Book Image

Serverless Integration Design Patterns with Azure

By : Abhishek Kumar, Srinivasa Mahendrakar
Book Image

Serverless Integration Design Patterns with Azure

By: Abhishek Kumar, Srinivasa Mahendrakar

Overview of this book

With more enterprises adapting cloud-based and API-based solutions, application integration has become more relevant and significant than ever before. Parallelly, Serverless Integration has gained popularity, as it helps agile organizations to build integration solutions quickly without having to worry about infrastructure costs. With Microsoft Azure’s serverless offerings, such as Logic Apps, Azure Functions, API Management, Azure Event Grid and Service Bus, organizations can build powerful, secure, and scalable integration solutions with ease. The primary objective of this book is to help you to understand various serverless offerings included within Azure Integration Services, taking you through the basics and industry practices and patterns. This book starts by explaining the concepts of services such as Azure Functions, Logic Apps, and Service Bus with hands-on examples and use cases. After getting to grips with the basics, you will be introduced to API Management and building B2B solutions using Logic Apps Enterprise Integration Pack. This book will help readers to understand building hybrid integration solutions and touches upon Microsoft Cognitive Services and leveraging them in modern integration solutions. Industry practices and patterns are brought to light at appropriate opportunities while explaining various concepts.
Table of Contents (15 chapters)

Example 1 – The batching or aggregator pattern in Logic Apps

Batch processing is a critical requirement for most organizations. With event-based patterns and cloud consumption models, working with batch files is cost-effective and provides the end user with better insights into the business data. Logic Apps has built-in connectors for batch-processing use cases, in which the batch connector groups related messages and events in a collection until a specific criteria is met.

To understand this more clearly, let's take the example of a social media website. When we post an update on a social media site, we may get some comments. To analyze those comments, it is important to batch them up and pass them to a central repository such as a data lake for analytical purposes, or Cognitive Services for sentiment analysis.

In this example, we have used a Cosmos graph database...