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Building Data Streaming Applications with Apache Kafka

You're reading from   Building Data Streaming Applications with Apache Kafka Design, develop and streamline applications using Apache Kafka, Storm, Heron and Spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787283985
Length 278 pages
Edition 1st Edition
Tools
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Authors (2):
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Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Messaging Systems FREE CHAPTER 2. Introducing Kafka the Distributed Messaging Platform 3. Deep Dive into Kafka Producers 4. Deep Dive into Kafka Consumers 5. Building Spark Streaming Applications with Kafka 6. Building Storm Applications with Kafka 7. Using Kafka with Confluent Platform 8. Building ETL Pipelines Using Kafka 9. Building Streaming Applications Using Kafka Streams 10. Kafka Cluster Deployment 11. Using Kafka in Big Data Applications 12. Securing Kafka 13. Streaming Application Design Considerations

Use case log processing - fraud IP detection

This section will cover a small use case which uses Kafka and Spark Streaming to detect a fraud IP, and the number of times the IP tried to hit the server. We will cover the use case in the following:

  • Producer: We will use Kafka Producer API, which will read a log file and publish records to Kafka topic. However, in a real case, we may use Flume or producer application, which directly takes a log record on a real-time basis and publish to Kafka topic.
  • Fraud IPs list: We will maintain a list of predefined fraud IP range which can be used to identify fraud IPs. For this application we are using in memory IP list which can be replaced by fast key based lookup, such as HBase.
  • Spark Streaming: Spark Streaming application will read records from Kafka topic and will detect IPs and domains which are suspicious.
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