2 Graph data engineering

 

This chapter covers

  • The main challenges related to big data as input to machine learning
  • How to handle big data analysis with graph models and graph databases
  • The shape and features of a graph database

Chapter 1 highlighted the key role played by data in a machine learning project. As we saw, training the learning algorithm on a larger quantity of high-quality data increases the accuracy of the model more than fine tuning or replacing the algorithm itself. In an interview about big data [Coyle, 2016], Greg Linden, who invented the now widely used item-to-item collaborative filtering algorithm for Amazon, replied:

Big data is why Amazon’s recommendations work so well. Big data is what tunes search and helps us find what we need. Big data is what makes web and mobile intelligent.

2.1 Working with big data

2.1.1 Volume

2.1.2 Velocity

2.1.3 Variety

2.1.4 Veracity

2.2 Graphs in the big data platform

2.2.1 Graphs are valuable for big data

2.2.2 Graphs are valuable for master data management

2.3 Graph databases

2.3.1 Graph database management

2.3.2 Sharding

2.3.3 Replication

2.3.4 Native vs. non-native graph databases

2.3.5 Label property graphs

Summary

References