Hi, I'm Lak and I lead a team that helps customers of Google Cloud successfully build applications that use our big data and machine learning products. Among the things we do is to create big data and machine learning training courses and labs; like this course, Big Data and Machine Learning Fundamentals with Google Cloud Platform. This course was designed to showcase real-world data and ML challenges and give you practical hands-on expertise in solving those challenges using Google Cloud. It's a critical course to master because it covers the most common use cases you and your team will encounter on your big data journey. This course is divided into six content modules. In the first module, which is on data-driven decision-making, you will learn all about the data and ML tools available on GCP from a high-level organizational perspective. Then in the next four modules, you will be introduced to Google Cloud products in context as they are employed to solve real-world problems. In the four modules, in addition to expanding your knowledge about our products and platform, you will also get to practice with hands-on Qwiklabs. Finally, in the summary module, we'll do a recap of everything you've learned in this course and provide you with additional resources on the topics that you've done. In each module, this is the typical order we'll follow. First, we'll have lectures from subject matter experts, where we'll introduce the big data scenario and the associated challenges and how to address them with Cloud technologies. Next, you'll see a demo of the solution and action which will highlight key features that you will learn and practice in your labs. After you understand the scenarios and have watched the demos, it's time for you to practice with Qwiklabs in a real Google Cloud Platform account. Finally, you will explore real customer use cases and architectures, so you can familiarize yourself with best practices and get inspired in your own solutions. In other words, we describe a common class of big data and ML problems and hone in on one specific problem and then we show you a demo of a solution to that problem. Then we talk about why we built a solution that way, using that opportunity to cover how and when to use the various products used in the solution. Finally, we widen the lens and show you real-world applications that are variants of the principles covered in the chapter. You're already taking this course which means you recognize the importance of big data processing. But why is this skill set in such high demand? According to McKinsey research, by 2020, we'll have 50 billion devices connected in the Internet of Things. These devices will cause the supply of data to double every two years. Unfortunately though, only about one percent of the data generated today is actually analyzed according to McKinsey. This state of affairs provides a wide open opportunity because there's a lot of value in data. I believe that the ability to build applications that handle large amounts of data and derive insights from that data in an automated manner. This ability is a skill that will be well rewarded in the marketplace. Individuals who have this skill will have many opportunities open to them and companies that develop this skill will become more successful. So the opportunity for data analysts, data scientists, and data engineers we'll talk about what these roles are and what the differences are. The opportunity for all three of these roles is very clear. At its core, this course is primarily geared towards data engineers. That said, if you're an analyst, an ML engineer, or a tech lead for your team, it's a valuable skill to know how all the big data and ML products interact to solve some of the most common challenges that data engineers face. What are those challenges? Those challenges are migrating your existing big data workloads to an environment where you can effectively analyze all of your data, interactively analyzing large and by large I mean terabytes to petabytes; analyzing large datasets of historical data. Third, building scalable pipelines that can handle streaming data, so that your business can make data-driven decisions more quickly. Finally, building machine learning models so that you're not just reacting to data, you're able to make predictive forward-looking actions using your data.
