Community Alerts

Blogger Tiana Warner just posted a new article to the FME Blog.Title: HOW TO CONNECT SNOWFLAKE AND ESRI USING FMESummary: Learn how to easily integrate Snowflake and Esri, including geospatial data type conversions, automatic synchronization, and leveraging SQL.Introduction: Key takeaways: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.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.

Blogger Dmitri Bagh just posted a new article to the FME Blog.Title: FIVE YEARS IN THE MAKING: FROM SIMPLE EXPERIMENT TO FME REALIZESummary: From an AR experiment to a city-scale breakthrough, FME Realize and Data Virtualization turn complex data workflows into seamless, real-world experiences.Introduction: It all started about 6-7 years ago, when I first began experimenting with augmented reality using the FME AR app. At the time, AR was mostly a curiosity, a passion project of our developers, but it didn’t take me long to realize that underground utilities were a perfect match for it — especially for our existing audience of cities, utility companies, and departments of transportation. Utilities is something that exists unseen, other than some on-the-surface manifestations of the networks, such as manholes, fire hydrants or catch basins.Would you like to know more? Click here to read this new article!

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:

Safe Software just uploaded a new FME version to their website. Version: 2025.1.3.1Build: 25645Release Date: 23-10-2025 (23 Oct 2025)Would you like to know more? Click here to visit the downloads page!

GIS (StackExchange) user user89877 just made a new post with the tags: 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 Robyn Dennehy just posted a new article to the FME Blog.Title: UNLOCKING DIGITAL TRANSFORMATION: ANALYSING MARKET SURVEY DATA WITH FME AND SISENSESummary: With FME and Sisense, a global sports media company transformed massive survey data into clear insights that drive smarter decisions.Introduction: Would you like to know more? Click here to read this new article!

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!

Blogger Tiana Warner just posted a new article to the FME Blog.Title: ARCGIS DATA INTEROP & THE FME PLATFORM: UNLOCKING MORE POSSIBILITIES FOR YOUR DATASummary: Learn how the Data Interoperability extension and FME are related, and the powerful functionality they have to offer.Introduction: Key takeaways: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: 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