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May 19, 2021

How Netflix Uses Big Data | Data Science Daily | Episode 26

Do you know?

Despite so much competition, Netflix still has the largest subscriber count in 2020

The company is older than most of us know as it started as a DVD rental platform

41% of Netflix, users are watching without paying, thanks to account and password sharing

Netflix is one of the oldest streaming services available as an app on various devices

With a company that is brimming with data, it’s generally a wise choice to put that pile of data to good use. By incorporating principles like machine learningdata analysisstatistics and deep learning. So let's jump on to How Netflix Uses Big Data

With a tremendous user base of more than 140 million subscribers. Some data that Netflix records to give an individual taste to everyone are What day you watch content, What time you watch content, The device on which the content was watched, How the nature of the content, Searches on the platform, Portions of content that got re-watched, Whether content was paused, rewind, or fast forward, User location data, When you leave content, The ratings given by the users, Browsing and scrolling behavior

With time, Netflix has deployed certain algorithms which utilize this data and generate critical insights that help steer the organization in the correct direction. Some of these tools and features are:

● Real-Time Recommendation Engine

Netflix stores all of the data and using machine-learning algorithms, develops a unique pattern indicating the viewer’s choice. This style would never match with another viewer because everyone’s taste is unique.

● Artwork & Imagery Selection

The tool used for this is called AVA, which is an algorithm that selects what artworks and images to show to whom. 

 ● Production Planning

Data plays a pivotal role when developers come up with an idea about any new movie or show. A lot of brainstorming happens before anything gets finalized, and that’s exactly where data comes in. Big Data helps draw out the possible solutions to many of the problems encountered during the planning stage.

● Metaflow

Metaflow provided data scientists the flexibility to play around with their ideas by offering a set of fine-tuned features that nearly make Metaflow feel like a plug-and-play framework. Ease of work on a distributed computing platform, Option to snapshot code as well as data for versioning and experimenting, high-performance and High-speed S3 client, Support for most machine learning frameworks are a few features of meta flow.

● Polynote

Polynote is a polyglot notebook with the help for Scala and various other features Developed and open-sourced by Netflix. Polynote enables smooth integration of JVM based machine-learning platform with Python to machine learning researchers and data scientists. 

● Metacat

The great pool of data which Netflix operates on is spread across multiple platforms like Druid, Amazon S3Redshift, and MySql, etc. Netflix created Metaflow with the intent of serving objectives like to unify and provide centralized views of metadata systems, to offer a singular API for dataset metadata for platforms, to give a solution for business and user metadata storage of datasets

● Druid

Netflix utilizes Apache Druid to ensure that its users get a high-quality user experience each time. Druid’s job is to offer real-time analytics on databases where queries execute at uncertain time periods and frequently. It is extremely scalable and offers great performance for any given workload.

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