Chapter 1: What Are Data Streams?
A data stream is a system where data continuously flows from multiple sources, just like water flows through a stream. The data is often produced and collected simultaneously in a continuous flow of many small files or records. Data streams are utilized by a wide range of business, medical, government, social media, and mobile applications. These applications include financial applications for the stock market and e-commerce ordering systems that collect orders and cover fulfillment of delivery.
In the entertainment space, live data is produced by sensing devices embedded in player equipment, video game players generate large amounts of data at a massive scale, and there are new social media posts thousands of times per second. Governments also leverage streaming data and geospatial services to monitor land, wildlife, and other activities.
Data volume and velocity are increasing at faster rates, creating new challenges in data processing and analytics. This book will detail these challenges and demonstrate how Amazon Kinesis can be used to address them. We will begin by discussing key concepts related to messaging in a technology-agnostic form to provide a solid foundation for building your Kinesis knowledge.
Incorporating data streams into your application architecture will allow you to deliver high-performance solutions that are secure, scalable, and fast. In this chapter, we will cover core streaming concepts so that you will have a detailed understanding of their application to distributed systems. You will learn what a data stream is, how to leverage data streams to scale, and examine a number of high-level use cases.
This chapter covers the following topics:
- Introducing data streams
- Challenges associated with distributed systems
- Overview of messaging concepts
- Examples of data streaming
