Probabilistic Data Structures with Redis

Coming Soon

This course is currenty in development, and we aim to launch it mid 2023. Register now to be one of our first students and to receive email updates as we approach the release date.

Can’t wait? Join us on Discord to talk about all things Redis in the meantime!

What you’ll learn

Modern applications frequently have to deal with massive or never ending data sets or streams. In this course you’ll learn about how probabilistic data structures can help make sense of huge amounts of data, trading absolute accuracy for processing and storage efficiency.

We’ll look at how deterministic data structures compare with their probabilistic equivalents and show how to use the various probabilistic data structures that Redis Stack provides.

This course covers:

• Hyperloglog - Counting unique items.
• Bloom and Cuckoo Filters - Set membership, has a given value been seen before?
• Count-Min Sketch - How many times has a given value been seen before?
• Top-K - Which are the k most frequently occurring values in a data set?
• t-Digest - Efficiently estimating distribution of values in a data set.

You can also expect to learn about features of the Redis String, Set, Sorted Set and Hash data types on this course. For a more thorough introduction to these data types, we recommend our RU101: Introduction to Redis Data Structures course.

You’ll work with Redis Insight and the Redis CLI with hands-on exercises for each data structure. We’ll provide code examples for Java, Node.js, Python and C# showing how to integrate these data structures into your application. You don’t need to be proficient in these programming languages to be successful with this course.

Your knowledge of the topics will be assessed using a series of multiple choice questions interspersed throughout the course. If you answer 65% or more of these correctly, you’ll earn a certificate of completion that you can share as a credential on your LinkedIn profile or elsewhere.