The ever-increasing growth in the production and analytics of Big Data keeps presenting new challenges, and the data scientists and programmers gracefully take it in their stride – by constantly improving the applications developed by them. One such problem was that of real-time streaming. Real-time data holds extremely high value for businesses, but it has a time-window after which it loses its value – an expiry date, if you will. If the value of this real-time data is not realised within the window, no usable information can be extracted from it. This real-time data comes in quickly and continuously, therefore the term “Streaming”.
Analytics of this real-time data can help you stay updated on what’s happening right now, such as the number of people reading your blog post, or the number of people visiting your Facebook page. Although it might sound like just a “nice-to-have” feature, in practice, It is essential. Imagine you’re a part of an Ad Agency performing real-time analytics on your ad-campaigns – that the client paid heavily for. Real-time analytics can keep you posted on how is your Ad performing in the market, how the users are responding to it, and other things of that nature. Quite an essential tool if you think of it this way, right?
Looking at the value that real-time data holds, organisations started coming up with various real-time data analytics tools. In this article, we’ll be talking about one of those – Apache Storm. We’ll look at what it is, the architecture of a typical storm application, it’s core components (also known as abstractions), and its real life-use cases.
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#big data #data #technical skills #technology #everything you need to know about apache storm #apache storm
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The origin of the article: https://www.youtube.com/watch?v=ZtX7ZIVcXH4
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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One of the additions is that we can import dependencies dynamically with async/await. That means we do not have to import everything before, and we can import the dependencies only when we need them. As a result, the performance of the application improves by loading the modules at runtime.
How does it improve performance? With the conventional module system, we import the modules statically at the beginning of the program. Whether we need them now or later, we have to import them first. Also, all the code from an imported module is evaluated at the load time. Thus, it slows down the application unnecessarily. Why? Because it downloads the imported modules and evaluates the code of each module before executing your code.
Python has gained a lot of focus in the past few years and the reason for that is the salient features offered by python. It supports object-oriented programming, procedural programming approaches, and provides dynamic memory allocation. Let’s explore them!
First things first, Python is a high level, dynamic, and mainly it’s a free open source. Also, Python supports object-oriented programming the same as java, if not we can continue with procedural oriented programming.
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