new article to the FME Blog. Title: AI and FME use cases: Generating metadata and automated workflow QA Summary: 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. INPUT Le 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,

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 Endpoint Would 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. Functionality The 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 Types After 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:

Blogger Tiana Warner just posted a new article to the FME Blog. Title: AI and FME Use Cases: Generating Metadata and Automated Workflow QA Summary: 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 alexbiz just uploaded a new transformer to the FME Hub. The LogNormalizer custom transformer is designed to read and normalize log files. It attempts to extract and standardize key information such as the timestamp and severity level, producing structured attributes that can be used for further processing in FME. Originally developed to handle FME Flow log files, it can also be applied to log files from other applications. How It Works Each input feature triggers the parsing of a log file. Typically, it is combined with the Directory and File Pathnames reader to list all log files within a directory and its subdirectories. The transformer uses different regular expressions to detect: The severity level of each log line The timestamp format When a timestamp is found, it is converted and normalized into both FME and ISO 8601 formats. If no timestamp is detected for a line, the transformer attempts to reuse the timestamp of the previous line and marks it as "unknown". Paramet

FME Hub user alexbiz just uploaded a new transformer to the FME Hub. The DateRangeFilter custom transformer filters features based on a timestamp attribute and a user-defined date range. It is designed to work with attributes containing timestamps in the FME Date/Time format. Overview Each input feature is evaluated against the specified date range. Features with timestamps inside the range are routed to the Inside output port, while those outside are routed to the Outside port. This transformer is useful for workflows where only a subset of features within a certain time window should be processed. Parameters DateTime Attribute (FME Format) Select the attribute that contains the timestamp to evaluate. The timestamp must be in FME Date/Time format. From The start date of the filtering range. Features with timestamps earlier than this date will be routed to Outside. To The end date of the filtering range. Features with timestamps later than this date will be routed to Outside. Output Po

FME Hub user alexbiz just uploaded a new transformer to the FME Hub. The LogParser custom transformer is designed to read and normalize log files. It attempts to extract and standardize key information such as the timestamp and severity level, producing structured attributes that can be used for further processing in FME. Originally developed to handle FME Flow log files, it can also be applied to log files from other applications. How It Works Each input feature triggers the parsing of a log file. Typically, it is combined with the Directory and File Pathnames reader to list all log files within a directory and its subdirectories. The transformer uses different regular expressions to detect: The severity level of each log line The timestamp format When a timestamp is found, it is converted and normalized into both FME and ISO 8601 formats. If no timestamp is detected for a line, the transformer attempts to reuse the timestamp of the previous line and marks it as "unknown". Parameters

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

FME Hub user andreas_h just uploaded a new transformer to the FME Hub. StaticWebCrawler This Custom Transformer is a web crawler designed to extract links from anchor tags and iframes within a specified domain. It respects common guidelines for web crawlers, ensuring ethical crawling by respecting robots.txt rules. The crawler can also discover and utilize sitemap XML files to prioritize crawling based on URL priority values. Key Features: Robots.txt Compliance: Automatically fetches and respects Disallow and Allow rules from a domain's robots.txt file. Sitemap Integration: Discovers sitemaps from robots.txt and uses them to prioritize URLs for crawling based on their <priority> value. Domain Scoping: Restricts crawling to a specific domain to prevent the crawler from wandering. Configurable Limits: Set a maximum number of pages to crawl and a delay between requests to be respectful to servers. Comprehensive Output: Provides detailed attributes for each discovered link, including its s

FME Hub user alexbiz just uploaded a new transformer to the FME Hub. The UUIDv5Generator is a custom transformer that generates Version 5 Universally Unique Identifiers (UUIDs) for incoming features. UUID v5 values are generated deterministically based on a given namespace and name, making them consistent for the same input parameters. This transformer is useful for creating reproducible identifiers across datasets, workflows, or systems. Parameters Namespace Type: Choice (Dropdown) Options: DNS → Predefined namespace for DNS names URL → Predefined namespace for URLs OID → Predefined namespace for Object Identifiers X500 → Predefined namespace for X.500 Distinguished Names Custom → Use a user-defined namespace Custom Namespace Type: Text (String) Enabled only if: Namespace = Custom Must contain a valid UUID (format: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx). Allows you to define your own namespace for UUID v5 generation. Name Type: Text (String) The input string used in combination with th