The Blog Spotlight is a dedicated space to highlight great blog posts showcasing FME.
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Welcome to this week's blog spotlight: 8 Organizations Using FME to Turn Real-Time Data Into Action. From flood sensors in New Zealand to wildfire feeds in British Columbia, this one's a tour through eight real deployments where FME moves data fast enough to actually matter.Why This MattersThe blog makes a simple point: when a river is rising or a fire is spreading, the bottleneck usually isn't a lack of data, it's getting that data into a usable shape, in the right coordinate system, validated, and in front of the person who has to decide before the moment passes. FME Flow ingests live data two ways: Streams for high-velocity feeds from brokers like Kafka and MQTT, and Automations for event-driven triggers like webhooks and polling. Every story in the post is really the same pattern underneath: ingest a live feed, clean it, and push it somewhere it can drive a decision.Inside the BlogThe post walks through eight organizations putting that pattern to work in very different settings:Haw
Welcome to this week's blog spotlight: How to Give AI Access to Sensitive Data Without Letting It Leave Your Network. If you've been wondering how to connect AI to your most sensitive systems without handing over the keys, this one is for you!Why This MattersThe blog frames MCP around a simple principle: expose tools, not systems. Rather than giving a model direct access to a database or file share, you give it a defined tool that takes specific parameters and returns structured results β the model never sees what's underneath. For organizations sitting on sensitive data in on-premises databases, CAD/BIM repositories, or geospatial systems, that distinction is what makes AI adoption possible without a compliance nightmare.Inside the BlogThe post covers the full spectrum of keeping data protected while still letting AI act on it. A few highlights:Any FME workspace can become an MCP tool the same way as before. Swap in an MCP writer, publish to FME Flow, register with clear parameters β
Welcome to this week's blog spotlight: Turning FME Workspaces into MCP Tools: Connecting ArcGIS to AI. If you've been wondering how your existing FME workspaces could plug into AI agents without a rebuild, this one lays out the whole path.Why This MattersThe blog reframes MCP as more than an AI feature: it's a general-purpose integration layer. Connect a data source or workflow once, and every MCP-capable client, whether that's a chat assistant, an app, or another system entirely, can use it. For FME users, that means years of accumulated workflows don't need to be rebuilt to become AI-ready; they just need to be exposed.Inside the BlogThe post walks through turning a municipal sidewalk-repair workflow into a live AI tool, then scales up to multi-tool, agent-driven workflows. A few highlights:Any FME workspace can become an MCP tool in three steps: swap the terminal step for an MCP writer, publish to FME Flow, and register it with clear parameter descriptions. The tool's description is
Welcome to this week's blog spotlight:Β 3 Pillars of Designing FME Flow Workflows That Scale.Β Β If you've ever watched a workspace balloon from a clean, focused tool into an unmanageable monster that takes forever to run and even longer to debug, this one is for you!Why This MattersWe've all been there: a workspace that started small and sensible, then grew one bolted-on feature at a time until it became a single massive job that blocks your queue, resists debugging, and terrifies anyone who has to open it. This blog puts a name to the problem and gives you a clear, actionable path out of it.Inside the BlogThe post breaks scalability down into three layers (workspace design, orchestration, and infrastructure) and walks through how a problem in any one of them can undermine the others. A few highlights:Large do-it-all workspaces don't just run slowly, they run sequentially. An FME engine is single-threaded, so ten readers sitting side by side on a canvas still execute one after another. B
Welcome to this week's blog spotlight: How to Validate Data at Scale: Geometry, Schema, Attributes, and AI. If you've ever had a workflow break because of bad incoming data, or worse, not break but silently pass bad data through, this one is essential reading!Why This MattersΒ The blog opens with a striking real-world example: a mislabeled utility line contributed to an oil spill in Burnaby, BC, in 2007. It's a powerful reminder that data quality isn't just a technical concern; it has real consequences. And as more organizations lean on automation and AI, the stakes only get higher.Inside the BlogThe post walks through a practical, layered approach to data validation that covers the three most common ways data goes wrong (geometry, schema, and attributes) and then goes a step further to address AI-generated outputs too. A few highlights:Geometry validation should run in order: check for corrupt or null geometries first, then test for compliance. Don't waste time running complex checks o
Welcome to this week's blog spotlight: 9 Data Integration Tips and Answers from FME Experts.Β If you've ever hit a wall with a tricky FME workflow and wished you could just ask someone who really knows their stuff,Β this one's for you!Why This MattersThis blog isΒ a recap of a recent Ask Me Anything webinar where FME's own technical support team tackled real questions from real users.Inside the BlogThe questions cover a really nice range of day-to-day FME challenges.Β From underrated features you might be sleeping on (the Add Attribute shortcut is a gem), to dynamic workflows, schema mapping, and knowing when to automate vs. keep things manual.A few highlights:The FME Flow REST API is more approachable than most people think SchemaMapper is your best friend for table-driven attribute mapping The "rule of three" heuristic for deciding when to automate is genuinely usefulKey Takeaways Whether you're newer to FME or deep in the weeds on Flow deployments, there's something in here for you. The
Welcome to this week's blog spotlight: FME Flow Hosted vs. On-Prem: 5 Use Cases That Make the Cloud Worth ItΒ π¦ This blog isΒ packed with practical insights for teams managing FME infrastructure, weighing cloud vs. on-prem, or looking to cut costs without sacrificing capability!Why This MattersChoosing between FME Flow Hosted and on-prem isn't just a technical decision β it's an operational one. This blog breaks down exactly where the cloud version shines, with real-world examples to back it up.Inside the SolutionFME Flow Hosted is the fully cloud-managed version of FME Flow, running on AWS infrastructure that Safe Software operates on your behalf. That means no OS patching, no vulnerability monitoring, no infrastructure headaches β just data work.The blog walks through 5 concrete use cases showing where FME Flow Hosted delivers: from spinning up a proof-of-concept server in under 10 minutes, to event-driven architectures that start and stop instances automatically, to one organization
Welcome to this week's blog spotlight! The Safe SoftwareΒ blog just dropped a new post, and if you're navigating the world of enterprise data, this one's for you.Why This MattersGetting data from point A to point B is only half the battle. This blog digs into why so many enterprises struggle with the harder problem - making sure the receiving system can actually understand and use the data once it arrives.Breaking it downThe post breaks down the four key challenges standing in the way of true enterprise data interoperability: data silos and legacy systems, schema mismatches, API complexity, and governance gaps.Each challenge is explored with real clarity -Β from decades of accumulated departmental silos that were never designed to connect, to the silent failures that happen when an API update quietly breaks a critical data flow. The blog makes a compelling case that reactive, one-off fixes don't scale, and that interoperability needs to be treated as a core organizational capability.Key
Welcome to this weeks blog spotlight!Β π¦Β There's a new post on the Safe Software blog, and itβs a must-read for anyone working in land administration, government workflows, or CAD-GIS integration!Why This MattersManual plan validation doesn't scale. This blog shows how The Land Title and Survey Authority of British Columbia (LTSA) used FME-powered automation to process nearly 80,000 survey plans with a small team and zero backlog.Inside the SolutionLTSA manages ParcelMap BC, British Columbia's authoritative cadastral fabric, and tackled the challenge of validating high volumes of survey plan submissions by building a fully automated pipeline on FME Form and FME Flow.Β By mandating standardized DWG file submissions alongside every plan, they created the foundation for automated topology checks, geometry validation, and seamless parcel fabric integration - all surfaced through a clean web portal for surveyors.Β Key TakeawaysThe blog post also shares practical takeaways on:Β Workspace docume
