Читать книгу Official Google Cloud Certified Professional Data Engineer Study Guide - Dan Sullivan - Страница 39

Structured data can be represented in tables of columns and rows, where columns are attributes and rows are records or entities. Table 1.1 showed an example of structured data. Structured data may be oriented to transactional processes or analytical use cases.

Transactional structured data is often operated on one row at a time. For example, a business application may look up a customer’s account information from a customer table when displaying data about the customer’s shipping address. Multiple columns from a single row will be used, so it is efficient to store all row attributes together in a data block. Retrieving a single data block will retrieve all the needed data. This is a common pattern in transactional databases such as Cloud SQL and Cloud Spanner, which use row-oriented storage.

Now consider a data warehousing example. A business analyst is working with a sales data mart and wants to understand how sales last month compare to the same period last year. The data mart has one row for each product on each date, which include the following attributes in addition to product and date: number of units sold, total revenue for units sold, average unit price, average marginal revenue, and total marginal revenue. The analyst is only interested in the monthly sums of total revenue for units sold for each product. In this case, the analyst would query many rows and only three columns. Instead of retrieving the full row for all rows selected, it is more efficient to retrieve only the date, product, and total revenue of units sold columns. This is a common pattern in analytical applications and the reason why BigQuery uses a column-oriented storage mechanism.