Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Learning Real-time Analytics with Storm and Cassandra

You're reading from   Learning Real-time Analytics with Storm and Cassandra Solve real-time analytics problems effectively using Storm and Cassandra

Arrow left icon
Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781784395490
Length 220 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Let's Understand Storm FREE CHAPTER 2. Getting Started with Your First Topology 3. Understanding Storm Internals by Examples 4. Storm in a Clustered Mode 5. Storm High Availability and Failover 6. Adding NoSQL Persistence to Storm 7. Cassandra Partitioning, High Availability, and Consistency 8. Cassandra Management and Maintenance 9. Storm Management and Maintenance 10. Advance Concepts in Storm 11. Distributed Cache and CEP with Storm A. Quiz Answers Index

Storm topology wired to the Cassandra store


Now you have been educated and informed about why you should use Cassandra. You have been walked through setting up Cassandra and column family creation, and have even covered the various client/protocol options available to access the Cassandra data store programmatically. As mentioned earlier, Hector has so far been the most widely used API for accessing Cassandra, though the Datastax and Astyanax drivers are fast catching up. For our exercise, we'll use the Hector API.

The use case we want to implement here is to use Cassandra to support real-time, adhoc reporting for telecom data that is being collated, parsed, and enriched using a Storm topology.

As depicted in the preceding figure, the use case requires live telecom Call Detail Record (CDR) capture using the data collection components (for practice, we can use sample records and a simulator shell script to mimic the live CDR feeds). The collated live feed is pushed into the RabbitMQ broker...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime