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Transformations include data cleansing, which is the process of detecting erroneous data and correcting it. Some cleansing operations are based on the data type of expected data. For example, a column of data containing only numeric data should not have alphabetic characters in the column. The cleansing process could delete rows of data that have alphabetic characters in that column. It could alternatively keep the row and substitute another value, such as a zero, or treat the value as NULL.

In other cases, business logic is applied to determine incorrect data. Some business logic rules may be simple, such as that an order date cannot be earlier than the date that the business began accepting orders. An example of a more complex rule is not allowing an order total to be greater than the credit limit assigned to a customer.

The decision to keep the row or delete it will depend on the particular use case. A set of telemetry data arriving at one-minute intervals may include an invalid value. In that case, the invalid value may be dropped without significantly affecting hour-level aggregates. A customer order that violates a business rule, however, might be kept because orders are significant business events. In this case, the order should be processed by an exception-handling process.

Transformations also include normalizing or standardizing data. For example, an application may expect phone numbers in North America to include a three-digit area code. If a phone number is missing an area code, the area code can be looked up based on the associated address. In another case, an application may expect country names specified using the International Organization for Standardization (ISO) 3166 alpha-3 country code, in which case data specifying Canada would be changed to CAN.

Cloud Dataflow is well suited to transforming both stream and batch data. Once data has been transformed, it is available for analysis.