Community Alerts

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:

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 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 WorksEach 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".Parameter

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.OverviewEach 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.ParametersDateTime 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 PortsIn

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 WorksEach 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".ParametersLog

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.StaticWebCrawlerThis 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 sou

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. ParametersNamespaceType: 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 NamespaceType: 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. NameType: Text (String) The input string used in combination

Blogger Tiana Warner just posted a new article to the FME Blog.Title: Case study: Automating ArcGIS content discovery with FMESummary: Learn how Ripple automates ArcGIS content discovery, reporting, and relationship mapping in our most popular session from the 2025 Peak of Data and AI.Introduction: Managing large ArcGIS environments can be overwhelming, especially when it comes to discovering, understanding, and maintaining content spread across users, groups, and servers. But what if you could automate all of that without deploying any additional infrastructure?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: Case Study: Automating ArcGIS Content Discovery with FMESummary: Learn how Ripple automates ArcGIS content discovery, reporting, and relationship mapping in our most popular session from the 2025 Peak of Data and AI.Introduction: Managing large ArcGIS environments can be overwhelming, especially when it comes to discovering, understanding, and maintaining content spread across users, groups, and servers. But what if you could automate all of that without deploying any additional infrastructure?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: Real-time insights with ArcGIS and FME at the Peak of Data & AI 2025Summary: Learn about two amazing case studies: automating real-time environmental monitoring and performing scalable data edits using FME and ArcGIS.Introduction: Integrating FME and ArcGIS opens new doors to operational efficiency and smarter decision-making. At the Peak of Data and AI 2025, we saw two innovative case studies: one that automates real-time environmental monitoring for construction permitting, and another that streamlines bulk editing of massive geospatial datasets.Would you like to know more? Click here to read this new article!

FME Hub user andreas_h just uploaded a new transformer to the FME Hub.Origo Config Layer ExtractorSummaryExtracts detailed layer information from an Origo-map configuration JSON file. This transformer is specifically designed to handle Origo's non-standard JSON format, which may include single-line (//) and multi-line (/* ... */) comments, before parsing the layer definitions.GitHubhttps://github.com/origo-mapDetailed DescriptionOrigo map configurations are stored in JSON files that define groups, layers, sources, styles, and more. This transformer simplifies the process of reading these configurations by targeting the layer definitions directly.A key feature of this transformer is its ability to parse JSON files containing comments. Standard JSON parsers will fail on files with // or /* ... */ comments, a common practice in Origo configurations. This transformer pre-processes the file to strip out comments, ensuring robust and error-free parsing.It iterates through all layers defined

FME Hub user virtualcitymatt just uploaded a new transformer to the FME Hub.SafeFilenameCreatorDescriptionThe SafeFilenameCreator transformer ensures that attribute values are converted into safe filenames by replacing all illegal characters with an underscore (_). It works by internally using a StringReplacer to sanitize the attribute values.ParametersAttributes to Replace (String, Required): The name of the attribute(s) whose values will be converted into a safe filename.BehaviorTakes the value of the specified attribute.Replaces all characters that are not allowed in filenames (such as \/:*?"<>|) with _.Outputs the sanitized string as the same attribute value or a new attribute (depending on configuration).Example UsageInput Attribute- Name: Filename- Value: My:Illegal/FileName?.txtOutput Attribute- Name: Filename- Value: My_Illegal_FileName_.txtNotesOnly affects the characters that are illegal in filenames.Can be combined with other transformers to build fully automated file

FME Hub user pierrekoelich just uploaded a new transformer to the FME Hub.IfcGlobalIDGeneratorThis custom transformer creates a field called ifc_global_id and generates a compliant IFC global ID for each feature that is fed in.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: Real-Time Insights with ArcGIS and FME at the Peak of Data & AI 2025Summary: Learn about two amazing case studies: automating real-time environmental monitoring and performing scalable data edits using FME and ArcGIS.Introduction: Integrating FME and ArcGIS opens new doors to operational efficiency and smarter decision-making. At the Peak of Data and AI 2025, we saw two innovative case studies: one that automates real-time environmental monitoring for construction permitting, and another that streamlines bulk editing of massive geospatial datasets.Would you like to know more? Click here to read this new article!

FME Hub user daveatsafe just uploaded a new transformer to the FME Hub.Encodes point data into the DGGS JSON schema.A DGGS JSON dataset consists of a top zone whose boundary encompasses all the point data and a full set of subzones one or more zoom levels down from the top zone. Each subzone conatins a single set of values for that location. This is conceptually similar to raster data, where the top zone is the raster extents, and the subzones are the pixels. The transformer will group the source points by subzone and calculate the user's choice of mean, median, minimum or maximum to assign to the value of the subzone.How does it work?The DGGSJSONEncoder uses the open source dggal library to generate the top zone and subzone cells. To install the library, please follow the instructions on Installing Python Packages to FME Form to run the following install:fme python -m pip install dggalThis library provides an interface to the following Discrete Global Grids:GNOSIS Global GridIcosahedr