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Machine Learning – Apache Storm: Learn by Example

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Overview The course will get you up to date on building a storm topology. If you aspire to work in …

Overview

The course will get you up to date on building a storm topology. If you aspire to work in software engineering or data engineering, this is an excellent course for you. The certification will set you apart from other candidates, and make you competitive in the industry. You can further your career by using this qualification and get closer to achieving your career goals. In this ’Apache Storm: Learn by Example’ online course, you will learn how to use Storm to 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.  Supplemental material included!

Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.

This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).

  • Who is it for?
  • Course description:
  • Course Duration:
  • Method of Assessment:
  • Certification:
  • Entry Requirement:
  • Career Path:
  • Presenter Information:
  • Software engineers
  • Data engineers

This course consists of the following modules:

  • 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

From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.

At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed the course.

Successful candidates will be awarded a certificate in Machine Learning – Apache Storm: Learn by Example.

  • Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
  • Experience in Java programming
  • Familiarity in using the Java framework

Once you successfully complete the course in Machine Learning – Apache Storm: Learn by Example, you will gain a recognised qualification that will prove your expertise in using Storm. You can use the certification to enhance career opportunities and find the desired job role in the industry. A career with bright prospects await you with the completion of this course. According to www.payscale.uk, some of the key job positions along with the average UK salary per annum you can go for after completing this course will be as follows.

  • Software Engineer - £35,214 per annum
  • Big Data Engineer - £39,259 per annum

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

 

PLEASE NOTE: We do not provide any software with this course.

Course Curriculum

Free Introduction
Machine Learning – Apache Storm Learn by Example FREE 00:00:00
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 5: Submitting a topology to the Storm Cluster
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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