Learn By Example : Apache Storm
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Learn By Example : Apache Storm

25 Solved examples on Real Time Stream Processing
Bestselling
4.7 (52 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
984 students enrolled
Created by Loony Corn
Last updated 2/2017
English
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 4 hours on-demand video
  • 15 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Build a Storm Topology for processing data
  • Manage reliability and fault tolerance of the topology
  • Control parallelism using different grouping strategies
  • Perform complex transformations using Trident
  • Apply Machine Learning algorithms on the fly in Storm applications
View Curriculum
Requirements
  • Experience in Java programming and familiarity with using Java frameworks
  • A Java IDE such as IntelliJ Idea should be installed
Description

Storm is to real-time stream processing what Hadoop is to batch processing.  Using Storm you can build applications which need you to be highly responsive to the latest data and react within seconds and minutes, such as finding the latest trending topics on twitter, or monitoring  spikes in payment gateway failures. From simple data transformations to applying machine learning algorithms on the fly, Storm can do it all. 

This course has 25 Solved Examples on building Storm Applications.

What's covered?

1) Understanding Spouts and Bolts which are the building blocks of every Storm topology. 

2) Running a Storm topology in the local mode and in the remote mode

3) Parallelizing data processing within a topology using different grouping strategies : Shuffle grouping, fields grouping, Direct grouping, All grouping, Custom Grouping

4) Managing reliability and fault-tolerance within Spouts and Bolts 

5) Performing complex transformations on the fly using the Trident topology : Map, Filter, Windowing and Partitioning operations

6) Applying ML algorithms on the fly using libraries like Trident-ML and Storm-R


Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?
  • Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time
  • Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing
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Curriculum For This Course
35 Lectures
04:04:16
+
Start Here
1 Lecture 02:06

We'll start with an introduction, what the course covers and who it benefits. 

Preview 02:06
+
Stream Processing with Storm
5 Lectures 25:59

Understand how stream processing is different from batch processing

Preview 05:42

Stream processing is great for certain applications, but performance can be an issue at large scale. How do we solve this?

Improving Performance using Distributed Processing
05:39

Understand Spouts and Bolts which make up a Storm topology

Building blocks of Storm Topologies
05:38

Understand how a Storm topology allows parellelism across components

Adding Parallelism in a Storm Topology
04:54

A Storm topology runs on a cluster. Understand the different services which run on the cluster

Components of a Storm Cluster
04:06
+
Implementing a Hello World Topology
4 Lectures 25:20


Ex 1: Implementing a Bolt
04:43

Ex 1: Submitting the Topology
05:14
+
Processing Data using Files
4 Lectures 34:08

Representing Data using Tuples
03:25

Ex 3: Accessing data from Tuples
09:07

Ex 4: Writing Data to a File
09:58
+
Running a Topology in the Remote Mode
2 Lectures 14:42
Setting up a Storm Cluster
07:22

Ex 5: Submitting a topology to the Storm Cluster
07:20
+
Adding Parallelism to a Storm Topology
5 Lectures 24:36
Ex 6 : Shuffle Grouping
06:42

Ex 7: Fields Grouping
04:37

Ex 8: All Grouping
02:22

Ex 9: Custom Grouping
05:16

Ex 10: Direct Grouping
05:39
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Section 7: Building a Word Count Topology
1 Lecture 10:04
Ex 11: Building a Word Count Topology
10:04
+
Remote Procedure Calls Using Storm
1 Lecture 12:48
Ex 12: A Storm Topology for DRPC calls
12:48
+
Managing Reliability of Topologies
1 Lecture 10:31
Ex 13: Managing Failures in Spouts
10:31
+
Integrating Storm with Different Sources/Sinks
2 Lectures 15:33
Ex 14: Implementing a Twitter Spout
08:16

Ex 15: Using a HDFS Bolt
07:17
2 More Sections
About the Instructor
Loony Corn
4.3 Average rating
4,968 Reviews
38,872 Students
77 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)