Applying Real-time Processing Using Apache Storm

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Storm lets you to work with large scale streaming data using it's distributed real-time processing architecture. This course discusses the components of Storm topologies and how to use Storm for applying machine learning in real-time.

1-Course Overview

1 -Course Overview

2-Understanding the Components of Storm

2 -Contrasting Real-time and Batch Processing

3 -Understanding the Components of Storm

4 -Representing Data in Storm Components

5 -Building a Hello World Topology

6 -Implementing the Spout

7 -Implementing the Bolt

8 -Running the Topology

3-Parallelizing Data Processing Using Storm Components

9 -Understanding Parallelism in a Storm Cluster

10 -Setting up a Remote Cluster

11 -Running a Topology on a Remote Cluster

12 -Controlling Data Flow with Stream Grouping

13 -Building a Word Count Topology

14 -Implementing the Topology Components

15 -Contrasting Different Stream Grouping Strategies

16 -Implementing a Custom Stream Grouping

4-Customizing Storm Components for Better Reliability

17 -Guaranteeing Message Processing in Storm

18 -Managing Reliability in Storm Components

19 -Implementing Reliability in a Spout

20 -Implementing Reliability in a Bolt

5-Querying Storm Data Streams Using Trident

21 -Processing Data Using Trident

22 -Implementing a Trident Topology to Process Text

23 -Aggregating Data Streams

24 -Using Trident for Distributed Remote Procedure Calls

25 -Maintaining and Querying State

26 -Querying Word Counts

6-Applying Machine Learning to Storm Data Streams

27 -Understanding the Machine Learning Process

28 -Integrating Storm with Python

29 -Predicting Sentiment in Real-time

30 -Training a Sentiment Analysis Model in Python

31 -Building a Storm Topology to Predict Sentiment

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تولید کننده: PluralSight