In recent years, businesses innovate and migrate at a faster pace by using Microsoft Azure cloud-native technologies. Azure Integration Services, an industry leading Integration Platform as a Service (iPaas) provides multiple offerings like Logic Apps, Service Bus, API Management, Event Grid, Azure Functions & Data Factory to meet application integration scenarios. Azure Logic Apps gaining good interest with more than 40,000 customers using it.

In business-to-business (B2B) solutions and seamlessly creating communication between organizations, establishing a standard format to create communication across different services and enabling the security trust fact across various services is the most challenging parameter to achieve. But today, you can build automated, scalable enterprise integration workflows by using the Enterprise Integration Pack (EIP) with Azure Logic Apps at ease.

In the fast-growing Azure world, the significant need for organisations would be productivity. Any enterprise that invests in a tool for their serverless needs aims for optimised management, which boosts productivity. This article will explain how to increase productivity while having a critical resource like Azure Storage, which is essential for organisations with their business developed in Azure. For an effective performance for such substantial resources, optimised management is mandatory.

For the people using Azure Service Bus namespaces – we often have the need to ensure the Azure Service Bus Performance by testing our system integration by generating some test messages on the Azure Service Bus resources. You might need this for QA/Development for performance testing, load testing etc. This blog will explain how to simulate the test environment using Serverless360 to check Azure Service Bus performance and its throughput.

Towards the end of 2016, it all started with developing a simple platform to manage Microsoft Azure Service Bus namespaces. The then classic Azure portal had limited capabilities to manage Azure Messaging resources like Service Bus Queues and Topics. Paolo Salvatori developed and managed a community tool called Service Bus Explorer. We identified that there are some challenges or limitations in managing and monitoring Azure Messaging resources using the above two.

Azure Event Hubs are cloud-scale telemetry ingestion from websites, apps, and devices. Because of the tremendous event handling capacities, IoT architectures also consume the Azure Event Hubs. Thus, we talk about handling millions of events per second. With the implementation of multiple partition architecture behind the scenes, Azure Event Hubs are highly scalable to receive events from hundreds of sources.

2020 was indeed challenging for Kovai.co still had great learning! One of them is what I am sharing with the community through this blog. For enterprises like us, worldwide work from anywhere became the new reality. We had to move towards a cloud-native world where secure data storage and effective remote management of IT infrastructure were of supreme importance.

Today’s applications are expected to be highly responsive and always online. Such circumstances excite the pressure on applications to respond in real-time to large changes in usage at peak hours, store ever-increasing volumes of data and make it available in milliseconds. Azure Database Services comes to rescue in such situations, and Azure supports many industry-standard databases and APIs.

Over the past few years, Microsoft Azure has seen tremendous growth, and many new enhancements have improved user productivity. To keep users well informed about their resource status and availability, Microsoft has also been actively working on various monitoring capabilities.

Data sources ingest data in different sizes and shapes across on-premises and in the cloud, including product data, historical customer behaviour data, and user data. Enterprise could store these data in data storage services like Azure Blob store, an on-premises SQL Server, Azure SQL Database, and many more. This blog will highlight how users can define pipelines to migrate the unstructured data from different data stores to structured data using the Azure ETL tool, Azure Data Factory.