Know How Data Science is Making Netflix Succeed!

Posted by rohit rohi on August 23rd, 2022

Many people have found refuge in services like Netflix, Hulu, Amazon Prime, and others during the pandemic. Indeed, how else are you supposed to pass the time being caged up in one location for an extended period of time? If you want to stay sane while being cooped inside, you can read, play video games, zoom, or binge-watch another season of Stranger Things.

Furthermore, it is not surprising that TV viewing and time spent on popular streaming services increased significantly during the lockdown. It appears that Netflix, Apple TV, and others will continue to gain popularity. But how do they captivate audiences and ensure that the proper products are delivered to them? The solution is data science. A great example of the potential of AI and how {: gap {:kind:userinput}} can use it to your audience's advantage is Netflix. 

Let's look at how Netflix handles it, one of the biggest providers of downstream traffic on the internet.

Development of Netflix's Data Science and Recommendation Engine

The only information Netflix could examine in the late 1990s, when it was just getting started as a DVD sales and rental business, was the titles of the movies and TV shows that their customers had ordered, the programs and movies that were on their DVD queues, and movie star ratings from 1 to 5. However, it was insufficient. For this reason, Netflix held a public competition in 2007–2009 with a million cash prize to enhance its current five-star rating system for recommendations.

Netflix's prediction algorithm was improved by 10.05% thanks to BellKor's Pragmatic Chaos team. That was a significant development for the business that would help it significantly improve its streaming service and the world at large.

In 2012, Netflix began creating its original content, producing incredible television programs and motion pictures such as War Machine, Narcos, House of Cards, Orange is the New Black, and many others. In 2016, the business became global, enabling customers from all over the world to subscribe to Netflix.

The ability of Netflix's recommendation engine to tailor content to users' likes and requirements using information they've gathered over many years and from a variety of viewers has helped it become the most widely used streaming service in the world today. Not only can you see what devices and locations they are using to view a show, but you can also quickly determine how much time they spend using the streaming service, what kinds of material they like, and what they are likely to select next. Additionally, the business makes use of both behavioral and demographic data. (Refer the data science course to learn more.)

Starting with your preferred device—from a smart TV to an Xbox or PlayStation, to your homepage—you get a tailored user experience. The main screen may display customized visuals of TV series and movies as well as suggested shows, which is basically a collection of several algorithms. For instance, since Netflix algorithms will present the finest selections, you'll see personalized rows with suggested TV programs. Additionally, the suggestion system caters to not only your tastes but also the tastes of everyone in your home, giving each streaming service a unique experience.

What Kind of Big Data Does Netflix Use?

Currently, millions of customers from several nations are streaming Netflix. Additionally, it indicates that the business will be receiving a growing number of data clusters that must be stored, processed, and leveraged to provide outstanding outcomes. The streaming service supplier employs various information, including user ratings (several billion statistics), information from social media, search phrases, metadata, video queue data, evaluations from critics, box office performance, demographics, localities, and languages, to name a few. 

Here are the primary technologies that Netflix utilizes to manage big data, and Netflix has fully transitioned to AWS Cloud to handle that data.

• To improve scalability, availability, and performance, data can be stored using Amazon S3, also known as Amazon Simple Storage Service.

• A distributed stream-processing system is Apache Kafka.

• The most popular data warehouse is Apache Hive.

• An analytics engine for massive data processing is Apache Spark.

Along with many other technologies, Netflix also makes use of Python, R, Tableau, Sting, Presto, Pandas, and TensorFlow. And Netflix employs big data in this manner. To learn these skills and become a data scientist, Learnbay has the best data science course in Mumbai. It offers flexible data science training along with hands-on projects.