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Amazon Kinesis vs. Apache Kafka For Big Data Analysis

Dataconomy

Amazon Kinesis is a platform to build pipelines for streaming data at the scale of terabytes per hour. The post Amazon Kinesis vs. Apache Kafka For Big Data Analysis appeared first on Dataconomy. Parts of the Kinesis platform are.

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Big data engineering simplified: Exploring roles of distributed systems

Data Science Dojo

The generation and accumulation of vast amounts of data have become a defining characteristic of our world. This data, often referred to as Big Data , encompasses information from various sources, including social media interactions, online transactions, sensor data, and more. databases), semi-structured data (e.g.,

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Apache Kafka use cases: Driving innovation across diverse industries

IBM Journey to AI blog

Apache Kafka is an open-source , distributed streaming platform that allows developers to build real-time, event-driven applications. With Apache Kafka, developers can build applications that continuously use streaming data records and deliver real-time experiences to users. How does Apache Kafka work?

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Navigating the Big Data Frontier: A Guide to Efficient Handling

Women in Big Data

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline.

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How Netflix Applies Big Data Across Business Verticals: Insights and Strategies

Pickl AI

Summary: Netflix’s sophisticated Big Data infrastructure powers its content recommendation engine, personalization, and data-driven decision-making. As a pioneer in the streaming industry, Netflix utilises advanced data analytics to enhance user experience, optimise operations, and drive strategic decisions.

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Bundesliga Match Fact Ball Recovery Time: Quantifying teams’ success in pressing opponents on AWS

AWS Machine Learning Blog

How it’s implemented Positional data from an ongoing match, which is recorded at a sampling rate of 25 Hz, is utilized to determine the time taken to recover the ball. This allows for seamless communication of positional data and various outputs of Bundesliga Match Facts between containers in real time.

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Bundesliga Match Fact Keeper Efficiency: Comparing keepers’ performances objectively using machine learning on AWS

AWS Machine Learning Blog

Therefore, it’s no surprise that determining the proficiency of goalkeepers in preventing the ball from entering the net is considered one of the most difficult tasks in football data analysis. Bundesliga and AWS have collaborated to perform an in-depth examination to study the quantification of achievements of Bundesliga’s keepers.