Overview

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”).

Syllabus

Module 1: Big data definition and characteristics
In module 1, you will learn how to recognize the characteristics of a big data problem in agriculture, to see where its biggest challenge lies. Should the solution focus on size, speed, various formats or uncertainty of data? Should you scale up or scale out?
Module 2: Big data principles: what are they and why do we need them
In module 2, you'll learn the principles that are required for scaling out: immutability and pure functions, and map-reduce. What are these and why do we need them?
Module 3: Bring those principles to practice
Module 3 shows you how to bring those principles into practice. You will learn what a cluster is, and how a distributed file system in a client-server architecture works, with Hadoop. You will understand why such a system is indeed scalable.
Module 4: Big data technologies that make implementation so much easier
Module 4 goes further into the application of big data technology, the “big data stack of technologies". The main message here is that if you know what you want to do, these technologies can take the work out of your hands. For example, you will see Apache Spark, a big data technology platform, that applies map-reduce for you.
Module 5: The big data workflow and pipeline; the how and why of datalakes
Module 5 dives deeper into the data. You'll learn about datalakes and why a datalake is different from a traditional database. You'll understand what a big data workflow looks like and what a pipeline is.