If you have ever done any analysis, you have probably used a table representation of data, like an Excel spreadsheet or SQL database. Additionally, if you are dealing with large numbers of documents, you might have used parquet format or JSON-like objects to represent the data.
Figure 1.1 shows table and JSON document representations of orders. For example, the table contains information about four orders of various dates and products. Table representations efficiently perform aggregations, like aggregating the total revenue or counting the number of new customers. Likewise, document structures can be great for storing vast amounts of data. In figure 1.1, a JSON object is used to store information about an online order, such as the order status, shipping address, and more. However, data analytics tools designed for tables or documents frequently overlook the relationships between data points.
