So far in this book, we have focused on batch data processing. That is, all our analysis, feature extraction, and model training has been applied to a fixed set of data that does not change. This fits neatly into Spark's core abstraction of RDDs, which are immutable distributed datasets. Once created, the data underlying the RDD does not change, although we might create new RDDs from the original RDD through Spark's transformation and action operators.
Our attention has also been on batch machine learning models where we train a model on a fixed batch of training data that is usually represented as an RDD of feature vectors (and labels, in the case of supervised learning models).
In this chapter, we will:
- Introduce the concept of online learning, where models are trained and updated on new data as it becomes available
- Explore stream processing using Spark...
