Summary

In this introductory chapter on streaming data and streaming analytics, you have first seen some definitions of what streaming data is, and how it is opposed to batch data processing. In streaming data, you need to work with a continuous stream of data, and more traditional (batch) data science solutions need to be adapted to make things work with this newer and more demanding method of data treatment.

You have seen a number of example use cases, and you should now understand that there can be much-added value for businesses and advanced technology use cases to have data science and analytics calculated on the fly rather than wait for a fixed moment. Real-time insights can be a game-changer, and autonomous machine learning solutions often need real-time decision capabilities.

You have seen an example in which a data stream was created and a simple real-time alerting system was developed. In the next chapter, you will get a much deeper introduction to a number of streaming solutions. In practice, data scientists and analysts will generally not be responsible for putting streaming data ingestion in place, but they will be constrained by the limits of those systems. It is, therefore, important to have a good understanding of streaming and real-time architecture: this will be the goal of the next chapter.