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.
