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Redis Stack for Application Modernization

Redis Stack for Application Modernization

By : Luigi Fugaro, Ortensi
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Redis Stack for Application Modernization

Redis Stack for Application Modernization

5 (2)
By: Luigi Fugaro, Ortensi

Overview of this book

In modern applications, efficiency in both operational and analytical aspects is paramount, demanding predictable performance across varied workloads. This book introduces you to Redis Stack, an extension of Redis and guides you through its broad data modeling capabilities. With practical examples of real-time queries and searches, you’ll explore Redis Stack’s new approach to providing a rich data modeling experience all within the same database server. You’ll learn how to model and search your data in the JSON and hash data types and work with features such as vector similarity search, which adds semantic search capabilities to your applications to search for similar texts, images, or audio files. The book also shows you how to use the probabilistic Bloom filters to efficiently resolve recurrent big data problems. As you uncover the strengths of Redis Stack as a data platform, you’ll explore use cases for managing database events and leveraging introduce stream processing features. Finally, you’ll see how Redis Stack seamlessly integrates into microservices architectures, completing the picture. By the end of this book, you’ll be equipped with best practices for administering and managing the server, ensuring scalability, high availability, data integrity, stored functions, and more.
Table of Contents (18 chapters)
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1
Part 1: Introduction to Redis Stack
6
Part 2: Data Modeling
11
Part 3: From Development to Production

Vector embeddings for unstructured data modeling

Vector embeddings are lists of floating-point numbers that are used to describe the semantics of unstructured data. The principal feature of vector embeddings is that they have fixed sizes and allow a compact and dense representation of data in fewer bytes, compared to other encoding models. Features can be, in certain cases, engineered manually or using standard methods. An example of embedding can be the description of a color, expressed by the three RGB color components. So, using the RGB representation, we can express any color as an array of numbers:

[34, 93, 232]

While this approach will work perfectly with this and many other data modeling problems, nowadays, generating vector embeddings from unstructured data involves deep learning techniques. These aim to produce models that do the following:

  1. Take the raw unstructured data as input (a bitmap file or a voice recording).
  2. Capture the relevant and distinguishing...

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