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1. GETTING STARTED

1.1 Introduction

What is event streaming?

Event streaming is the digital equivalent of the human body's central nervous system. It is the

technological foundation for the 'always-on' world where businesses are increasingly software-de¦ned

and automated, and where the user of software is more software.

Technically speaking, event streaming is the practice of capturing data in real-time from event sources

like databases, sensors, mobile devices, cloud services, and software applications in the form of streams

of events; storing these event streams durably for later retrieval; manipulating, processing, and reacting to

the event streams in real-time as well as retrospectively; and routing the event streams to different

destination technologies as needed. Event streaming thus ensures a continuous §ow and interpretation of

data so that the right information is at the right place, at the right time.

What can I use event streaming for?

Event streaming is applied to a wide variety of use cases across a plethora of industries and

organizations. Its many examples include:

To process payments and ¦nancial transactions in real-time, such as in stock exchanges, banks, and

insurances.

To track and monitor cars, trucks, §eets, and shipments in real-time, such as in logistics and the

automotive industry. To continuously capture and analyze sensor data from IoT devices or other equipment, such as in

factories and wind parks.

To collect and immediately react to customer interactions and orders, such as in retail, the hotel and

travel industry, and mobile applications.

To monitor patients in hospital care and predict changes in condition to ensure timely treatment in

emergencies.

2020/11/17 Apache Kafka

kafka.apache.org/documentation/ 5/397

To connect, store, and make available data produced by different divisions of a company. To serve as the foundation for data platforms, event-driven architectures, and microservices.

Apache Kafka® is an event streaming platform. What does that mean?

Kafka combines three key capabilities so you can implement your use cases for event streaming end-toend with a single battle-tested solution:

1. To publish (write) and subscribe to (read) streams of events, including continuous import/export of

your data from other systems.

2. To store streams of events durably and reliably for as long as you want.

3. To process streams of events as they occur or retrospectively.

And all this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure

manner. Kafka can be deployed on bare-metal hardware, virtual machines, and containers, and onpremises as well as in the cloud. You can choose between self-managing your Kafka environments and

using fully managed services offered by a variety of vendors.

How does Kafka work in a nutshell?

Kafka is a distributed system consisting of servers and clients that communicate via a high-performance

TCP network protocol. It can be deployed on bare-metal hardware, virtual machines, and containers in onpremise as well as cloud environments.

Servers: Kafka is run as a cluster of one or more servers that can span multiple datacenters or cloud

regions. Some of these servers form the storage layer, called the brokers. Other servers run Kafka

Connect to continuously import and export data as event streams to integrate Kafka with your existing

systems such as relational databases as well as other Kafka clusters. To let you implement missioncritical use cases, a Kafka cluster is highly scalable and fault-tolerant: if any of its servers fails, the other

servers will take over their work to ensure continuous operations without any data loss.

Clients: They allow you to write distributed applications and microservices that read, write, and process

streams of events in parallel, at scale, and in a fault-tolerant manner even in the case of network problems

or machine failures. Kafka ships with some such clients included, which are augmented by dozens of

clients provided by the Kafka community: clients are available for Java and Scala including the higherlevel Kafka Streams library, for Go, Python, C/C++, and many other programming languages as well as

REST APIs.