Most of the systems built in organizations today are data processing systems. A typical data processing system can perform one or a combination of the following tasks:

• Conversion of data from one format to another
• Targeting of input data to appropriate storage
• Validation and clean-up of data
• Sorting of data
• Summarization of data
• Aggregation of data from multiple sources
• Statistical analysis of existing or new data
• Generating reports that list a summary or details of computed information

Data processing function apps can be used to build Nanoservices that can be aggregated to form data processing systems. Data processing function apps are always triggered by a data event. A data event is raised when state of data changes in a linked resource for example an item being added to a table, a queue, a container, and so on.

A data processing function has a set of in parameters which contain the data coming in for processing. Some of the scenarios where data processing functions are commonly used are:

• Responding to CRUD operations: Scenarios which require performing an action whenever state of data in a data store changes for example sending an email whenever a new use signs up.
• Perform CRUD operations: Scenarios in which data needs to be created or updated in another data store in response to data being added or updated in a data store.
• Moving content between data stores: Scenarios which require moving content between data stores for example moving a file to a temporary location for approval before moving it to a discoverable location. Such data transfer tasks can be carried out through a data processing Nanoservice.
• Access data across services: Scenarios which require pulling data from data stores such as blobs, queues, tables, and so on, and perform operations on them. The Nanoservice that accesses data from the stores can be integrated with other applications or Nanoservices that wish to reuse the workflow of the Nanoservice.