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Mastering Hadoop 3

You're reading from   Mastering Hadoop 3 Big data processing at scale to unlock unique business insights

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Length 544 pages
Edition 1st Edition
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Authors (3):
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Timothy Wong Timothy Wong
Author Profile Icon Timothy Wong
Timothy Wong
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
Chanchal Singh Chanchal Singh
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Chanchal Singh
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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Hadoop 3 FREE CHAPTER
2. Journey to Hadoop 3 3. Deep Dive into the Hadoop Distributed File System 4. YARN Resource Management in Hadoop 5. Internals of MapReduce 6. Section 2: Hadoop Ecosystem
7. SQL on Hadoop 8. Real-Time Processing Engines 9. Widely Used Hadoop Ecosystem Components 10. Section 3: Hadoop in the Real World
11. Designing Applications in Hadoop 12. Real-Time Stream Processing in Hadoop 13. Machine Learning in Hadoop 14. Hadoop in the Cloud 15. Hadoop Cluster Profiling 16. Section 4: Securing Hadoop
17. Who Can Do What in Hadoop 18. Network and Data Security 19. Monitoring Hadoop 20. Other Books You May Enjoy

Presto – introduction

The growing popularity of big data use cases has bought many new technologies and frameworks each of them comes with scalability, high throughput, and low latency in mind. Some companies have very large data warehouses storing hundreds of petabytes of data, and the data is used for various applications such as machine learning, batch analytics, and more. The data is used by technical engineering teams to get insights into businesses, which helps improve the product or services and yields new opportunities to generate more revenue for companies. 

The performance of data warehouses plays an important role, as fast results will always help in quicker decision making. Data warehouses should have the ability to run queries in parallel and give results in less time to help businesses increase their productivity and profitability. It is also important...

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