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

Professional Data Engineer Certification


Up next is the list of services that are typically a must-know for a Professional Data Engineer.

Topics for Cloud Data Engineer Certification

The following are the list of important topics required for the certification:

  • BigQuery
  • Dataflow
  • Dataproc
  • Machine Learning API
  • TensorFlow
  • Stream Pipeline
  • Streaming Analytics and Dashboards

Let’s discuss all of them in more detail.

BigQuery

BigQuery, as we know by now, is a serverless SQL data analysis tool on petabyte-scale data. Have some very good hands-on experience on the service and study different use cases. Learn how BigQuery works and the features it supports: serverlessness, SQL-like queries, wildcards, loading data (using a CLI, web UI, or API).

You can also have a user-defined function and the different constraints it has. Learn a few best practices such as stopping projecting unnecessary columns, filtering often with the where cause, and many others.

Dataflow

As you already know by now, Dataflow is about autoscaling...