Apache Storm is a free and open source, distributed real-time computation system for processing fast, large streams of data. Storm adds reliable real-time data processing capabilities to Apache Hadoop 2.x. Its effective stream processing capabilities are trusted by Twitter and Yahoo for quickly extracting insights from their Big Data.

1. Introduction to Real Time
Analytics using Apache Storm
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Batch Starts On: 17th May 07:00 AM , IST / 16th May 06:30 PM, PDT
Course Fee: USD 329 / INR (17795 + 12.36% Service tax)**
Introductory (15% OFF) Price : USD 280 / INR 15126
For Existing edureka Customers (25% OFF) Price : USD 247/ INR 13346
* Offer expires on 11th May
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2. Objectives of this Session
• Un
• The need for Real Time Analytics - Usecases
• How does Storm come to rescue?
• Where does Storm fit in Hadoop Framework?
• Storm Architecture – Components of Storm
• Quiz to reinforce your learning
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3. Need of Real Time Analytics
Ret
• Banking - Fraud Transaction Detection
• Telecommunication – Silent Roamers Detection
• Retail- Inventory Dynamic Pricing
• Social Networking- Trending Topics
*Covered in module 5 and 6 in the course
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4. Growing Interest in Apache Storm
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5. Storm Usecases – Need for Real Time Analytics
Twitter Trends
Responsive Logs
Source: https://github.com/nathanmarz/storm/wiki/Powered-By
Custom Magazine Feeds
Real Time Video Analytics
Enable Clinicians to Make
Medical Decisions
Compare and Display
Real Time Prices
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6. What is Storm ?
 Apache Storm is a free and open source distributed real-time computation system.
 Storm makes it easy to reliably process unbounded streams of data.
 Storm does for real-time processing what Hadoop did for batch processing.
 Simple, can be used with any programming language.
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7. Understanding the Storm Architecture
Nimbus
Zookeeper
Supervisor
Zookeeper
Zookeeper
Supervisor
Supervisor
Supervisor
Supervisor
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*Covered in module 2 in the course
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8. ZooKeeper
Nimbus ZooKeeper
ZooKeeper
Supervisor
Supervisor
Supervisor
Supervisor
Supervisor
Nimbus node (master node, similar to the Hadoop
JobTracker):
» Uploads computations for execution
» Distributes code across the cluster
» Launches workers across the cluster
» Monitors computation and reallocates
workers as needed
ZooKeeper nodes:
» Coordinates the Storm cluster
Supervisor nodes :
» Communicates with Nimbus through
Zookeeper, starts and stops workers
according to signals from Nimbus
Storm Components
A Storm cluster has 3 sets of nodes
1. Nimbus node
2. Zookeeper nodes
3. Supervisor nodes
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9. The work is delegated to different types of components that are each responsible for a simple specific processing task.
The input stream of a Storm cluster is handled by a component called a spout.
The spout passes the data to a component called a bolt, which transforms it in some way.
A bolt either persists the data in some sort of storage, or passes it to some other bolt.
Storm Topology
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spout
spout
bolt
bolt
bolt
bolt
passes data
passes data
transforms data
data storage
Input Data
Source

10. Why Storm is ideal for Real Time Processing
Fast – benchmarked as processing one million, 100 byte messages, per second per node.
Scalable – with parallel calculations that run across a cluster of machines.
Fault-tolerant – when workers die, Storm will automatically restart them. If a node dies, the
worker will be restarted on another node.
Reliable – Storm guarantees that each unit of data (tuple) will be processed at least once or
exactly once. Messages are only replayed when there are failures.
Easy to operate – standard configurations are suitable for production on day one. Once
deployed, Storm is easy to operate.
http://hortonworks.com/hadoop/storm/
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11. MapReduce
(Batch)
INTERACTIVE
(Text)
ONLINE
(HBase)
STORM
(Streaming)
GRAPH
(Giraph)
IN-MEMORY
(Spark)
HPC MPI
(OpenMPI)
OTHER
(Search)
(Weave..)
http://hadoop.apache.org/docs/stable2/hadoop-yarn/hadoop-yarn-site/YARN.html
Storm in the Hadoop Framework
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12. Upcoming Batch for Storm
Start Date:
17th May (07:00 AM – 10:00 AM, India Time) / 16th May (06:30 PM – 09:30 PM, Pacific Time)
Curriculum:
Module 1: Introduction of Big Data and Storm
Module 2: Getting Started with Storm
Module 3: Spouts and Bolts
Module 4: Trident Topologies
Module 5: Real Life Storm Project – 1
Module 6: Real Life Storm Project – 2
Price:
Course Fee: USD 329 / INR (17795 + 12.36% Service tax)**
Introductory Discount : 15%
Discount for Existing Edureka Customers: 25%
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13. www.edureka.in/apache-storm
Annie’s Question
Storm can be used in:
- Real-time Processing
- Batch Processing
- Both

14. www.edureka.in/apache-storm
Annie’s Answer
Real-time Processing

15. www.edureka.in/apache-storm
Annie’s Question
Which of them can be a source of Stream?
- Spout
- Bolt
- Both

16. www.edureka.in/apache-storm
Annie’s Answer
Both

17. www.edureka.in/apache-storm
Annie’s Question
It is not possible to run Storm process along with MapReduce jobs inside a
Hadoop Cluster.
- True
- False

18. www.edureka.in/apache-storm
Annie’s Answer
False. With Hadoop 2.0, it is possible.

19. www.edureka.in/apache-storm
Annie’s Question
A Nimbus Node is similar to TaskTracker Node in Hadoop Cluster.
- True
- False

20. www.edureka.in/apache-storm
Annie’s Answer
No. A Nimbus Node is more like a JobTracker Node in Hadoop

21. www.edureka.in/apache-storm
Annie’s Question
A Storm topology is defined in terms of
- Nimbus, Zookeeper, Supervisor nodes
- Spout, Bolt
- Spout, Bolt, Nimbus, Zookeeper, Supervisor nodes
- Spout, Bolt, Zookeeper node

22. www.edureka.in/apache-storm
Annie’s Answer
Spout and Bolt

23. Questions?
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Buy Complete Course at : www.edureka.in/apache-storm
Batch Starts On: 17th May 07:00 AM , IST / 16th May 06:30 PM, PDT
Course Fee: USD 329 / INR (17795 + 12.36% Service tax)**
Introductory (15% OFF) Price : USD 280 / INR 15126
For Existing edureka Customers (25% OFF) Price : USD 247/ INR 13346
* Offer expires on 11th May