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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Integrating the presentation layer with Storm


Visualization over data is adding power to know your data in the best way and also you can take key decisions based on those. There are numerous tools available on the market for visualization. Every visualization tool needs a database to store and process the data. Some combinations are Grafana over Elasticseach, Kibana over Elasticsearch,and Grafana over Influxdb. In this chapter, we will discuss the fusion of Grafana, Elasticsearch, and Storm.

In this example, we will use the data stream from PubNub, which provides real-time sensor data. PubNub provides all types of APIs to read data from the channel. Here, a program is required to get the values from the PubNub subscribed channel and push it into a Kafka topic. You will the find program in the code bundle.

Setting up Grafana with the Elasticsearch plugin

Grafana is analytics platform which understands your data and visualizes it on a dashboard.

Downloading Grafana

Download Grafana from https:...

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