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

Building a Data Lake for a Telecom Client


This use case is going to focus more on the analytics engine and building a Data Lake on cloud.

Abstract

A telecom company requires a to build a Data Lake on cloud and build an analytics engine on the top of that.

Introduction

The telecom project comprises building a Data Lake as well as data migration for a 25 years old telecom company. The client is a renowned telecom company and has a good presence in Asia, Europe, Africa, and some parts of North America. Their customer base is widely spread across India, Bangladesh, African countries, Latin America, and Middle-Eastern countries. The data resides in various data sources, more than 205. The data includes customer data (even secured), customer location data, tower locations for specific geographies, customer care complaints, feedback, and many other types of data.

Data is in CSV, PSV, TEXT, MSG, audio files, XML, JSON, and even in RDBMS (MySQL, Sybase, Oracle) databases in structured format.

The first...