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Apache Storm Machine Learning

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Course Description:

This excellent Machine Learning – Apache Storm: Learn by Example course will teach you how to use Apache Storm to create applications, which will be extremely responsive to the latest data, and will react within seconds. Your Apache Storm applications will be able to do amazing things, like find the latest topics on Twitter, or monitor spikes in gateway failures. If you’re someone who works in computer programming or app development, or if you have a general interest in these areas, this course is for you! Understand how to use Apache Storm, and make your own incredible applications.

Our learning material is available to students 24/7 anywhere in the world, so it’s extremely convenient. These intensive online courses are open to everyone, as long as you have an interest in the topic! We provide world-class learning led by IAP, so you can be assured that the material is high quality, accurate and up-to-date.

What skills will I gain?

  • 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

What are the requirements?

  • You must be 16 or over
  • You should have a basic understanding of English, Maths and ICT
  • You will need a computer or tablet with internet connection (or access to one)
  • Experience in Java programming and familiarity with using Java frameworks
  • A Java IDE such as IntelliJ Idea should be installed on your computer

Meet the Instructor:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

  • 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
  • Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
  • Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

Course outline:

  • Module 01: You, This Course, and Us
  • Module 02: Stream Processing with Storm
  • Module 03: Implementing a Hello World Topology
  • Module 04: Processing Data using Files
  • Module 05: Running a Topology in the Remote Mode
  • Module 06: Adding Parallelism to a Storm Topology
  • Module 07: Building a Word Count Topology
  • Module 08: Remote Procedure Calls Using Storm
  • Module 09: Managing Reliability of Topologies
  • Module 10: Integrating Storm with Different Sources/Sinks
  • Module 11: Using the Storm Multilang Protocol
  • Module 12: Complex Transformations using Trident

How will I be assessed?

  • You will have one assignment. Pass mark is 65%.
  • You will only need to pay £19 for assessment.
  • You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days.

What Certification am I going to receive?

Those who successfully pass this course will be awarded a Machine Learning – Apache Storm: Learn by Example certificate. Anyone eligible for certification will receive a free e-certificate, and printed certificate.

What careers can I get with this qualification?

Once you have completed this Machine Learning – Apache Storm: Learn by Example course you will have desirable skills. You could go on to further study of this topic, or could gain entry level employment in web development, or app development. These roles often command a high salary, for example, the average salary of a Web Developer in the UK is £24, 901, and this will go up with experience ( When you complete this Machine Learning – Apache Storm: Learn by Example, you could fulfil any of the following roles:

  • Java Developer
  • Senior Java Developer
  • Java Software Developer / Programmer
  • Java Programmer
  • Java Web Software Developer
  • Front End Web Developer

Key Features

Gain an accredited UK qualification

Access to excellent quality study materials

Learners will be eligible for TOTUM Discount Card

Personalized learning experience

One year’s access to the course

Support by phone, live chat, and email

Course Curriculum Total Units : 35
1: You, This Course, and Us
1. Introduction
2: Stream Processing with Storm
1. How does Twitter compute Trends?
2. Improving Performance using Distributed Processing
3. Building blocks of Storm Topologies
4. Adding Parallelism in a Storm Topology
5. Components of a Storm Cluster
3: Implementing a Hello World Topology
1. A Simple Hello World Topology
2. Ex 1: Implementing a Spout
3. Ex 1: Implementing a Bolt
4. Ex 1: Submitting the Topology
4: Processing Data using Files
1. Ex 2: Reading Data from a File
2. Representing Data using Tuples
3. Ex 3: Accessing data from Tuples
4. Ex 4: Writing Data to a File
5: Running a Topology in the Remote Mode
1. Setting up a Storm Cluster
2. Ex 5: Submitting a topology to the Storm Cluster
6: Adding Parallelism to a Storm Topology
1. Ex 6 : Shuffle Grouping
2. Ex 7: Fields Grouping
3. Ex 8: All Grouping
4. Ex 9: Custom Grouping
5. Ex 10: Direct Grouping
7: Building a Word Count Topology
1. Ex 11: Building a Word Count Topology
8: Remote Procedure Calls Using Storm
1. Ex 12: A Storm Topology for DRPC calls
9: Managing Reliability of Topologies
1. Ex 13: Managing Failures in Spouts
10: Integrating Storm with Different Sources/Sinks
1. Ex 14: Implementing a Twitter Spout
2. Ex 15: Using a HDFS Bolt
11: Using the Storm Multilang Protocol
1. Ex 16: Building a Storm Topology using Python
12: Complex Transformations using Trident
1. Ex 17: Building a basic Trident Topology rs Classifier
2. Ex 18: Implementing a Map Function
3. Ex 19: Implementing a Filter Function
4. Ex 20: Aggregating data Classifiers
5. Ex 21: Understanding States
6. Ex 21: Understanding States
7. Ex 23: Joining data streams
8. Ex 24: Building a Twitter Hashtag Extractor
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