Book Image

Implementing Azure: Putting Modern DevOps to Use

By : Florian Klaffenbach, Oliver Michalski, Markus Klein, Mohamed Waly, Namit Tanasseri, Rahul Rai
Book Image

Implementing Azure: Putting Modern DevOps to Use

By: Florian Klaffenbach, Oliver Michalski, Markus Klein, Mohamed Waly, Namit Tanasseri, Rahul Rai

Overview of this book

This Learning Path helps you understand microservices architecture and leverage various services of Microsoft Azure Service Fabric to build, deploy, and maintain highly scalable enterprise-grade applications. You will learn to select an appropriate Azure backend structure for your solutions and work with its toolkit and managed apps to share your solutions with its service catalog. As you progress through the Learning Path, you will study Azure Cloud Services, Azure-managed Kubernetes, and Azure Container Services deployment techniques. To apply all that you’ve understood, you will build an end-to-end Azure system in scalable, decoupled tiers for an industrial bakery with three business domains. Toward the end of this Learning Path, you will build another scalable architecture using Azure Service Bus topics to send orders between decoupled business domains with scalable worker roles processing these orders. By the end of this Learning Path, you will be comfortable in using development, deployment, and maintenance processes to build robust cloud solutions on Azure. This Learning Path includes content from the following Packt products: • Learn Microsoft Azure by Mohamed Wali • Implementing Azure Solutions - Second Edition by Florian Klaffenbach, Oliver Michalski, Markus Klein • Microservices with Azure by Namit Tanasseri and Rahul Rai
Table of Contents (29 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Data processing function apps


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...