July 2017
Beginner to intermediate
216 pages
7h 23m
English
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Chapter 5. Algorithms for data analysis
This chapter covers
- Querying a stream
- Thinking about time
- Understanding four powerful summarization techniques
Chapter 4 covered how the data flows through many stream-processing frameworks, the delivery semantics, and fault tolerance. In this chapter we’re going to depart from the architectural views and discuss the algorithmic side of stream processing, often called streaming analytics or stream mining. We will focus on the what and why of streaming analysis algorithms and occasionally dip our toes into the detailed how. Don’t worry if you’re looking for the detailed math or code behind the algorithms—ample resources will be provided so that you can continue your learning.
Before we begin, I’ll ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.