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Corporate Bridge Consultancy Private Limited
Updated on Sep, 2026
Programming, Database and Design Development, Apache Spark
Duration - 1.5 hours
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Apache Storm is a real-time distributed framework for real-time processing of Big Data. It allows you to perform real-time analytics of a wide variety of streamed data. Apache Storm is written using Java and Clojure. It is designed to process vast amounts of data in a fault-tolerant and horizontally scalable method. Apache Storm continues to be the leader in real-time analytics. It is easy to install, set up and operate. Apache Storm is used in many fields to deal with large data.
Here is a list of all the benefits of Apache Storm:
Apache Storm is suitable in various use cases and a few are listed below:
At the end of this course you will be able to:
Apache Storm was originally created by Nathan Marz while he was working at BackType. Nathan discovered Storm because of the cumbersome and brittle system of distributed queues and workers faced in another real-time component. Storm was the first to introduce the concept of stream which is a fault tolerant and reliable model. Storm is now acquired and open-sourced by Twitter. In a very short period of time, Apache Storm has become a more popular and leading real-time processing system. This chapter contains a brief introduction to Apache and explains the history of Apache Storm.
Apache Storm has a lot of good features than other real-time processing systems. The below-mentioned are the top features of Apache Storm
The architecture of Apache Storm
One of the main advantages of Apache Storm is its fault-tolerant and no single point of failure. In this chapter, a pictorial representation of the architecture of Apache Storm is given for easy understanding of the cluster design of the storm. Apache Storm has two types of nodes, the Nimbus which is the Master node and the Supervisor which is the Worker node. The goal of Nimbus is to run the storm topology and the work of the supervisor is to delegate the tasks to the worker processes.
The below-mentioned components are explained in detail in this chapter:
Architecture Explanation in Detail:
Storm has an architecture that helps it to process real-time data in the best possible and quickest way. It contains a monitoring tool called monit which helps the process to restart if there is any failure. Storm has an advanced topology called Trident Topology which provides a high-level API like Pig. All these features are discussed in detail in this chapter.
Topology is a graph of operators and streams. Topology is a combination of Spouts and Bolts. Topology helps to define a streaming application in Storm. The node in a topology contains processing logic. The links in topology determine how the data should be run through the nodes. The process of running a topology is straightforward. Apache Storm’s main objective is to run the topology as many times until the topology is killed. The topology command is explained in detail in this lesson.
The topology in Apache Storm is a thrift structure. Topology Builder has simple and easy methods to create topologies. Topology Builder has a Create Topology syntax to create a new topology. The code to create a topology is given in this chapter.
Apache Storm has an advanced topology called Trident Topology. Trident has functions, filters, joins, grouping and aggregation. These components are explained in detail in this chapter.
The other topics included in this chapter are listed below :
A topology usually starts with Spouts. These are the sources of streams in a topology that are used for data creation. Spout reads tuples from a messaging framework and transfers them to one or more bolts. A tuple is a named list of values in Apache Storm.
Spout will implement an “IRichSpout” interface which has the following components
Bolt is considered to be a node in a topology. Bolts help to process the input stream and produce a new stream. Bolts have the smallest processing logic. The output of one bolt can be used as input for another bolt.
Bolt is a component that takes a tuple as input, processes it and produces a new tuple or tuples as output. This implements the “IRichBolt” interface. The operations are carried out using two classes CallLog CreaterBolt and CallLog CounterBolt. The interface in bolt has the following methods.
A stream is an unordered sequence of tuples that is processed by the application. Apache storm reads raw stream data from one end and this data goes through a sequence of processing units which produces the output at the other end. Streams of data flow from spouts to bolts and from one bolt to another. The stream concept in Storm is discussed in this section with examples.
Stream grouping helps to control the route of the tuples in topology and helps to understand the workflow of the topology. There are four in-built groupings as explained in this chapter
Section 3: Installation Process
Apache Storm can be installed in your system using three steps
Installation of Java – The steps of Java installation are listed below
Installation of Zookeeper framework
Below are the steps to install the Zookeeper framework:
Installation of Apache Storm framework:
Target Audience for this course:
This course is meant for professionals who are willing to start their career in Big data analytics using the Apache Storm framework. The others include Software professionals, Data scientists, ETL developers and Project managers.
Check out the detailed breakdown of what’s inside the course
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