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What is Apache Storm?

Apache Storm is an advanced big data processing engine that processes real-time streaming data at an unprecedented speed, which is way faster than Apache Hadoop.

What is Apache Storm?
 18th Jul, 2019
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Introduction to Apache Storm

Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner.

  • Apache Storm is able to process over a million jobs on a node in a fraction of a second.
  • It is integrated with Hadoop to harness higher throughputs.
  • It is easy to implement and can be integrated with any programming language.

Storm was developed by Nathan Marz as a back type project which was later acquired by Twitter in the year 2011. In the year 2013, Twitter made Storm public by putting it into GitHub. Storm then entered Apache Software Foundation in the same year as an incubator project, delivering high-end applications. Since then, Apache Storm is fulfilling the requirements of Big Data Analytics.

Storm vs. Spark

Along with the other projects of Apache such as Hadoop and Spark, Storm is one of the star performers in the field of data analysis. Companies can get benefitted immensely as this technology facilitates multiple applications at once. Some of the situations when you should go for Storm over Spark are mentioned in the below table:

SituationSparkStorm
Stream processingBatch processingMicro-batch processing
LatencyLatency of a few secondsLatency of milliseconds
Multi-language supportLesser language supportMultiple language support

Apache Storm Architecture

Now that you know what Apache Storm is, let’s come to its architecture. Apache Storm architecture is quite similar to that of Hadoop. However, there are certain differences which can be better understood once you get a closer look at its cluster:

  • Nodes: There are two types of nodes in the Storm cluster, similar to Hadoop, which are the master node and the worker nodes.
  • Master node: The master node of Storm runs a daemon called ‘Nimbus’, which is similar to the ‘Job Tracker’ in the Hadoop cluster. Nimbus is responsible for distributing codes, assigning tasks to machines, and monitoring their performance.
  • Worker nodes: Similar to the master node, worker nodes also run ‘Supervisors’ that are able to handle one or more worker processes on their nodes. Each supervisor handles the works assigned to it by Nimbus and starts and stops the worker processes when required. Every worker process runs a specific set of topology which consists of worker processes working around machines. Since Apache Storm does not have the ability to manage its cluster state, it depends on Apache ZooKeeper for this purpose. ZooKeeper facilitates communication between Nimbus and Supervisors with the help of message acknowledgments, processing status, etc.

Go through this short video from Intellipaat elucidating on Apache Storm:

Storm Components/Abstractions

There are basically four components which are responsible for performing tasks in Apache Storm:

  • Topology: Storm Topology can be described as a network made of spouts and bolts. It can be compared to the Map and Reduce jobs of Hadoop. Spouts are the data stream source tasks and bolts are the accrual processing tasks. Every node in the network consists of processing logics and links to demonstrate the ways in which data will pass and the processes will be executed. Each time a topology is submitted to the Storm cluster, Nimbus consults the Supervisor nodes about the worker nodes.
  • Stream: One of the basic abstractions of Storm architecture is stream which is an unbounded pipeline of tuples. A tuple is a fundamental component in the Storm cluster containing a named list of values or elements.
  • Spout: It is the entry point or the source of streams in the topology. It is responsible for getting in touch with the actual data source, receiving data continuously, transforming the data into the actual stream of tuples, and finally sending it to bolts to be processed.

  • Bolt: Bolts keep the logic required for processing. They are responsible for consuming any number of input streams, processing them, and emitting the new streams for processing. They are capable of running functions, filtering tuples, aggregating and joining streams, linking with databases, etc.

Wish to have a clearer answer to the question ‘What is Apache Storm?’ Read this Extensive Storm Tutorial!

Why use Apache Storm?

Experts in the software industry consider Storm to be the Hadoop for real-time processing. While real-time processing was becoming a much talked about topic among the BI professionals and data analysts, Apache Storm evolved with all those capabilities which were needed to fasten up the traditional processes.

What were those features which made Apache Storm suitable for real-time processing? Let’s take a look:

Storm UI REST API: Storm UI daemon provides a REST API that allows you to interact with a Storm cluster, which includes retrieving metrics data, configuring information, and managing operations such as starting or stopping topologies.

  • It processes around 1 million messages of 100 bytes on a single node.
  • It works on a ‘fail fast, auto restart’ approach.
  • Each node is processed ‘at least once or exactly once’ even if a failure occurs.

Storm is highly scalable with the ability to continue calculations in parallel at the same speed under increased load. As mentioned earlier, it is important to note that it has created a benchmark of processing 1 million messages of 100 bytes size on a single node which makes it one of the fastest technology platforms. Equipped with these features of speed and scalability, this technology outshines other existing ones when it comes to processing bulks of data at an unprecedented speed.

Moreover, Apache Storm is based on the ‘fail fast, auto restart’ approach that allows it to restart the process once a node fails without disturbing the entire operation. This feature makes Storm a fault-tolerant engine. It guarantees that each tuple will be processed ‘at least once or exactly once’, even if any of the nodes fail or a message is lost.

The standard configuration of Storm makes it fit for production instantly. Once the Storm cluster is deployed, it can be easily operated. Besides, it is a robust and user-friendly technology, making it suitable for both small- and big-sized firms.

Grab lucrative analytics jobs by cracking relevant interviews with the help of Top Apache Storm Interview Questions!

Apache Storm Job Opportunities

With massive data being produced each second, the need for processing them in real time has grown tremendously. However, some companies still wish to stick to the traditional batch-processing system and interactive workflows. Apache Storm fulfills these conventional requirements along with its additional tools and technologies which are commonly used with Hadoop.

Its scalability, high speed, and reliability have made Storm a preferred choice among the present-day Data Analysts.

  • More than 3,500 jobs are available per jobseeker, currently, which is anticipated to grow more in the near future.
  • Jobseekers are taking more interest in learning Storm due to its growing demand in the market.

Apache Storm job opportunities

There is still room for many advancements that can make Apache Storm richer with respect to driving its demand and market ahead. Some of them are listed below:

Integration with YARN: YARN’s strengths have already improvised Hadoop by implementing new services into its infrastructure. Similarly, YARN can be integrated with Storm to make it more viable for developers as, with YARN, they would be able to focus on their applications regardless of the infrastructure being implemented.

More Security Provisions: Apache Storm was initially considered as threat prone and less secure. But, it is going to cover up this loophole in the coming years with the following features:

  • Kerberos authentication with automatic credential push and renewal
  • Multi-tenant scheduling
  • Secure integration with other Hadoop projects (such as ZooKeeper, HDFS, HBase, etc.)
  • User isolation

Increased Scalability: Storm is already providing scalability to its users with less than 20 nodes to process a million jobs per second. This speed is going to increase in the forthcoming years as it is going to scale up to several thousands of nodes for real-time processing.

More Languages to be Incorporated: Apache Storm is considered to be supportive for multiple programming languages which will only increase in the future to facilitate higher productivity to its users.

Who is the right audience to learn Apache Storm?

Enrolling in an Apache Storm course is the best choice for individuals who wish to establish their career as Big Data Analysts, Software Developers, Mainframe Professionals, ETL Developers, Data Scientists, Project Managers, etc.

Any fresh graduates can enroll in Intellipaat’s comprehensive Apache Storm Training. However, having a basic knowledge of Core Java and Linux administration will give a better insight into Apache Storm.

How will Apache Storm help you in your career growth?

Apache Storm is not only leading in the software industry but is having a widespread applicability across diverse domains like telecommunication, social media platforms, weather forecasting, etc., which make it one of the most demanded technologies of present times.

The availability of jobs for Apache Storm professionals is at its peak, paying an average salary of $123,000 to an Apache Storm Developer per year. This clearly demonstrates the kind of demand Storm has worldwide.

Being one of the robust technologies with the ability to support multiple programming languages has made Storm a preferred choice among corporates. Hence, getting trained in Storm will surely help you grab better career opportunities.

Hope you liked this post. Share your queries and feedback in the comment section below!

 

 

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