Big Data in the Agri-food Domain

An intermediate-level course offered by WageningenX

Course Description

"Big Data in the Agri-food Domain" is an intermediate-level course offered by WageningenX that demystifies complex big data technologies and their application in the agricultural sector. This comprehensive course bridges the gap between agri-food business and data science, providing students with the fundamental knowledge and skills necessary to manage and process large datasets effectively.

What Students Will Learn

  • Recognizing big data characteristics (volume, velocity, variety, veracity)
  • Understanding the difference between scaling up and scaling out
  • Grasping big data principles: immutability and pure functions
  • Processing big data with map-reduce using clusters
  • Comprehending technologies like distributed file systems and Hadoop
  • Utilizing dataframes and wrapper technology (Apache Spark)
  • Understanding the big data workflow and pipeline
  • Organizing data in datalakes using lazy evaluation
  • Applying learned concepts to real-world scenarios

Pre-requisites

A university education and/or working knowledge of math and science is recommended. Some programming experience, although possibly rusty, is beneficial. Being a computer science enthusiast will greatly aid in understanding the course material.

Course Coverage

  • Big data definition and characteristics
  • Big data principles and their importance
  • Practical application of big data principles
  • Advanced big data technologies and their implementation
  • Big data workflow, pipeline, and datalake concepts
  • Real-world examples from the agri-food sector

Target Audience

This course is ideal for managers and researchers who are dealing with big data sets or considering investing in big data tools. It's particularly suited for professionals in the agri-food sector who want to leverage data science and artificial intelligence to improve their operations and decision-making processes.

Real-World Applications

  • Managing dairy cattle more effectively by combining various data sources
  • Reducing the use of fertilizers, pesticides, and water in crop management through precision agriculture
  • Predicting crop yields on a continental scale using historic and current data
  • Developing innovative solutions for smart farming and precision agriculture
  • Evaluating opportunities for big data technology application within their domain

Syllabus

Module 1: Big data definition and characteristics

  • Recognizing big data characteristics in agriculture
  • Identifying the main challenges in big data problems
  • Understanding the difference between scaling up and scaling out

Module 2: Big data principles: what are they and why do we need them

  • Learning about immutability and pure functions
  • Understanding the map-reduce concept

Module 3: Bring those principles to practice

  • Understanding clusters and distributed file systems
  • Learning about client-server architecture and Hadoop
  • Exploring the scalability of these systems

Module 4: Big data technologies that make implementation so much easier

  • Diving into the "big data stack of technologies"
  • Introduction to Apache Spark and its application of map-reduce

Module 5: The big data workflow and pipeline; the how and why of datalakes

  • Understanding datalakes and their differences from traditional databases
  • Exploring big data workflows and pipelines

Conclusion

This course equips learners with the foundational knowledge and skills necessary to tackle big data challenges in the agri-food sector, preparing them to find more effective and scalable solutions for smarter, innovative insights.