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

Cloud Dataflow


Cloud Dataflow is one of the first services we are going to learn in this chapter. It is a fully managed service that transforms data in the streams and batches while providing equal reliability.

You can develop a simplified and fast pipeline in Cloud Dataflow. We can express Cloud Dataflow in Java and the Python API in the Apache Beam SDK.

When to use

We have multiple uses of the Cloud Dataflow service in Google Cloud Platform.

We can use it for clickstream, point-of-sale, and segmentation analysis in retail. Cloud Dataflow can help the company getting the data to analyze it on the go and take important business decisions.

Another use case can be fraud detection in financial services. We can have Cloud Pub/Sub helping in gathering data from multiple data sources, which can increase the efficiency of fraud detection and accuracy.

You can also use Dataflow to provide personalized user experience in gaming to millions of users depending on their behavior.

IoT also generates huge data...