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In FME, workspaces define the parameters of your data translation; they confirm what data should be pulled in, how that data should be transformed and the final output. Not all workspaces are created equal and in this webinar we take you through some real-world examples of how to build efficient workspaces.
The power to bring data together rather than just moving or rearranging it is harnessed in FME through the merging and joining of data from multiple datasets regardless of type.
Managing the impact of dredging and disposal on the environment in the construction of the new wharf at Napier Port requires the project team to regularly engage with key stakeholders and maintain compliance with resource and regulatory requirements throughout. The wharf, of which construction is well underway, will offer extended vessel capacity and meet the..
customer-story
FME Takes the Guess Work Out of Asset Tracking
Knowing the whereabouts of major assets on a construction site at any given stage is a safety requirement of all large building projects. It is also a significant operational efficiency enhancer. Understanding the precise locations and ensuring the routine monitoring of hot works, dredging, piling and the overall composition of site works at 6 Wharf..
customer-story
Leveraging the Power of FME at Hutt City Council
At Hutt City Council, the development and production environments were one-in-the-same. Separating the development and production environments was much needed and driven also by growth in the FME user base which can present additional risk where there is no division in working environments.
customer-story
Powerco Delivers New Customer Data Automation
Due to the nature of its dual-energy distribution role and the geographical spread of urban and rural customers, the challenge of keeping customer data up-to-date is critical to Powerco operations.
The Digital RFS (Request for Service) app needed a single automated ETL solution that would remove data inconsistencies, manual processing and maintain the latest assets.
Inconsistent data capture and manual processing proved challenging for Surveyors in the field to provide As-Built that met the needs of Wellington Water’s data schemas.