Big Data’s ever-growing growth in production and analytics continues to present new challenges, Apache Storm and data scientists and programmers have been using them with pride to improve their practices. Such a problem was in real-time flow. Real-time data has an extremely high value for businesses, but there is a time window after which it has lost its value – if you wish an expiry date. If the value of this real-time data does not occur in the window, the available information cannot be obtained. This real-time data comes quickly and continuously, so it is the term transfer.
This can help you stay up to date on real-time data analysis, what’s going on right now, the number of people reading your blog post or the number of people visiting your Facebook page. In practice, this is essential, although it may seem like a property to be possessed. Imagine that you are part of an advertising agency that performs real-time analysis of your ad campaigns. The customer pays heavily. Real-time analysis can inform you about how your ad performs in the market, how users respond to it and other things in this nature. If you think this way, isn’t it a very important tool?
Looking at the value of real-time data, organizations began to emerge with various real-time data analysis tools. In this article, we’ll talk about one of them – Apache Storm.
What is Apache Storm?
Apache Storm – is a distributed open source code, published by Twitter, that helps in real-time processing of data. Apache Storm works for real-time data as Hadoop works for data batch processing (Batch processing is the opposite of real time. In this case, data is divided into groups and each batch is processed. This is not actually done. -Time.)
Apache Storm does not have any state management capability and uses it to a large extent to manage Apache ZooKeeper (a central service for managing configurations in Big Data applications), cluster state – message approvals, transaction status and other similar messages. Apache Storm has designed its applications in the form of directed acyclic graphs. It is known by the processing of one million tuples per node – which provides a high degree of scalability and processing job guarantees. The storm was written in the Clojure language, the first programming language that is similar to Lisp.
In the heart of the Apache Storm, there is a key point definition to identify and present the logic graph (also known as topologies). Since Thrift can be applied in any language you choose, topologies can also be created in any language. This supports Storm in a number of languages and makes it even more developer-friendly.
The storm runs on YARN and perfectly integrates with the Hadoop ecosystem. It is a real real-time data processing framework with zero stack support. Instead of leaving data in small batches, it takes a complete data stream as an entire event.
Apache Storm: General Architecture and Key Components
Let’s take a look at the general architecture of the Storm application – we’ll give you more information about how Storm works!
There are two types of nodes in any Storm application (as shown above).
Master Node (Nimbus Service)
If you are aware of the inner workings of Hadoop, you need to know what a business follower is. This is a background that runs on Hadoop’s Main node and is responsible for distributing the task between nodes. Nimbus is a similar service for Storm. It runs on the Master Node of the storm cluster and is responsible for distributing tasks between the working nodes.
Nimbus is the Thrift service provided by Apache that allows you to send your code in your preferred programming language. This will help you write your application, especially without having to learn a new language for Storm.
As we have talked before, Storm is devoid of any state governance capability. The Nimbus service must rely on ZooKeeper to track messages sent by nodes that are running when it processes tasks. All running nodes update the task status in the ZooKeeper service to see and monitor Nimbus.
Worker Knot (Consultant Service)
These are nodes that are responsible for performing the tasks. The worker nodes in the storm operate a service called Supervisor. As its name implies, the Audit Authority supervises employee processes and helps them complete assigned tasks.
Who uses the storm?
Although a very powerful and easy-to-use tool is found in the Big Data market, the Storm finds a unique place in this list due to its ability to use any programming language you throw. Many organizations put it here to use Storm (find a comprehensive list here).
Let’s look at several big players using Apache Storm.
Twitter Since the storm has developed (which was later purchased by Apache and named Apache Storm), it seamlessly integrates with the rest of the Twitter infrastructure (Cassandra, Memcached, etc.).
Spotify is known for streaming music to more than 50 million active users and 10 million subscribers. Music recommendation provides a wide variety of real-time features, such as tracking, analysis, ad targeting, and playlist creation. To achieve this, Spotify uses Apache Storm.
Stacked with Kafka, Memcached and netty-zmtp based messaging media, Apache Storm enables Spotify to easily create low-latency defective distributed systems.
If you want to build your career as a Big Data analyst, streaming is also a way. If you specialize in dealing with real-time data, you will be the number one choice of companies hired for the role of analyst. There couldn’t be a better time to dive into real-time data analysis, because that’s the real need for the watch!