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FME Hub user hansh just uploaded a new transformer to the FME Hub.Custom transformer til at hente skema for alle datasæt i det nye Datafordeler endpoint for fleksibel opslagslogik.Læs mere her: https://github.com/sweco-se/30002416-DAFQL/blob/main/UsageLet at bruge, du skal oprette adgang til Datafordelerens URL for fleksibel opslagslogik, før du kan bruge denne transformer, se Parametre nedenfor.KonfigurationParametreapi-keyFor at bruge denne custom transformer skal du have en API-nøgle for at få adgang til Datafordelarens nye API til fleksibel opslagslogik. Denne API-nøgle indtastes i den webforbindelse, der følger med denne custom transformer.URL to DAF Flexible schemaHvis URL'en til fleksibel opslagslogik ændres, skal du ændre den parameter, der indeholder denne URL. Lige nu er denne URL: https://graphql.datafordeler.dk/flexible/v1/schemaFilterDer er to parametre til filtrering af, hvilke datasæt og enheder du vil hente skemaet for"Dataset to get schema for"er et filter for, hvilket

FME Hub user hansh just uploaded a new transformer to the FME Hub.Custom transformer til at hente GEODKV_Bygning og dens to-niveau relationer til BBR_Bygning og videre til DAR_Husnummer inden for en afgrænset ramme, som du angiver, og lever resultatet som polygoner for bygningerne med attributterne i alle tre datasæt.Læs mere her: https://github.com/sweco-se/30002416-DAFQL/blob/main/Let at bruge, du skal oprette adgang til datafordeleren URL for fleksibel opslagslogik, før du kan bruge denne transformer, se Parametre nedenfor.KonfigurationParametreURL to Datafordeleren flexibleHvis URL'en til fleksibel opslagslogik ændres, skal du ændre den parameter, der indeholder denne URL. Lige nu er denne URL:https://graphql.datafordeler.dk/flexible/v1/API TokenFor at bruge denne custom transformer skal du have en API-nøgle for at få adgang til Datafordelarens nye API til fleksibel opslagslogik.Number of entities to fetchAntal resultater, der skal hentes for hver side i resultatet.Number of fetch

FME Hub user dmitribagh just uploaded a new transformer to the FME Hub.ICEYE (https://www.iceye.com) provides near real-time hazard information worldwide. This connector streamlines data access, letting you integrate flood depth rasters, flood extent polygons, and other layers directly into FME workflows.The ICEYEConnector transformer connects to the ICEYE API using secure credentials (OAuth 2.0 Client Credentials Flow) and retrieves peril data — currently Flood, Cyclone, and (coming soon) Wildfire.To use the ICEYEConnector, you must have an active subscription with ICEYE and valid API credentials. If you do not yet have access, please contact ICEYE to learn more about subscription options and to obtain credentials for API integration.The transformer can operate globally or within a specified area of interest (AOI)supplied as a polygon geometry and supports two primary actions:List existing perils for a given time range, returning bounding boxes and download links.Download selected per

FME Hub user abal just uploaded a new transformer to the FME Hub.DescriptionThis transformer allows you to access the COPERNICUS Data Space Ecosystem and download Sentinel-II images right from the source.Since the existing Sentinel-II reader targets an "Requester pays" Amazon bucket, AWS credentials are required in addition to a charge per x requests or GB.Using the con terra SentinelDownloader transformer, you only need a free account from COPERNICUS Data Space Ecosystem to access the Sentinel-II data. Upon creating an account, you have to register an OAuth client, as desribed in here.If you have any question or if you are looking for professional services support, please contact fme@conterra.de. ConfigurationPlease ensure that you saved the workspace you're using this custom transformer in to a file before using it. The workspace uses referential paths when accessing the downloaded images.Input PortInitiator: An Area Of Interest (AOI) polygon feature OR A point feature (e.g. defined

FME Hub user ewoudvdc just uploaded a new transformer to the FME Hub.Solar Angle CalculatorEwoud Van der Cruyssen - Kavel10 BVOverviewThis transformer calculates the Solar Elevation and Solar Azimuth for a given geographic position and timestamp.It implements the NOAA Solar Position Algorithm, providing highly accurate results (typically within ±0.0005° compared to NOAA’s reference calculator).DescriptionThe Solar Position Calculator determines the position of the sun relative to a point on Earth based on:- Latitude- Longitude- Timestamp (UTC or Local Time)and optionally a Time Zone offset.The transformer computes:- Solar Elevation (degrees): The angle of the sun above the horizon. A value of 0° means the sun is on the horizon, while 90° means it is directly overhead.- Solar Azimuth (degrees): The compass direction of the sun measured clockwise from North (0° = North, 90° = East, 180° = South, 270° = West).The computation follows the NOAA method, including:- Julian day and century ca

FME Hub user antoine just uploaded a new template to the FME Hub.FME-SQL-LLM Report GeneratorType: Template workspace (Form)Purpose: Turn a plain-language question about a table into a planned set of SQL queries, execute them in DuckDB, and produce a Markdown → HTML report using three lightweight LLM roles (Planner, SQL Generator, Analyst).What it doesStandardizes input: reads any tabular file (Excel/CSV/Parquet), normalizes headers, and writes a temporary Parquet dataset.Profiles data: extracts schema, summary stats, and sample rows with DuckDB (compact “metadata” for prompts).Plans the analysis (Planner LLM): converts the goal + metadata into formal sub-questions and SQL intents.Drafts SQL (SQL Generator LLM): returns executable SQL per sub-question (minimal JSON: just sql).Executes safely in DuckDB: runs each query with guardrails (e.g., COPY (SELECT ...), semicolon stripping, optional LIMIT cap).Analyzes results (Analyst LLM): synthesizes named result sets into a Markdown narrative

FME Hub user andreas_h just uploaded a new transformer to the FME Hub.URLResolverOverviewThe URLResolver is an Custom Transformer that resolves relative URLs against a base URL with input validation. It ensures base URLs are absolute and relative URLs are relative before combining them using urllib.parse.urljoin,Key FeaturesURL validation (base_url must be absolute, relative_url must be relative)RFC 3986 compliant URL resolution using urllib.parse.urljoinHandles all relative URL types (root-relative, parent-relative, document-relative, query-only, fragment-only)Automatic path navigation resolution (.. and . segments)Error reporting with detailed messagesInput AttributesThe transformer expects the following attributes on input features:AttributeRequiredValidationDescriptionExample_base_urlYesMust be absolute with schemeThe base URL (must include protocol)https://example.com/path/page.html_relative_urlYesMust be relative without schemeThe relative URL to resolve../other.html, /path/file.

FME Hub user takashi just uploaded a new format to the FME Hub.Reads DEM data file(s) (GML format) of the Japanese Fundamental Geospatial Data (FGD) as raster feature(s).This reader generates one raster feature per one source data file; the feature type name will always be set to "DEMRASTER".You can read any type DEM data (DEM1A, DEM5A, DEM5B, DEM10A, or DEM10B) with this reader, but DO NOT change source data file names since it determines the DEM type based on the naming rule for FGD DEM data files.This reader skips reading a source file when the schema location or the coordinate system defined in the source file is invalid, or the source file name is invalid as an FGD DEM data file. If you don't want to validate schema location, set "No" to the "Validate Schema Location" parameter. This reader considers these srsName as valid, and sets a corresponding coordinate system (EPSG code) to the output raster feature. Note: at 2025-10-25, since EPSG code for JGD2024 isn't defined, sets EPSG:

FME Hub user mark2atsafe just uploaded a new transformer to the FME Hub.Creates a datetime interval using values entered via published parameters.Would you like to know more? Click here to find out more details!

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

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

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!

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

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

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:

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

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

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