Hi, I'm Lak. Welcome to the first module of our Big Data Fundamentals course. It provides an introduction to Google Cloud Platform. In this module, we'll examine the infrastructure behind Google Cloud Platform or GCP, which was originally built to power Google's own applications and is now available to you. Then we'll cover the big data and ML products that are built on top of that infrastructure and when you should choose which products for your solution architecture. After that, my favorite part of this course, learning from other customers who are using Google Cloud by exploring their use cases and getting inspired to solve similar challenges for your own teams and projects. You will learn where you can look up reference case studies of GCP customers by industry or by product, and then you'll examine their solution architecture in a short activity. Building the right team structure is critical to solving these big data challenges. We will explore the different types of roles and personas for building a successful big data team within your organization. Consider for a second, the impact that Google Search has on our daily lives with timely and relevant responses. Now think of other Google products: Gmail, Google Maps, Chrome, YouTube, Android, Play, Drive. Each of these products has over one billion monthly users. Google has had to develop the infrastructure to ingest, manage, and serve all the data from these applications, and to do so, with a growing user base and data requirements that are constantly evolving. Seven products with a billion users. Actually there's an eighth Google product that has a billion end-users, Google Cloud, except that it's your users. The end users served by Google Cloud customers such as Home Depot, Spotify, Twitter, New York Times, Colgate-Palmolive and Go Check. Let's look at the building blocks behind Google's big data infrastructure and how you can leverage it with Google Cloud. There are four fundamental aspects of Google's core infrastructure and a top layer of products and services that you will interact with most often. The base layer that covers all of Google's applications and therefore Google Cloud's too, is security. On top of that, are compute, storage, and networking. These allow you to process, store, and deliver business changing insights, data pipelines, and machine learning models. Finally, while running your big data applications on bare metal virtual machines as possible, Google has developed a top layer of big data and ML products to abstract away a lot of the hard work of managing and scaling that infrastructure for you.
