Community Alerts

FME Hub user uvmsci just uploaded a new transformer to the FME Hub.Reads tables from CityGRID database (Oracle, MSSQL or PostgreSQL).Would you like to know more? Click here to find out more details!

FME Hub user uvmsci just uploaded a new transformer to the FME Hub.This custom transformer writes models into CityGRID database. Optionally terrains or images can be linked to the model(s).Would you like to know more? Click here to find out more details!

FME Hub user uvmsci just uploaded a new transformer to the FME Hub.Reads tables from CityGRID database (Oracle, MSSQL or PostgreSQL).Would you like to know more? Click here to find out more details!

Blogger Thiago Lúcio Brigagão just posted a new article to the FME Blog.Title: ChatGPT + FME: Transforming text into spatial dataSummary: Master the five essential principles to elevate your FME workflows from functional to exceptional.Introduction: Would you like to know more? Click here to read this new article!

FME Hub user danielmartins18 just uploaded a new transformer to the FME Hub.This transformer adds attachments to an ArcGIS Online feature service. The transformer does not store username and password, allowing to use a AGOL Web Connection existing on your workspace.Based on by "AGOLAttachmentAdder" created by Chris Duncan_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: AI Data Integration: Unlocking insights with FME & Google GeminiSummary: Discover how FME and Google Gemini revolutionize data pipelines with automation, AI-powered schema mapping, and large-scale data transformation.Introduction: What if you could process 1.09 million handwritten records in just 26 hours instead of 19 years? AI is revolutionizing data pipelines, making them faster, smarter, and more efficient than ever before—and enabling previously impossible projects like this one.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: AI Data Integration: Unlocking Insights with FME & Google GeminiSummary: Discover how FME and Google Gemini revolutionize data pipelines with automation, AI-powered schema mapping, and large-scale data transformation.Introduction: What if you could process 1.09 million handwritten records in just 26 hours instead of 19 years? AI is revolutionizing data pipelines, making them faster, smarter, and more efficient than ever before—and enabling previously impossible projects like this one.Would you like to know more? Click here to read this new article!

Blogger Sam Harper just posted a new article to the FME Blog.Title: 5 Presentations to Watch at Peak of Data & AI 2025Summary: With Peak of Data & AI 2025 just around the corner, Safe Software is proud to showcase 5 must-see presentations at the Seattle conference in May!Introduction: The 2025 Peak of Data & AI conference will see the largest gathering of FME users from around the world, with a target of over 1000 attendees! From May 6-8th, industry leaders and professionals will gather in Seattle, Washington to participate in an action-packed week of expert-led workshops, inspiring keynotes and hands-on sessions, all while building stronger connections with fellow members of the FME community through networking and social events.Would you like to know more?

FME Hub user rvoisin just uploaded a new transformer to the FME Hub.Citywide GET ConnectorDescriptionThis transformer handles pagination for the Citywide REST API.UsageIf given a valid Citywide REST API GET endpoint, this transformer will loop through all available pages of results and return all results combined into a single JSON response as cw_response. Please note that querying for large data sets my result in slower response times. This transformer is not designed for use with Bulk Routes.ParametersWeb Connection - A valid Citywide web authentication connection.Location - The Citywide GET endpoint to be queried.Filter Field - Optional. A field name to be used for filtering.Filter Value - Optional. A field value to be used for filtering.Would you like to know more?

FME Hub user rvoisin just uploaded a new transformer to the FME Hub.Citywide Service Request ConnectorDescriptionThis transformer handles the creation of Service Requests with the Citywide REST API. It can be used to access various POST actions, including create new Service Requests and attaching Notes, Files, and Attributes.UsageTo use this transformer, a JSON POST body must be assembled to be sent to the relevant endpoint. It preforms one function at a time, which can be selected under Basic Parameters. Several uses of this transformer must be chained together to accomplish the entire Service Request creation process.Basic ParametersSelect a Function - Select which REST API action to preform. Create New Service Request: creates a new Citywide Service Request. Commit Service Request Transaction: this step must be preformed i

FME Hub user rvoisin just uploaded a new web_connection to the FME Hub.This Web Service connects to the Citywide REST API.To use this connection, you will need your username and database name as well as your user API Key.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: 7 ways utility companies use FME data Integration to deliver better servicesSummary: See real-world examples of how companies in the utilities sector are improving their operations and delivering better services using FME.Introduction: Utility companies face the challenge of managing large datasets of assets and customers while ensuring information is accessible to those who need it—across the organization, to field crews, the public, and more. Often, data resides in legacy systems, requiring significant manual effort to process and analyze. This is why automated data integration workflows can greatly improve operational efficiency and help deliver better services.Would you like to know more?

FME Hub user sanaeatsafe just uploaded a new transformer to the FME Hub.DescriptionThe OllamaConnector allows you to interact with the Ollama API, enabling response generation and seamless integration with locally deployed large language models (LLMs) within an FME Workspace.Examples of local LLM models that might be used with the OllamaConnector are Llama, LLaVa, Phi, Gemma, Mistral, Moondream, Deepseek, and Starling. For more information, review the Ollama API documentationWould you like to know more? Click here to find out more details!

GIS (StackExchange) user stratagem 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

GIS (StackExchange) user stratagem just made a new post with the tags: arcgis-pro, fme-form, relationship-class.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: 7 Ways Utility Companies Use FME Data Integration to Deliver Better ServicesSummary: See real-world examples of how companies in the utilities sector are improving their operations and delivering better services using FME.Introduction: Utility companies face the challenge of managing large datasets of assets and customers while ensuring information is accessible to those who need it—across the organization, to field crews, the public, and more. Often, data resides in legacy systems, requiring significant manual effort to process and analyze. This is why automated data integration workflows can greatly improve operational efficiency and help deliver better services.Would you like to know more?

Blogger Hamish Kingsbury just posted a new article to the FME Blog.Title: Five building blocks for FME successSummary: Master the five essential principles to elevate your FME workflows from functional to exceptional.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.This transformer fits a range of numbers into a different range (or scale), in a process called Normalization (specifically Unity Based Normalization), or Min-Max Feature Scaling.Input PortsInput: Incoming data. Values to be normalized must be numeric. Features with non-numeric values will be rejected and the results calculated from the remaining records.Output PortsOutput: The outgoing data with the result in a new attribute called _NormalizedValueRejected: Any features rejected by the process.Feature order is unaffected by any processes in this transformer.ParametersAttribute to Normalize: The attribute containing the values to be normalized.New Scale Minimum: The minimum value for the

FME Hub user antoine just uploaded a new template to the FME Hub.FME Workspace DescriptionUse caseThis FME workspace is designed for processing, analyzing, and preparing data to feed a local ollama model with your own pdf. It is just a demo workspace to understand RAG concept and Ollama calls without python.RequirementsInstall OllamaLoad your embedding model plus your LLM model into it first.Key ComponentsChunk Processing:Splits the input data into smaller chunks for parallel processing.Utilizes transformers to manage chunk size and distribute data.Embedding:Embeds validation steps to identify and correct errors in data chunks.Isolates problematic chunks for furt

FME Hub user chrisatsafe just uploaded a new template to the FME Hub.# Agentic Schema Mapping (AI Assistant Schema Mapping)## OverviewThis template highlights the development of an FME Workspace that can be configured as either a Workspace App or Automation tailored for schema mapping and address data standardization from various data providers. These solutions demonstrate how FME can seamlessly handle schema mapping and data preparation for ingestion into downstream processes, using Agentic AI (Gemini) to expedite the process without the need to build a look up table for schema mapping or manually define schema definitions. This works with any AI provider that supports structured outputs. ### Key Features1. **Schema Mapping Address Data**: - Accepts incoming files with varied schemas. - Converts the data to CSV for prompt

FME Hub user chrisatsafe just uploaded a new template to the FME Hub.Agentic Schema Mapping (AI Assistant Schema Mapping)OverviewThis template highlights the development of an FME Workspace that can be configured as either a Workspace App or Automation tailored for schema mapping and address data standardization from various data providers. These solutions demonstrate how FME can seamlessly handle schema mapping and data preparation for ingestion into downstream processes, using Agentic AI (Gemini) to expedite the process without the need to build a look up table for schema mapping or manually define schema definitions. This works with any AI provider that supports structured outputs. Key FeaturesSchema Mapping Address Data:Accepts incoming files with varied schemas.Converts the data to CSV for prompt engineering.Submits the data to Gemini f

Blogger Irene Munoz just posted a new article to the FME Blog.Title: FME around the world: Transforming data into impact in EuropeSummary: Discover how FME partners across Europe are solving real-world challenges, from expanding rural broadband to streamlining operations at major events.Introduction: With over 160 trusted partners across five continents, FME partners are catalysts for change, helping organizations solve complex challenges and realize their data’s full potential.Would you like to know more? Click here to read this new article!

GIS (StackExchange) user stratagem just made a new post with the tags: fme-form, fme-transformer.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 kasrajamshidi just uploaded a new fme_package to the FME Hub.Neo4j Reader using the official Python client.Would you like to know more? Click here to find out more details!

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