Learn By Example : Apache Storm

BY
Udemy

Gain a practical grasp of data streaming and processing with Apache Storm using practical examples.

Mode

Online

Fees

₹ 1799

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course overview

Apache Storm is open and free access distributed real-time computation platform, that is used for real-time processing of everything Hadoop achieved for batch systems by making it simple to safely process unlimited streams of data. Apache Storm is easy to use, compatible with all programming languages, and a wonderful experience. Learn By Example: Apache Storm online certification was designed by Loony Corn, a team of ex-Google, Stanford, and Flipkart employees, which is provided by Udemy.

Learn By Example: Apache Storm online training is developed for individuals who are looking for a practical training program that could help them master the principles and strategies involved with Apache Storm using real examples. With Learn By Example: Apache Storm online classes, individuals will be provided with 4 hours of digital sessions supported by 15 downloadable study materials which cover topics like application development, data processing, stream processing, distributed processing, grouping, parallelism, transformations, ML algorithms and more.

The highlights

  • Certificate of completion
  • Self-paced course
  • 4 hours of pre-recorded video content
  • 15 downloadable resources

Program offerings

  • Online course
  • Learning resources
  • 30-day money-back guarantee
  • Unlimited access
  • Accessible on mobile devices and tv

Course and certificate fees

Fees information
₹ 1,799
certificate availability

Yes

certificate providing authority

Udemy

What you will learn

Software development skills Knowledge of python Application of ml algorithms

After completing the Learn By Example: Apache Storm certification course, individuals will develop an understanding of the strategies and concepts involved with apache storm for developing applications, word count topology, and storm topology using python. Individuals will explore the fundamentals associated with data streaming, data processing, distributed processing, and grouping using apache storm. Individuals will learn about concepts involved with reliability, fault tolerance, Twitter spout, parallelism, transformation, HDFS, and DRPC calls. In addition, individuals will gain knowledge on how to use machine learning algorithms in storm applications.

The syllabus

Start Here

  • You, This Course and Us

Stream Processing with Storm

  • How does Twitter compute Trends?
  • Improving Performance using Distributed Processing
  • Building blocks of Storm Topologies
  • Adding Parallelism in a Storm Topology
  • Components of a Storm Cluster

Implementing a Hello World Topology

  • A Simple Hello World Topology
  • Ex 1: Implementing a Spout
  • Ex 1: Implementing a Bolt
  • Ex 1: Submitting the Topology

Processing Data using Files

  • Ex 2: Reading Data from a File
  • Representing Data using Tuples
  • Ex 3: Accessing data from Tuples
  • Ex 4: Writing Data to a File

Running a Topology in the Remote Mode

  • Setting up a Storm Cluster
  • Ex 5: Submitting a topology to the Storm Cluster

Adding Parallelism to a Storm Topology

  • Ex 6 : Shuffle Grouping
  • Ex 7: Fields Grouping
  • Ex 8: All Grouping
  • Ex 9: Custom Grouping
  • Ex 10: Direct Grouping

Section 7: Building a Word Count Topology

  • Ex 11: Building a Word Count Topology

Remote Procedure Calls Using Storm

  • Ex 12: A Storm Topology for DRPC calls

Managing Reliability of Topologies

  • Ex 13: Managing Failures in Spouts

Integrating Storm with Different Sources/Sinks

  • Ex 14: Implementing a Twitter Spout
  • Ex 15: Using a HDFS Bolt

Using the Storm Multilang Protocol

  • Ex 16: Building a Storm Topology using Python

Complex Transformations using Trident

  • Ex 17: Building a basic Trident Topology
  • Ex 18: Implementing a Map Function
  • Ex 19: Implementing a Filter Function
  • Ex 20: Aggregating data
  • Ex 21: Understanding States
  • Ex 22: Windowing operations
  • Ex 23: Joining data streams
  • Ex 24: Building a Twitter Hashtag Extractor

Instructors

Mr Janani Ravi
Instructor
Udemy

Mr Vitthal Srinivasan
Instructor
Udemy

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