Six rectangular tiles organized in neat horizontal bands define most of our days .These carefully curated images change with some regularity, as to not get stale, but only give the illusion that we have a choice of TV shows and movies to watch. They’re chosen for us.
So,Netflix utilizes data to suggest shows to visitors. How? Hordes of data scientists analyze what we’re watching, when we’re watching it — to make each dashboard match the interests of its viewers. That’s why no two dashboards are alike.
This is all thanks to an abundance of data being generated by you and me. It’s no surprise that we love data so much. It inspires beautiful, carefully curated experiences for users. But with great opportunity, comes great risk.
Data can be a powerful resource if used properly, but can also be a swamp of jumbled, unintelligible information.
So how this data is managed by Netflix ,this is the topic of discussion for this article..
From relatively humble beginnings as a DVD-by-mail service, Netflix has grown into one of the most influential media streaming service in the world. The company was one of the first to see the potential of streaming technology and began to transition to a subscription video-on-demand model in 2007. Since this transition,annual revenue has grown from 1.36 billion to around 15.8 billion in just ten years. The number of Netflix subscribers has followed a similar trend, growing from less than 22 million in 2011 to nearly 150 million in 2019. The service is becoming so popular that an estimated 37 percent of the world’s internet users use Netflix.
So Data is the main form of business in today’s world,the more data you have the bigger your business is..
“Where there is data smoke, there is business fire.”
— Thomas Redman
Now let us know how Netflix uses Big Data to achieve what it does…
So as Netflix has been long enough in the streaming business, it has stacked up heaps of data about its viewers, such as their age, gender, location, their taste in media, etc.
By gathering information across every customer interaction, Netflix can dive right into the minds of its viewers and get an idea of what they might like to watch next even before they finish a show or movie.
So through the data Netflix acquires by tracking our day-to-day activities it has been able to provide us with the services and with high efficiency.
For acquiring such awesome features , Netflix has deployed several algorithms and mechanisms that make use of this data and generate critical insights that help steer the company in the right direction. Some of these tools and features are:
● Real-Time Recommendation Engine
With a sea of users, each user generates hundreds of ratings per day based on what they watch, search and add to their watch-list, this data ultimately becomes a part of Big Data. Netflix stores all of this information and using key machine learning algorithms, it builds a pattern indicating the viewer’s taste. This pattern may never match with another viewer because of how everyone’s taste is unique.
Based on the ratings, Netflix categorizes its media and suggests the viewer what the recommendation system thinks they might like to watch next.Netflix will know everything.
Netflix will know when a person stops watching it. They have all of their algorithms and will know that this person watched five minutes of a show and then stopped. They can tell by the behavior and the time of day that they are going to come back to it, based on their history.
– Mitchell Hurwitz
● Artwork & Imagery Selection
The tool behind this is called AVA, which is essentially an algorithm that selects what artworks and images to show to whom. Short for Aesthetics Visual Analysis, AVA sifts through every video available and identifies the frames that are best suitable to be used as artworks.
AVA takes a lot of metrics into consideration before finalizing on images, such as facial expressions of actors, the scene lighting, areas of interest, positioning of subjects on screen. It even categorizes and sorts artworks to show to users categorized into several taste groups.
● Production Planning
Data plays an integral part when creators come up with an idea about a new show or movie. A lot of brainstorming takes place before anything gets on the paper, and that’s where data comes in.
With prior experience in creating new and original content and loads of data about how the viewers perceived the previous content, Big Data helps bring out the possible solutions to many of the challenges faced during the planning phase.
These challenges could include identifying shoot locations, time and day of the shoot, and more. Even with simple prediction models, Netflix can save a significant amount of effort put into planning, further reducing expenses.
Netflix is commissioning original content because it knows what people want before they do.
– The New York Times
● Metaflow
Netflix has open-sourced Metaflow, their cloud native, human-centric framework aimed at boosting data scientist productivity
The idea behind Metaflow was to shift the focus of data scientists from worrying about the infrastructure of models to solving problems. Metaflow allowed them the freedom to experiment with their ideas by offering a set of fine-tuned features that almost makes Metaflow feel like a plug-and-play framework. A few noteworthy features of Metaflow are:
● Ability to work on a distributed computing platform
● Option to snapshot code and data for versioning and experimenting
● High-speed and high-performance S3 client
● Support for most machine learning frameworks
Metaflow — A simple Python library(source)
● Polynote
Developed and open-sourced by Netflix, Polynote is a polyglot notebook with support for Scala and various other features. Polynote allows smooth integration of JVM based machine learning platform with Python to data scientists and machine learning researchers. A few highlights of this notebook are:
● Provides insights into kernel status and tasks in execution
● Offers simplistic dependency and configuration management
● Provides IDE-like features such as auto-complete, error highlights, reproducibility, editing, improvements, visibility, data visualization and many more.
● Metacat
The vast pool of data that Netflix operates on is spread across multiple platforms such as Amazon S3, Druid, Redshift and MySQL, to name a few. To maintain seamless interoperability among these data stores, Netflix needed a service.
This need for simplicity gave birth to Metacat, whose sole purpose was to provide centralized metadata access for all data stores. Netflix created Metaflow with the intent of serving the following core objectives:
● To unify and provide centralized views of metadata systems
● To offer a singular API for datasets metadata for platforms
● To provide a solution for business and user metadata storage of datasets
● Druid
“Apache Druid is a high performance real-time analytics database. It’s designed for workflows where fast queries and ingest really matter. Druid excels at instant data visibility, ad-hoc queries, operational analytics, and handling high concurrency.”
— druid.io
Netflix uses Apache Druid for ensuring that its users get a high-quality user experience every time. Delivering a top-notch user experience every time is not a simple feat. It requires constant analysis of several events, gathering the necessary data and analyzing it. This data could be anything from the playback information, to device information, to measuring platform performance and several others. All these event metrics make raw data complicated, and that’s where Druid comes into play.
Druid’s task is to provide real-time analytics on databases where queries execute regularly and at uncertain time-periods. It is highly scalable and offers excellent performance for any given workload.
● Use of Python
Netflix loves Python because of how powerful it is and how excellent it gets when paired with libraries, not to mention how smoothly it integrates with other platforms. Netflix uses Python for managing a host of its mission-critical aspects such as:
● Applications managing the CDN infrastructure
● Analyzing operational data, traffic distribution and operating efficiency
● Prototyping visualization tools
● Gaining insights via statistical tools, data exploration and cleaning
● For maintaining information security
● Managing several core tasks using Jupyter notebooks
● For experimentation using A/B tests
Conclusion
Big Data plays a critical role in not just deciding the functioning of Netflix but also presents them with newer opportunities to grow. New technologies often bring their fair share of issues with them, but at Netflix, they have been tackling those issues head-on, consistently by taking community inputs. By open-sourcing several of the libraries and frameworks to the community, Netflix aims to improve not just itself, but other companies as well. In the end, it would be incorrect to say that Netflix takes all its decisions based on Big Data insights as they still rely on human inputs from a lot of people.
Using big data, Netflix saves $1 billion per year on customer retention.
Today, many companies use big data to expand and enhance their businesses, and one of the best video streaming services — Netflix, is a perfect example of that. The digital users’ favorite streaming service, Netflix had 163.5 million subscribers as of October 2019. Now, the California-based company can help us answer the question: what are the benefits of big data? Well, one of the benefits of using big data in streaming services is customer retention as a result of lower subscription cancelation rates. Netflix has a strategy to tie its audience to their seats, and big data is a big part of that strategy.
Some of the information Netflix collects includes searches, ratings, re-watched programs, and so on. This data helps Netflix provide its users with personalized recommendations, show videos similar to the ones they’ve already watched, or suggest various titles from a specific genre. Plus, we have to admit that the company’s “Continue Watching” feature improves the user experience a lot.
While going through various big data statistics, we discovered that back in 2009 Netflix invested $1 million in enhancing its recommendation algorithm. What’s even more interesting is that the company’s budget for technology and development stood at $651 million in 2015. In 2018, the budget reached $1.3 billion.
As for the $1 billion in savings from customer retention, this was just a rough estimate Carlos Uribe-Gomez and Neil Hunt made in 2016. We believe that number is significantly higher now, as, among other reasons, Netflix spent over $12 billion on content in 2018, and that number reached $17 billion in 2020.
So as we now know how Netflix manages its data ,you are left with one task that is like and share this blog …
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