Modern geospatial applications are becoming more and more complex. Whether it be an application for self driving cars or prediction engine for energy forecasting the number of moving pieces are increasing. Applications are dealing with real-time data, often with errors and/or missing data. In addition to this skills specialization means teams working on these applications have to collaborate and communicate across different aspects of the software in a meaningful way. This can range from machine learning and data science workflows to delivering meaningful insights to end users.
In this talk we will describe design patterns of geo data pipelines in the age of big data & machine learning. We will provide tips on the open source options available to develop such architectures. Where possible we will identify gaps in the market.
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