Facts represent business activities that have already happened. Facts are similar to the notion of domain events in the sense that both describe things that happened in the past. However, contrary to domain events, there is no stylistic requirement to name facts as verbs in the past tense. Still, facts represent activities of business processes. For example, a fact table Fact_CustomerOnboardings would contain a record for each new onboarded customer and Fact_Sales a record for each committed sale. Figure 16-2 shows an example of a fact table.

Figure 16-2. A fact table containing records for cases solved by a company’s support desk

Also, similar to domain events, fact records are never deleted or modified: analytical data is append-only data: the only way to express that current data is outdated is to append a new record with the current state. Consider the fact table Fact_CaseStatus in Figure 16-3. It contains the measurements of the statuses of support requests through time. There is no explicit verb in the fact name, but the business process cap‐ tured by the fact is the process of taking care of support cases.

Figure 16-3. A fact table describing state changes during the lifecycle of a support case

Another significant difference between the OLAP and OLTP models is the granular‐ ity of the data. Operational systems require the most precise data to handle business transactions. For analytical models, aggregated data is more efficient in many use cases. For example, in the Fact_CaseStatus table shown in Figure 16-3, you can see that the snapshots are taken every 30 minutes. The data analysts working with the model decide what level of granularity will best suit their needs. Creating a fact record

for each change of the measurement—for example, each change of a case’s data— would be wasteful in some cases and even technically impossible in others.