Big Data for Agri-Food: Principles and Tools

Demystify complex big data technologies Compared to traditional data processing, modern tools can be complex to grasp. Before we can use these tools effectively, we need to know how to handle big data sets. You will understand how and why certain principles – such as immutability and pure functions – enable parallel data processing (‘divide and conquer’), which is necessary to manage big data. During this course you will acquire this principal foundation from which to move forward. Namely, how to recognise and put into practice the scalable solution that’s right for your situation. The insights and tools of this course are regardless of programming language, but user-friendly examples are provided in Python, Hadoop HDFS and Apache Spark. Although these principles can also be applied to other sectors, we will use examples from the agri-food sector. Data collection and processing in an Agri-food context Agri-food deserves special focus when it comes to choosing robust data management technologies due to its inherent variability and uncertainty. Wageningen University & Research’s knowledge domain is healthy food and the living environment. That makes our data experts especially equipped to forge the bridge between the agri-food business on the one hand, and data science, artificial intelligence (AI) on the other. Combining data from the latest sensing technologies with machine learning/deep learning methodologies, allows us to unlock insights we didn’t have access to before. In the areas of smart farming and precision agriculture this allows us to: Better manage dairy cattle by combining animal-level data on behaviour, health and feed with milk production and composition from milking machines. Reduce the amount of fertilisers (nitrogen), pesticides (chemicals) and water used on crops by monitoring individual plants with a robot or drone. More accurately predict crop yields on a continental scale by combining current with historic data on soil, weather patterns and crop yields. In short, this course’s foundational knowledge and skills for big data prepare you for the next step: to find more effective and scalable solutions for smarter, innovative insights. For whom? You are a manager or researcher with a big data set on your hands, perhaps considering investing in big data tools. You’ve done some programming before, but your skills are a bit rusty. You want to learn how to effectively and efficiently manage very large datasets. This course will enable you to see and evaluate opportunities for the application of big data technologies within your domain. Enrol now. This course has been partially supported by the European Union Horizon 2020 Research and Innovation program (Grant #810 775, “ Dragon ” ). 3b:T66d, M

• Recognize big data characteristics (volume, velocity, variety, veracity)
• The difference between scaling up and scaling out
• Big data principles: immutability and pure functions
• Processing big data with map-reduce, using clusters
• Understand technologies: distributed file systems, Hadoop
• How dataframes and wrapper technology (Apache Spark) make life easier
• The big data workflow and pipeline
• How data is organized in datalakes, using lazy evaluation
• Develop insight how to apply this to your own case

• A university education and/or working knowledge of math and science and, of course, being a computer science enthusiast will help a lot!