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This book offers fresh insight into a number of big data problems from a multidisciplinary standpoint. It combines practical applications like smart cars, wearables, and web tracking with legal, sociological, and economic perspectives on important big data issues like...
Data analytics uses computational techniques to generate insights. It is utilized in many industries today to help businesses and organizations make better decisions and to support or refute ideas and models that already exist.
Companies now have more customer data than ever before in the era of big data, far more than what the available technologies could possibly hope to handle.
A comprehensive manual for using Hadoop natively on Windows operating systems is provided in this book. Developers might benefit from Hadoop for Windows Succinctly, which offers both a conceptual overview and useful examples.
How can event streams improve the scalability, dependability, and maintainability of your application? This book demonstrates how stream processing may simplify and increase the flexibility of your data processing and storage systems.
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Many information sources available today are so-called data streams, arriving sequentially and quickly. These sources include sensor networks, financial markets, social networks, and healthcare monitoring.
Although Python is particularly well-suited for use with this distributed storage and processing technology, Hadoop is primarily designed in Java.
An open-source big-data processing platform called Apache Spark was created with speed, usability, and powerful analytics in mind.
In recent years, open data has sparked economic innovation, social change, and novel approaches to political and governmental accountability, but few people are familiar with how it operates.
The most widely used big data processing platform is Apache Hadoop, which may be integrated with a variety of other big data technologies to create effective analytics solutions.