Welcome to this third course of the data engineering specialization. In completing the previous two courses, you've already accomplished a lot. In the first course, you got a high-level overview of the field of data engineering, including the data engineering lifecycle, a framework for thinking about your work as a data engineer, and also practice building data engineering solutions. In the second course, you took a deep dive into source systems, ingestion, data ops, and orchestration. With these two first courses, you've already built up a powerful toolkit of knowledge and skills for your work as a data engineer. I'm here again with Joe Reis, your instructor for these courses. It's great to be with you again, and maybe you can say a bit about what learners will see next in this course. Sure thing. Thanks, Andrew. So the first two weeks of this course are all about data storage. Then in the third week, we'll talk about queries. At that point, I can imagine you might be wondering, a whole course on storage and queries? Is this really necessary? Believe me, while storage and queries might seem straightforward at first glance, they are anything but straightforward. Would you agree with this, Andrew? What do you think about storage and queries? I think storage and queries are not straightforward at all. There are many decisions in how to store your data that will affect how you can use the data efficiently afterward. For example, what type of storage? From long-term storage that's cheap but is practical to access only intermittently, to data storage using a relational database or a key value store, to storing it in memory, and then also the design of the database schema, and maybe even deciding which data centers, that is, in what parts of the world, you decide to store your data. All this can drive both performance as well as impact how you have to affect with regulatory concerns, such as data sovereignty. For example, I was recently just working on a project with a semiconductor company that couldn't let the data leave their country. So we had to architect a system that we wrote here in the United States that ran only in that country and shared only aggregate results with our U.S. team. And that's just one example, but the needs of different companies are so varied. This has led to a wide range of tools and frameworks that have been developed to handle these very different needs. That's right. So lots of complexity with storage and queries. So as you've seen in previous courses, you can think of data storage as a sort of hierarchy, where you have the raw ingredients of storage at the bottom, including the physical elements of storage, like magnetic disks, solid-state drives, and RAM, as well as the less tangible elements of storage, things like CPU, compression, networking, and serialization. On top of that, you have storage systems built from these raw ingredients, things like databases and object storage. And on top of that, you have storage abstractions built from these storage systems, so things like data warehouses, data lakes, and data lakehouses. Then when it comes to querying the data you have in storage, there are a wide range of tools and techniques you need to be aware of as a data engineer to ensure your systems are working efficiently. That sounds great. I'm excited about all of this that you cover in this course. And I think you'll find that in your work as a data engineer, having deep expertise in storage solutions and queries will help you be more successful at every stage of the lifecycle. Great. Let's get started.