This dataset, titled "Netflix Stock Data and Key Affiliated Companies", provides comprehensive insights into the stock performance of Netflix (NFLX) alongside several key companies that have played a significant role in Netflix's growth and operational success. These companies include major technology and media giants such as Amazon (AMZN), Intel (INTC), Warner Bros. Discovery (WBD), Sony (SONY), and others.

Dataset Overview:

The dataset includes daily stock data for Netflix and a selection of companies that contribute to its content distribution, technological infrastructure, cloud services, and content licensing. The selection of affiliated companies highlights the broad ecosystem of services and technologies that power Netflix's streaming service and its original content production.

Key Companies Included:

1. Netflix (NFLX) – The leading global streaming platform that revolutionized the way we consume media.
2. Amazon (AMZN) – Provides critical cloud infrastructure via Amazon Web Services (AWS), enabling Netflix to scale globally.
3. Intel (INTC) – Supplies advanced processors and server technologies that help Netflix handle massive data loads.
4. Akamai Technologies (AKAM) – A key content delivery network (CDN) provider that ensures fast and reliable streaming.
5. Warner Bros. Discovery (WBD), Paramount Global (PARA), and Sony (SONY) – These media companies have been essential content partners for licensing and streaming rights, fueling Netflix’s growth before and during its original content development.

Features:

• Daily stock prices for Netflix and its key partners over time.
• Open, High, Low, Close, Volume and other financial metrics for each company.
• The dataset allows users to analyze correlations between Netflix and companies that provide essential services like cloud infrastructure, content delivery, and licensed content.

Use Cases:

• Financial Analysis: Evaluate stock trends, price movements, and performance relationships among Netflix and its affiliated companies.
• Business Strategy: Understand how various partnerships and technological dependencies have influenced Netflix’s rapid growth.
• Machine Learning: Use the data for stock prediction models, time-series analysis, or risk assessments by understanding the historical volatility of these companies.

Potential Analysis:

• Stock Price Correlation: Investigate the correlation between Netflix’s stock and its key partners, such as Amazon and Intel.
• Impact of Technological Partners: Assess the importance of key tech partnerships in supporting Netflix’s streaming infrastructure and business operations.
• Influence of Media Partnerships: Examine how the stock performance of media companies like Warner Bros., Sony, and Paramount align with Netflix’s success.

Conclusion:

By analyzing the historical stock data of Netflix alongside these affiliated companies, users can gain deeper insights into how a diverse set of industries—including technology, media, and cloud infrastructure—come together to create the backbone of Netflix’s success. This dataset serves as a valuable resource for financial analysts, machine learning enthusiasts, and business strategists interested in the interconnections between these influential companies.

How to Use:

• Download the dataset, and explore the stock data for the companies mentioned.
• Perform exploratory data analysis (EDA), build models to predict stock prices, or conduct business analysis to uncover the relationships that drive Netflix’s market success.

This dataset provides a solid foundation for understanding the financial landscape surrounding Netflix and its key partners.

This is a cleaned version of a Netflix movies dataset originally used for exploratory data analysis (EDA). The dataset contains information such as:

• Title
• Release Year
• Rating
• Genre
• Votes
• Description
• Stars

Missing values have been handled using appropriate methods (mean, median, unknown), and new features like rating_level and popular have been added for deeper analysis.

The dataset is ready for: - EDA - Data visualization - Machine learning tasks - Dashboard building

Used in the accompanying notebook

Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

--- Original source retains full ownership of the source dataset ---

This version of the dataset is obsolete. It contains duplicate ratings (same user_id,book_id), as reported by Philipp Spachtholz in his illustrious notebook.

The current version has duplicates removed, and more ratings (six million), sorted by time. Book and user IDs are the same.

**It is available at https://github.com/zygmuntz/goodbooks-10k. **

There have been good datasets for movies (Netflix, Movielens) and music (Million Songs) recommendation, but not for books. That is, until now.

This dataset contains ratings for ten thousand popular books. As to the source, let's say that these ratings were found on the internet. Generally, there are 100 reviews for each book, although some have less - fewer - ratings. Ratings go from one to five.

Both book IDs and user IDs are contiguous. For books, they are 1-10000, for users, 1-53424. All users have made at least two ratings. Median number of ratings per user is 8.

There are also books marked to read by the users, book metadata (author, year, etc.) and tags.

Contents

ratings.csv contains ratings and looks like that:

book_id,user_id,rating
1,314,5
1,439,3
1,588,5
1,1169,4
1,1185,4

to_read.csv provides IDs of the books marked "to read" by each user, as user_id,book_id pairs.

books.csv has metadata for each book (goodreads IDs, authors, title, average rating, etc.).

The metadata have been extracted from goodreads XML files, available in the third version of this dataset as books_xml.tar.gz. The archive contains 10000 XML files. One of them is available as sample_book.xml. To make the download smaller, these files are absent from the current version. Download version 3 if you want them.

book_tags.csv contains tags/shelves/genres assigned by users to books. Tags in this file are represented by their IDs.

tags.csv translates tag IDs to names.

See the notebook for some basic stats of the dataset.

goodreads IDs

Each book may have many editions. goodreads_book_id and best_book_id generally point to the most popular edition of a given book, while goodreads work_id refers to the book in the abstract sense.

You can use the goodreads book and work IDs to create URLs as follows:

https://www.goodreads.com/book/show/2767052
https://www.goodreads.com/work/editions/2792775

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

Industry data revealed that Iceland had the most extensive Netflix media library worldwide as of February 2025, with over 9,700 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2025, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 22.89 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 7,900 and 8,500 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs, at around 2.87 to 3.66 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of September 2025, "KPop Demon Hunters" and "Red Notice" were the most popular English-language movies on Netflix, with over 200 million views in their first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

In the first quarter of 2025, revenues of Amazon Web Services (AWS) rose to 17 percent, a decrease from the previous three quarters. AWS is one of Amazon’s strongest revenue segments, generating over 115 billion U.S. dollars in 2024 net sales, up from 105 billion U.S. dollars in 2023. Amazon Web Services Amazon Web Services (AWS) provides on-demand cloud platforms and APIs through a pay-as-you-go-model to customers. AWS launched in 2002 providing general services and tools and produced its first cloud products in 2006. Today, more than 175 different cloud services for a variety of technologies and industries are released already. AWS ranks as one of the most popular public cloud infrastructure and platform services running applications worldwide in 2020, ahead of Microsoft Azure and Google cloud services. Cloud computing Cloud computing is essentially the delivery of online computing services to customers. As enterprises continually migrate their applications and data to the cloud instead of storing it on local machines, it becomes possible to access resources from different locations. Some of the key services of the AWS ecosystem for cloud applications include storage, database, security tools, and management tools. AWS is among the most popular cloud providers Some of the largest globally operating enterprises use AWS for their cloud services, including Netflix, BBC, and Baidu. Accordingly, AWS is one of the leading cloud providers in the global cloud market. Due to its continuously expanding portfolio of services and deepening of expertise, the company continues to be not only an important cloud service provider but also a business partner.