As you've learned earlier, BigQuery is a pay for what you use pricing model. You don't pay for any setup or infrastructure. All that is managed fully behind the scenes. Now let's talk a bit more about the pricing specifics and how you can optimize your queries for costs. Okay, to understand pricing for BigQuery, we have to understand what a unit of work for BigQuery is. So BigQuery does work on the order of jobs. So a job is a task. So it can be a querying task, much like you're running a SQL query, but also it could be a loading data, exporting data. All of these things take time and resources for BigQuery to execute. Now you can do multiple jobs at once. So for example, if you had for your Google Cloud platform account, you had 20 different users, everyone's executing jobs inside of BigQuery, and maybe you have a team that's loading data in and you have ten analysts that are querying data. You could have multiple, multiple jobs that are running concurrently at the same time, asynchronously. So the jobs perform tasks, and if you needed to check back on those jobs, the history of them is stored for six months. So you can potentially repeat an import job or take a look at some of the other things that you've done. So let's talk how that relates to pricing. So four jobs, you have querying data, loading data, extracting data, as an example, to Google Cloud Storage, or copying tables. For example, if you wanted to say hey, let's mirror our production data in our development environment, we're just going to copy that table on over. So for pricing purposes, great news for you guys is that three out of the four jobs are free, so loading or adjusting data into BigQuery is free, extracting it to the Google Cloud Storage is free, and copying tables are free. So the two main components of cost for BigQuery, again, consumption-based, is your charge for the amount of data that you process. Or the bytes, like the amount of gigabytes or megabytes or terabytes that you process. And for the storage cost of storing your data permanently on disk. Now, largely you've seen we're querying these public datasets. Obviously you're not charged for public datasets. The BigQuery team kind of manages and hosts those. But in the next course, where you're actually creating your very own dataset, you'll actually be having to use these permanent tables that are stored within BigQuery. And we'll get into how much those cost in just a few slides.