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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

External data lookups

The first question that must be in your mind is why we need external data lookups in the stream processing pipeline. The answer is that sometimes you need to perform operations such as enrichment, data validation, or data filtering on incoming events based on some frequently changing external system data. However, in the streaming design context, these data lookups pose certain challenges. These data lookups may result in increased end-to-end latency as there will be frequent calls to external systems. You cannot hold all the external reference data in-memory as these external datasets are too big to fit in-memory. They also change too frequently, which makes refreshing memory difficult. If these external systems are down, then they will become a bottleneck for streaming solutions.

Keeping these challenges in mind, there are three important factors while...

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