Book Image

Cloud Analytics with Google Cloud Platform

By : Sanket Thodge
Book Image

Cloud Analytics with Google Cloud Platform

By: Sanket Thodge

Overview of this book

With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation
Table of Contents (16 chapters)
Title Page
Packt Upsell
Foreword
Contributors
Preface
Index

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...