The next section of the exam guide covers building data processing systems. So that includes assembling data processing from parts as well as using full services. The first area of data processing we'll look at is building and maintaining structures and databases. So not just selecting a particular database or service, but also thinking about the qualities that are provided and starting to consider how to organize the data. You can familiarize yourself with this diagram as well. BigQuery is recommended as a data warehouse. BigQuery is the default storage for tabular data. Use Cloud Bigtable if you need transactions, use Cloud Bigtable if you want low latency, high throughput. Here's some concrete advice on flexible data representation. You want the data divided up in a way that makes the most sense for your given use case. If the data is divided up too much, it creates additional work. In the example on the left, each data item is stored separately making it easy to filter on a specific field and to perform updates. In the example on the right, all the data stored in a single record, like a single string, editing and updating is difficult, filtering on a particular field would be hard. In the example on the bottom, a relation is defined between two tables. This might make it easier to manage and report on the list of locations. Acid versus base is essential data knowledge that you will want to be familiar with so that you can easily determine whether a particular data solution is compatible with the requirements identified in the case. Example, for a financial transaction, a service that provides only eventual consistency might be incompatible. Did you know that in some cases an eventually consistent solution can be made strongly consistent for a specific limited use case? In Cloud Datastore, there are only two APIs that provide a strongly consistent view for reading entity values and indexes. One, the lookup by key method and two the ancestor query.