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  • Book Overview & Buying Learning Real-time Analytics with Storm and Cassandra
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Learning Real-time Analytics with Storm and Cassandra

Learning Real-time Analytics with Storm and Cassandra

By : Shilpi Saxena
3.5 (2)
Buy this Book
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Learning Real-time Analytics with Storm and Cassandra

Learning Real-time Analytics with Storm and Cassandra

3.5 (2)
By: Shilpi Saxena
Buy this Book

Overview of this book

If you want to efficiently use Storm and Cassandra together and excel at developing production-grade, distributed real-time applications, then this book is for you. No prior knowledge of using Storm and Cassandra together is necessary. However, a background in Java is expected.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Let's Understand Storm
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1. Let's Understand Storm
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Distributed computing problems
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Solutions for complex distributed use cases
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A high-level view of various components of Storm
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Delving into the internals of Storm
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Quiz time
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Summary
2
2. Getting Started with Your First Topology
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2. Getting Started with Your First Topology
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Prerequisites for setting up Storm
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Components of a Storm topology
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Executing a sample Storm topology – local mode
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Executing the topology in the distributed mode
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Executing the topology from Command Prompt
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Quiz time
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Summary
3
3. Understanding Storm Internals by Examples
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3. Understanding Storm Internals by Examples
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Customizing Storm spouts
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Anchoring and acking
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Stream groupings
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Quiz time
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Summary
4
4. Storm in a Clustered Mode
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4. Storm in a Clustered Mode
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The Storm cluster setup
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Zookeeper configurations
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Storm configurations
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Storm monitoring tools
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Quiz time
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Summary
5
5. Storm High Availability and Failover
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5. Storm High Availability and Failover
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An overview of RabbitMQ
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Installing the RabbitMQ cluster
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Integrating Storm with RabbitMQ
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Building high availability of components
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The Storm isolation scheduler
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Quiz time
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Summary
6
6. Adding NoSQL Persistence to Storm
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6. Adding NoSQL Persistence to Storm
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The advantages of Cassandra
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Columnar database fundamentals
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Setting up the Cassandra cluster
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Multiple data centers
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Introduction to CQLSH
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Introduction to CLI
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Using different client APIs to access Cassandra
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Storm topology wired to the Cassandra store
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The best practices for Storm/Cassandra applications
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Quiz time
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Summary
7
7. Cassandra Partitioning, High Availability, and Consistency
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7. Cassandra Partitioning, High Availability, and Consistency
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Consistent hashing
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Replication in Cassandra and strategies
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Cassandra consistency
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Quiz time
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Summary
8
8. Cassandra Management and Maintenance
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8. Cassandra Management and Maintenance
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Cassandra – gossip protocol
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Cassandra cluster scaling – adding a new node
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Cassandra cluster – replacing a dead node
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The replication factor
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The nodetool commands
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Cassandra fault tolerance
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Cassandra monitoring systems
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Quiz time
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Summary
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9. Storm Management and Maintenance
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9. Storm Management and Maintenance
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Scaling the Storm cluster – adding new supervisor nodes
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Scaling the Storm cluster and rebalancing the topology
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Setting up workers and parallelism to enhance processing
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Storm troubleshooting
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Quiz time
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Summary
10
10. Advance Concepts in Storm
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10. Advance Concepts in Storm
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Building a Trident topology
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Understanding the Trident API
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Examples and illustrations
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Quiz time
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Summary
11
11. Distributed Cache and CEP with Storm
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11. Distributed Cache and CEP with Storm
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The need for distributed caching in Storm
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Introduction to memcached
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Introduction to the complex event processing engine
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Quiz time
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Summary
12
A. Quiz Answers
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Chapter 1
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Chapter 2
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Chapter 3
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Chapter 4
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Chapter 5
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Chapter 6
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Chapter 7
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Chapter 8
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Chapter 9
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Chapter 10
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Chapter 11
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Index
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Index

Quiz time

Q.1. State whether the following statements are true or false:

  1. Cache is a read-only memory space.

  2. Data once added in cache remains there forever.

  3. CEP lets SQL-like queries be implemented over streaming data.

  4. Esper is based on event-driven architecture.

Q.2. Fill in the blanks:

  1. _______________ is the algorithm for memcache.

  2. When data is not present in the cache, it's called ______________.

  3. _______________ is the component of Esper that triggers the Endeca Query Language (EQL) execution.

  4. _______________ is generally used for the time series windowing function data.

Q.3. Create an end-to-end topology using Esper to display the top 10 speeding devices on a said freeway using Storm and Esper in conjunction.

CONTINUE READING
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Tech Concepts
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Programming languages
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Learning Real-time Analytics with Storm and Cassandra
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