▶Book Description
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
▶What You Will Learn
- Explore the basics of cloud analytics and the major cloud solutions
- Learn how organizations are using cloud analytics to improve the ROI
- Explore the design considerations while adopting cloud services
- Work with the ingestion and storage tools of GCP such as Cloud Pub/Sub
- Process your data with tools such as Cloud Dataproc, BigQuery, etc
- Over 70 GCP tools to build an analytics engine for cloud analytics
- Implement machine learning and other AI techniques on GCP
▶Key Features
- Master the concept of analytics on the cloud: and how organizations are using it
- Learn the design considerations and while applying a cloud analytics solution
- Design an end-to-end analytics pipeline on the cloud
▶Who This Book Is For
This book is targeted at CIOs, CTOs, and even analytics professionals looking for various alternatives to implement their analytics pipeline on the cloud. Data professionals looking to get started with cloud-based analytics will also find this book useful. Some basic exposure to cloud platforms such as GCP will be helpful, but not mandatory.
Book focuses on major aspects of each tool - utility, architecture, use cases, pricing, and right fit. But for you to get the complete understanding of each tool we have provided links to YouTube videos which will help you with the practical aspects of the services in GCP.
▶What this book covers
- Chapter 1, Introducing Cloud Analytics, discusses the traditional way that companies prefer to build their on-premise architecture for analytics. This will majorly discuss the enterprises' approach towards the analytics engine how they handle/process/report data. It will also give an introduction to analytics and data science concepts. And the top cloud vendors who provides it. This chapter will also give a brief overview of cloud computing.
- Chapter 2, Design and Business Considerations, talks more about the design and architecture of the cloud. Before moving to the cloud, do we need to consider on-premise hardware or should we consider moving it straight away? What are the prerequisites before migrating to the cloud? And the best practices to follow for migration. Topics like these will be covered.
- Chapter 3, GCP 10,000 Feet Above –. A High-Level Understanding of GCP, explains all the analytics tools such as Datastore, BigTable, BigQuery, Cloud SQL, machine learning, IoT, Pub/Sub, and many more in detail. Here we are covering all the services in GCP and appending them with top features, pricing, use cases of all the services.
- Chapter 4, Ingestion and Storing –. Bring the Data and Capture It, dives into the major services involving ingestion and storing. We have multiple options associated with ingestion and storage. We will be discussing about eight major services which can help us with ingestion and storage. We have videos for each of the services.
There will be a few cloud use cases from the industry about the purpose of each tool.
- Chapter 5, Processing and Visualizing –. Close Encounter, Squeeze the Data and Make It Work, discusses the processing tools and machine learning APIs that are available with GCP. GCP has extensive tools for processing data. For processing, we have Cloud Dataproc (Hadoop and Spark). BigQuery, Cloud SQL, and more will be covered. We have videos for each of the services.
- Chapter 6, Machine Learning, Deep Learning, and AI on GCP, talks predominantly about artificial intelligence and machine learning. In the beginning of the chapter, we will understand what artificial intelligence is, and later, we will understand what machine learning is. We have videos for most of the services.
- Chapter 7, Guidance on Google Cloud Platform Certification, focuses mainly on GCP certification with respect to cloud architects and data engineers. Along with that, it will also have some dummy/sample questions from certification.
- Chapter 8, Business Use Cases, includes examples from multiple sectors sectors. They will help the reader get a more precise understanding of the cloud and how they are used. We have three use cases - they talk about the problem statement, different approach towards each problem, solution to each, architecture, and list of services required.
- Chapter 9, Introduction to AWS and Azure, covers the major tools in AWS and Azure about data science and analytics. Most of the tools will be closely related to data science. The aim of this chapter will be relating the GCP tools with AWS and Azure. For example, we have cloud storage in GCP, and similarly we have S3 in AWS and Blob Storage in Azure.
