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Cloud Analytics with Google Cloud Platform

You're reading from   Cloud Analytics with Google Cloud Platform An end-to-end guide to processing and analyzing big data using Google Cloud Platform

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788839686
Length 282 pages
Edition 1st Edition
Concepts
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Author (1):
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Sanket Thodge Sanket Thodge
Author Profile Icon Sanket Thodge
Sanket Thodge
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Table of Contents (11) Chapters Close

Preface 1. Introducing Cloud Analytics FREE CHAPTER 2. Design and Business Considerations 3. GCP 10,000 Feet Above – A High-Level Understanding of GCP 4. Ingestion and Storing – Bring the Data and Capture It 5. Processing and Visualizing – Close Encounter 6. Machine Learning, Deep Learning, and AI on GCP 7. Guidance on Google Cloud Platform Certification 8. Business Use Cases 9. Introduction to AWS and Azure 10. Other Books You May Enjoy

Google Cloud Datalab


Google recently has launched a tool for data analysis and performing data operations using cloud, named Cloud Datalab. It is a perfect integration of the IPython Jupyter notebook system with Google's BigQuery data warehouse, along with many more nice features of Datalab. It also integrates standard Python libraries, such as graphics and scikit-learn, and Google's own machine learning toolkit TensorFlow.

Cloud Datalab is executed on a VM instance, which is packaged as a container. We can establish a connection from our browser to the Cloud Datalab container. Thus we can open existing Cloud Datalab notebooks and create the new notebooks. Notebooks are used by Cloud Datalab to store and write the code and not the plaintext files. Notebooks integrate the code together and the documentation is written as markdown, along with the results of code execution, whether it could be a text, image, or HTML/JavaScript, we can use Notebooks very similar to we writing a code in editor...

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