Home Data Building Data Streaming Applications with Apache Kafka

Building Data Streaming Applications with Apache Kafka

By Chanchal Singh , Manish Kumar
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  1. Free Chapter
    Introduction to Messaging Systems
About this book
Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it.
Publication date:
August 2017
Publisher
Packt
Pages
278
ISBN
9781787283985

 

Introduction to Messaging Systems

People have different styles of learning. This chapter will give you the necessary context to help you achieve a better understanding of the book.

The goal of any Enterprise Integration is to establish unification between separate applications to achieve a consolidated set of functionalities.

These discrete applications are built using different programming languages and platforms. To achieve any unified functionality, these applications need to share information among themselves. This information exchange happens over a network in small packets using different protocols and utilities.

So let us say that you are adding a new campaign component to an existing e-commerce application that needs to interact with a different application to calculate loyalty points. In this case, you will be integrating your e-commerce application with a different application using enterprise integration strategies.

This chapter will help you understand messaging systems, one of the common ways of establishing enterprise integration. It will walk you through various types of messaging system and their uses. At the end of this chapter, you will be able to distinguish between different messaging models available today and understand different design considerations for enterprise application integration.

We will be covering the following topics in this chapter:

  • Principles of designing a good messaging system
  • How a messaging system works
  • A point-to-point messaging system
  • A publish-subscribe messaging system
  • The AMQP messaging protocol
  • Finally we will go through the messaging system needed in designing streaming applications
 

Understanding the principles of messaging systems

Continuing our focus on messaging systems, you may have seen applications where one application uses data that gets processed by other external applications or applications consuming data from one or more data sources. In such scenarios, messaging systems can be used as an integration channel for information exchange between different applications. If you haven't built such an application yet, then don't worry about it. We will build it in upcoming chapters.

In any application integration system design, there are a few important principles that should be kept in mind, such as loose coupling, common interface definitions, latency, and reliability. Let's look into some of these one by one:

  • Loose coupling between applications ensures minimal dependencies on each other. This ensures that any changes in one application do not affect other applications. Tightly coupled applications are coded as per predefined specifications of other applications. Any change in specification would break or change the functionality of other dependent applications.
  • Common interface definitions ensure a common agreed-upon data format for exchange between applications. This not only helps in establishing message exchange standards among applications but also ensures that some of the best practices of information exchange can be enforced easily. For example, you can choose to use the Avro data format to exchange messages. This can be defined as your common interface standard for information exchange. Avro is a good choice for message exchanges as it serializes data in a compact binary format and supports schema evolution.
  • Latency is the time taken by messages to traverse between the sender and receiver. Most applications want to achieve low latency as a critical requirement. Even in an asynchronous mode of communication, high latency is not desirable as significant delay in receiving messages could cause significant loss to any organization.
  • Reliability ensures that temporary unavailability of applications does not affect dependent applications that need to exchange information. In general, the when source application sends a message to the remote application, sometimes the remote application may be running slow or it may not be running due to some failure. Reliable, asynchronous message communication ensures that the source application continues its work and feels confident that the remote application will resume its task later.
 

Understanding messaging systems

As mentioned earlier, application integration is key for any enterprise to achieve a comprehensive set of functionalities spanning multiple discrete applications. To achieve this, applications need to share information in a timely manner. A messaging system is one of the most commonly used mechanisms for information exchange in applications.

The other mechanisms used to share information could be remote procedure calls (RPC), file share, shared databases, and web service invocation. While choosing your application integration mechanism, it is important that you keep in mind the guiding principles discussed earlier. For example, in the case of shared databases, changes done by one application could directly affect other applications that are using the same database tables. Both of the applications are tightly coupled. You may want to avoid that in cases where you have additional rules to be applied before accepting the changes in the other application. Likewise, you have to think about all such guiding principles before finalizing ways of integrating your applications.

As depicted in the following figure, message-based application integration involves discrete enterprise applications connecting to a common messaging system and either sending or receiving data to it. A messaging system acts as an integration component between multiple applications. Such an integration invokes different application behaviors based on application information exchanges. It also adheres to some of the design principles mentioned earlier.

A graphical display of how messaging systems are linked to applications

Enterprises have started adopting micro service architecture and the main advantage of doing so is to make applications loosely coupled with each other. Applications communicate with each other asynchronously and it makes communication more reliable as both applications need not be running simultaneously. A messaging system helps in transferring data from one application to the other. It allows applications to think of what they need to share as data rather than how it needs to be shared. You can share small packets of data or data streams with other applications using messaging in a timely and real-time fashion. This fits the need of low latency real-time application integration.

For a start, you should understand some of the basic concepts of any messaging system. Understanding these concepts is beneficial to you as it will help you understand different messaging technologies such as Kafka. The following are some of the basic messaging concepts:

  • Message queues: You will sometimes find queues referred as channels as well. In a simple way, they are connectors between sending and receiving applications. Their core function is to receive message packets from the source application and send it to the receiver application in a timely and reliable manner.
  • Messages (data packets): A message is an atomic data packet that gets transmitted over a network to a message queue. The sender application breaks data into smaller data packets and wraps it as a message with protocol and header information. It then sends it to the message queue. In a similar fashion, a receiver application receives a message and extracts the data from the message wrapper to further process it.
  • Sender (producer): Sender or producer applications are the sources of data that needs to be sent to a certain destination. They establish connections to message queue endpoints and send data in smaller message packets adhering to common interface standards. Depending on the type of messaging system in use, sender applications can decide to send data one by one or in a batch.
  • Receiver (consumer): Receiver or consumer applications are the receivers of messages sent by the sender application. They either pull data from message queues or they receive data from messages queues through a persistent connection. On receiving messages, they extract data from those message packets and use it for further processing.
  • Data transmission protocols: Data transmission protocols determine rules to govern message exchanges between applications. Different queuing systems use different data transmission protocols. It depends on the technical implementation of the messaging endpoints. Kafka uses binary protocols over TCP. The client initiates a socket connection with Kafka queues and then writes messages along with reading back the acknowledgment message. Some examples of such data transmission protocols are AMQP (Advance Message Queuing Protocol), STOMP (Streaming Text Oriented Message Protocol), MQTT (Message Queue Telemetry Protocol), and HTTP (Hypertext Transfer Protocol).
  • Transfer mode: The transfer mode in a messaging system can be understood as the manner in which data is transferred from the source application to the receiver application. Examples of transfer modes are synchronous, asynchronous, and batch modes.
 

Peeking into a point-to-point messaging system

This section focuses on the point-to-point (PTP) messaging model. In a PTP messaging model, message producers are called senders and consumers are called receivers. They exchange messages by means of a destination called a queue. Senders produce messages to a queue and receivers consume messages from this queue. What distinguishes point-to-point messaging is that a message can be consumed by only one consumer.

Point-to-point messaging is generally used when a single message will be received by only one message consumer. There may be multiple consumers listening on the queue for the same message but only one of the consumers will receive it. Note that there can be multiple producers as well. They will be sending messages to the queue but it will be received by only one receiver.

A PTP model is based on the concept of sending a message to a named destination. This named destination is the message queue's endpoint that is listening to incoming messages over a port.

Typically, in the PTP model, a receiver requests a message that a sender sends to the queue, rather than subscribing to a channel and receiving all messages sent on a particular queue.

You can think of queues supporting PTP messaging models as FIFO queues. In such queues, messages are sorted in the order in which they were received, and as they are consumed, they are removed from the head of the queue. Queues such as Kafka maintain message offsets. Instead of deleting the messages, they increment the offsets for the receiver. Offset-based models provide better support for replaying messages.

The following figure shows an example model of PTP. Suppose there are two senders, S1 and S2, who send a message to a queue, Q1. On the other side, there are two receivers, R1 and R2, who receive a message from Q1. In this case, R1 will consume the message from S2 and R2 will consume the message from S1:

A graphical representation of how a point-to-point messaging model works

You can deduce the following important points about a PTP messaging system from the preceding figure:

  • More than one sender can produce and send messages to a queue. Senders can share a connection or use different connections, but they can all access the same queue.
  • More than one receiver can consume messages from a queue, but each message can be consumed by only one receiver. Thus, Message 1, Message 2, and Message 3 are consumed by different receivers. (This is a message queue extension.)
  • Receivers can share a connection or use different connections, but they can all access the same queue. (This is a message queue extension.)
  • Senders and receivers have no timing dependencies; the receiver can consume a message whether or not it was running when the sender produced and sent the message.
  • Messages are placed in a queue in the order they are produced, but the order in which they are consumed depends on factors such as message expiration date, message priority, whether a selector is used in consuming messages, and the relative message processing rate of the consumers.
  • Senders and receivers can be added and deleted dynamically at runtime, thus allowing the messaging system to expand or contract as needed.

The PTP messaging model can be further categorized into two types:

  • Fire-and-forget model
  • Request/reply model

In fire-and-forget processing, the producer sends a message to a centralized queue and does not wait for any acknowledgment immediately. It can be used in a scenario where you want to trigger an action or send a signal to the receiver to trigger some action that does not require a response. For example, you may want to use this method to send a message to a logging system, to alert a system to generate a report, or trigger an action to some other system. The following figure represents a fire-and-forget PTP messaging model:

Fire-and-forget message model

With an asynchronous request/reply PTP model, the message sender sends a message on one queue and then does a blocking wait on a reply queue waiting for the response from the receiver. The request/reply model provides for a high degree of decoupling between the sender and receiver, allowing the message producer and consumer components to be heterogeneous languages or platforms. The following figure represents a request/reply PTP messaging model:

Request/reply message model

Before concluding this section, it is important for you to understand where you can use the PTP model of messaging. It is used when you want one receiver to process any given message once and only once. This is perhaps the most critical difference: only one consumer will process a given message.

Another use case for point-to-point messaging is when you need synchronous communication between components that are written in different technology platforms or programming languages. For example, you may have an application written in a language, say PHP, which may want to communicate with a Twitter application written in Java to process tweets for analysis. In this scenario, a point-to-point messaging system helps provide interoperability between these cross-platform applications.

 

Publish-subscribe messaging system

In this section, we will take a look at a different messaging model called the publish/subscribe (Pub/Sub) messaging model.

In this type of model, a subscriber registers its interest in a particular topic or event and is subsequently notified about the event asynchronously. Subscribers have the ability to express their interest in an event, or a pattern of events, and are subsequently notified of any event generated by a publisher that matches their registered interest. These events are generated by publishers. It is different from the PTP messaging model in a way that a topic can have multiple receivers and every receiver receives a copy of each message. In other words, a message is broadcast to all receivers without them having to poll the topic. In the PTP model, the receiver polls the queue for new messages.

A Pub/Sub messaging model is used when you need to broadcast an event or message to many message consumers. Unlike the PTP messaging model, all message consumers (called subscribers) listening on the topic will receive the message.

One of the important aspects of Pub/Sub messaging models is that the topic abstraction is easy to understand and enforces platform interoperability. Moreover, messages can be retained in the topic until they are delivered to the active subscribers.

There is an option to have durable subscriptions in the Pub/Sub model that allows the subscriber to disconnect, reconnect, and collect the messages that were delivered when it was not active. The Kafka messaging system incorporates some of these important design principles.

The following figure describes a basic model of publish/subscribe messaging. Such event services are generally called queues. This kind of interaction need a service that provides storage of the event, a notification service, a way of managing subscriptions and ensuring the efficient guaranteed delivery of the event to destination. Generally, we call this service a queue. Queues act as a neutral mediator between the event producer and event consumer. The producer can produce all the data to queue that they want to and all the consumers will subscribe to the queue that they are interested in. The consumer does not care about the source and the producer does not care about consumers. Consumers can unsubscribe to a queue whenever they want to:

A graphical representation of a publish/subscribe message model

You can deduce the following important points about the Pub/Sub messaging system from the preceding figure:

  • Messages are shared through a channel called a topic. A topic is a centralized place where producers can publish, and subscribers can consume, messages.
  • Each message is delivered to one or more message consumers, called subscribers.
  • The publisher generally does not know and is not aware of which subscribers are receiving the topic messages.
  • Messages are pushed to consumers, which means that messages are delivered to consumers without their having to request them. Messages are exchanged through a virtual channel called a topic. Messages delivered to a topic are automatically pushed to all qualified consumers.
  • There is no coupling of the producers to the consumers. Subscribers and publishers can be added dynamically at runtime, which allows the system to grow or shrink in complexity over time.
  • Every client that subscribes to a topic receives its own copy of messages published to that topic. A single message produced by one publisher may be copied and distributed to hundreds, or even thousands, of subscribers.

You should use the Pub/Sub model when you want to broadcast a message or event to multiple message consumers. The important point here is that multiple consumers may consume the message.

By design, the Pub/Sub model will push copies of the message out to multiple subscribers. Some common examples are notifying exceptions or errors and change the notification of a particular data item in the database.

Any situation where you need to notify multiple consumers of an event is a good use of the Pub/Sub model. For example, you want to send out a notification to a topic whenever an exception occurs in your application or a system component. You may not know how that information will be used or what types of component will use it. Will the exception be e-mailed to various parties of interest? Will a notification be sent to a beeper or pager? This is the beauty of the Pub/Sub model. The publisher does not care or need to worry about how the information will be used. It simply publishes it to a topic.

 

Advance Queuing Messaging Protocol

As discussed in previous sections, there are different data transmission protocols using which messages can be transmitted among sender, receiver, and message queues. It is difficult to cover all such protocols in the scope of this book. However, it is important to understand how these data transmission protocols work and why it is an important design decision for your message-oriented application integration architecture. In the light of this, we will cover one example of such a protocol: Advance Message Queuing Protocol also known as AQMP.

AQMP is an open protocol for asynchronous message queuing that developed and matured over several years. AMQP provides richer sets of messaging functionalities that can be used to support very advanced messaging scenarios. As depicted in the following figure, there are three main components in any AQMP-based messaging system:

AQMP architecture

As the name suggests, producers sends messages to brokers that in turn deliver them to consumers. Every broker has a component called exchange that is responsible for routing the messages from producers to appropriate message queues.

An AQMP messaging system consists of three main components:

  • Publisher(s)
  • Consumer(s)
  • Broker/server(s)

Each component can be multiple in number and situated on independent hosts. Publishers and consumers communicate with each other through message queues bound to exchanges within the brokers. AQMP provides reliable, guaranteed, in-order message delivery. Message exchanges in an AQMP model can follow various methods. Let's look at each one of them:

  • Direct exchange: This is a key-based routing mechanism. In this, a message is delivered to the queue whose name is equal to the routing key of the message.
  • Fan-out exchange: A fan-out exchange routes messages to all of the queues that are bound to it and the routing key is ignored. If N queues are bound to a fan-out exchange, when a new message is published to that exchange, a copy of the message is delivered to all N queues. Fan-out exchanges are ideal for the broadcast routing of messages. In other words, the message is cloned and sent to all queues connected to this exchange.
  • Topic exchange: In topic exchange, the message can be routed to some of the connected queues using wildcards. The topic exchange type is often used to implement various publish/subscribe pattern variations. Topic exchanges are commonly used for the multicast routing of messages.
 

Using messaging systems in big data streaming applications

In this section, we will talk about how messaging systems play important role in a big data application.

Let's understand the different layers in a big data application:

  • Ingestion layer: The input data required for the processing gets ingested in some storage system. There can be many sources of data for which the same or different processing needs to be done.
  • Processing layer: This contains the business logic that processes the data received in the ingestion layer and applies some transformation to make it into a usable form. You can call it converting raw data to information. There can be multiple processing applications for the same or different data. Each application may have its different processing logic and capability.
  • Consumption layer: This layer contains data processed by the processing layer. This processed data is a single point of truth and contains important information for business decision makers. There can be multiple consumers who can use the same data for different purposes or different data for the same purpose.

Streaming applications would probably fall into the second layer--the processing layer. The same data can be used by many applications simultaneously, and there can be different ways of serving this data to the application. So, applications can be either streaming, batch, or micro-batch. All these applications consume data in different ways: streaming applications may require data as a continuous stream and batch applications may require data as batches. However, we have already said that there can be multiple sources for this data.

We can see multiple producer and multiple consumer use cases here, so we have to go for a messaging system. The same message can be consumed by multiple consumers so we need to retain the message until all the consumers consume it. How about having a messaging system that can retain the data until we want it, provides a high degree of fault tolerance, and provides a different way of consuming data streams, batches, and micro-batches?

Streaming applications will simply consume the data from the messaging queue that they want and process it as needed. However, there is one problem. What if the message received by the streaming application fails, what if there are a lot of such messages? In such cases, we may want to have a system that will help us provide those messages based on the request and reprocess them.

We need a messaging system that immediately tells the streaming application that, Something got published; please process it. The following diagram helps you understand a messaging system use case with a streaming application:

Real-time streaming with queues

The preceding figure explains the following points:

  • Streaming application 1 has subscribed to Topic 1, which means any event published to topic 1 will be immediately available to Streaming Application 1.
  • Streaming Application 1 processes the event and stores them into two destinations; one is a database and other is Topic 2 of the messaging system. Here, the the streaming application acts as the producer for Topic 2. Remember there can be other applications that may consume the event from Topic 1.
  • Streaming application 2 has subscribed to Topic 2, which will immediately receive the event when it gets published to Topic 2. Remember that there can be other applications that can publish the event to either Topic 1 or Topic 2.
  • Streaming Application 2 processes the event and stores it in the database.

In streaming application, each stream or message has its own importance; something will be triggered based on the type or nature of the message. There can be a scenario where one streaming application processes the event and passes it to another streaming application for further processing. In this case, they both need to have a medium of communication. Remember that the application should care about what it wants to do rather than how to send the data somewhere. This is the best use case for a publish/subscribe messaging system as it would ensure that a message published by the producer will reach to all the applications who have subscribed to it.

Concluding our discussion on messaging systems, these are the points that are important for any streaming application:

  • High consuming rate: Streaming data sources can be click-stream data or social media data where the rate of message producing is too high. Stream applications may or may not be required to consume at a similar rate. We may want to have a messaging queue that can consume data at a higher rate.
  • Guaranteed delivery: Some streaming applications cannot afford to lose messages; we need a system that guarantees the delivery of messages to the streaming application whenever needed.
  • Persisting capability: There can be multiple applications consuming similar data at a different rate. We may want to have a messaging system that retains data for a period of time and serves the data to a different application asynchronously. This helps in decoupling all the applications and designing micro service architecture.
  • Security: Some applications may want to have security over the data that they consume; you may not want to share some data with other applications consuming from the same messaging system. You want to have a system that ensures such security.
  • Fault tolerance: Applications never want to have a system that does not deliver messages or data whenever they need. We want to have a system that guarantees fault tolerance and serves messages irrespective of the failure of the server serving the data before.

There are many other points that force us to go for a messaging system that has at least the capabilities mentioned earlier. We will discuss how Kafka is different from other messaging systems, and meets the requirement of a messaging system for a streaming application, in upcoming chapters.

 

Summary

In this chapter, we covered concepts of messaging systems. We learned the need for Messaging Systems in Enterprises. We further emphasized different ways of using messaging systems such as point to point or publish/subscribe. We introduced Advance Message Queuing Protocol (AQMP) as well.

In next chapter, we will learn about the Kafka architecture and its component in detail. We will also learn about implementation part of what we discuss in messaging system and its type.

About the Authors
  • Chanchal Singh

    Chanchal Singh has over half decades experience in Product Development and Architect Design. He has been working very closely with leadership team of various companies including directors ,CTO's and Founding members to define technical road-map for company.He is the Founder and Speaker at meetup group Big Data and AI Pune MeetupExperience Speaks. He is Co-Author of Book Building Data Streaming Application with Apache Kafka. He has a Bachelor's degree in Information Technology from the University of Mumbai and a Master's degree in Computer Application from Amity University. He was also part of the Entrepreneur Cell in IIT Mumbai. His Linkedin Profile can be found at with the username Chanchal Singh.

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  • Manish Kumar

    Manish Kumar works as Director of Technology and Architecture at VSquare. He has over 13 years' experience in providing technology solutions to complex business problems. He has worked extensively on web application development, IoT, big data, cloud technologies, and blockchain. Aside from this book, Manish has co-authored three books (Mastering Hadoop 3, Artificial Intelligence for Big Data, and Building Streaming Applications with Apache Kafka).

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Latest Reviews (5 reviews total)
Good book but too much focus on Java/Scala
Excellent book, is not only about Kafka
Thorough book. Explains subject matter in great detail.