Last year we released FME Server, which takes spatial data interoperability to the next level by enabling organizations to transform huge quantities of data. Whether it is to move large amounts of data between or into a database or to make “tile” data available to support web applications, FME Server is the solution of choice for more and more organizations.

Then in the summer, we made an announcement with WeoGeo about enabling FME Server to be deployed in the cloud. At that time we envisioned a services-based world filled with small spatial ETL tasks that users could market and sell using cloud technology such as Amazon Web Services. While there is interest in this, we are seeing even more interest in a platform for “massive” spatial ETL processing that would be prohibitively expensive in a non-cloud environment due to the massive infrastructure deployment that would be required. Indeed, at the current time using the “cloud” as a spatial ETL powerhouse is where a lot of the excitement comes from.

Also arriving on the scene is an explosion of internet mapping applications, many of which use “tiles” to stream data back as efficiently as possible to huge numbers of users. Generating these “huge” numbers of tiles requires a huge amount of spatial etl processing that is a perfect fit for FME Server and the processing environment of the cloud. Using the cloud and FME Server technology, organizations can now generate the millions of tiles from vector and raster data.


The tasks for FME Server are the same as always. “Getting data to applications in a form that the applications can immediately use the data”! The beauty of the cloud is that users can for the first time entertain the thought of deploying thousands of machines while only having to pay for resources they use. At the risk of showing my age this gives me deja vu as this sounds a lot like the old mainframe model of old when many organizations would simply rent computer time and pay for what they used.

Initially we saw the cloud as merely an alternative deployment approach for FME Server. While that is very exciting it is even more exciting to see that FME Server coupled with the cloud enables a whole new set of spatial data interoperability problems to be solved. Watch for upcoming announcements as the cloud and FME Server deliver “Spatial ETL in the large!”

About Data Cloud Computing FME Server Spatial Data Interoperability

Don Murray

Don is the co-founder and President of Safe Software. Safe Software was founded originally doing work for the BC Government on a project sharing spatial data with the forestry industry. During that project Don and other co-founder, Dale Lutz, realized the need for a data integration platform like FME. When Don’s not raving about how much he loves XML, you can find Don working with the team at Safe to take the FME product to the next level. You will also find him on the road talking with customers and partners to learn more about what new FME features they’d like to see.


2 Responses to “FME Shines Thru the Clouds”

  1. Don:

    Very compelling indeed. With Amazon’s new Elastic MapReduce capabilities, you could conceivably throw serious firepower at the tile creation challenge.

    Good stuff.


  2. Don says:


    Exciting for sure. The Architecture of our Raster system in particular is one that enables us to easily break-up large raster data so that it can be worked on by many different processors. While initially we saw the cloud as “merely” a new deployment model what we are seeing now is an interest in unleashing the cloud as a method of implementing a powerful Virtual Earth (and other) tile creation system. As you know cloud technology is evolving very rapidly and we are still working to wrap our heads around things like Hadoop and MapReduce to be honest. Look for more information around FME and cloud technology as we get closer to the FME User Conference in June. We are working hard with Weogeo to have some pretty exciting demonstrations and announcements at that time. The cloud is definitely going to be more than just another deployment model.


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