How did Netflix become a data-driven company?
Netflix, like every tech company these days, is struggling to attract the best industry professionals. One way to attract the best, is to tip your hand about how tech-savvy you are. And Netflix has a lot to show. The casual user doesn't realize how much is going on underneath when they go to the platform to watch their favorite series. And there's a lot going on. So much that it would take a separate portal, not an article, to describe all the mechanisms of using Data Science with Netflix. Today we will try to introduce you to the most popular solutions that make the Netflix service dominate the streaming market worldwide.
An (un)pleasant problem
To realize the scale of the challenge Netflix engineers have to face, we'll use some data. Netflix currently has over 200 million paid subscribers worldwide. 74 million in the United States alone. The remaining subscribers are scattered around the world. The service is available in 190 countries. In 2020, one user watched an average of 3.2 hours of video per day! Each day, one subscriber downloads as much as 9.6 GB of data from Netflix's server! It is amazing how well the platform handles this demand.
Every paying user expects high image quality and platform reliability. The service for years has worked out a model of content distribution, which allows to meet such a gigantic demand. A demand that will continue to grow. In the case of such large projects it is not enough to add more servers. Netflix has been testing different approaches. It currently bases its main infrastructure on Amazon's cloud service, AWS. Each additional server costs money, and in Netflix's case, those amounts are huge, so using Amazon's services saves Netflix money and increases the reliability of its service.
Having so much data sent and received every day isn't just a cost. It's also an increasingly widely used tool to improve the service at virtually every stage. What does the most popular streaming service achieve with data? Visit https://inlookup.com
In the early days of developing the service, Netflix tried to develop its own data centers. As its popularity grew and its expansion into new markets accelerated, it decided to withdraw from this idea. The main reason was the problems with maintaining its own infrastructure and the fact that instead of focusing on its strongest point, which is delivering video content to users, it had to devote a significant portion of its resources to developing server rooms. Netflix needed a more secure solution. That's why it began migrating to Amazon's cloud in 2008, which the company finally completed in 2015. Netflix introduced something else to improve its service: Open Connect.
Open Connect is Netflix's proprietary content distribution solution, known by the acronym CDN (Content Delivery Network). Smaller server machines are scattered around the world and allow for quick access to the most popular content. Netflix's algorithms predict what specific content will be needed at a certain time and place. When the play button is clicked, video is sent to the user just from Open Connect. This cuts down on data transfer times because Open Connect's servers are closer to the user than Amazon's huge computing centers. It is important to be aware that Netflix supports thousands of devices on which a video can be shown. On top of that, there are other variables such as different picture qualities and soundtracks. Every video available on the platform must therefore be prepared in several thousand versions. All versions are stored on AWS, but Data Science decides which ones land directly in the CDN and are sent to the user. Open Connect acts as a cachce - a cache.
Of course, Netflix isn't just about streaming movies to the user. In fact, it's nearly 700 microservices (APIs) that make up the entire service. All APIs produce data that the company then uses to improve its service. You have to admit, it's an impressive path they've taken from sending DVDs through the mail.
Like any platform, Netflix also wants people to spend as much time on it as possible. The platform has a huge amount of data on what users watch and when, so it can effectively evolve its algorithms for referral systems. One of the more interesting treatments it uses is the large number of movie covers. The covers are in different styles and are meant to entice the user to watch a particular material. The system learns what kind of covers we prefer and subsequent productions are given in a style that hits our taste.
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