Microsoft Azure Reviews

My experience primarily revolves around modernization, IT transformation, and infrastructure as a service in the cloud, along with managed services, cloud service provider strategies, designing cloud services, and monetization strategies.

So, I'm mainly focused on consulting for customers. However, I also use it personally. 

My customers integrate Microsoft Azure into their existing IT infrastructure. They adopt a hybrid approach, and they tend to move as much as possible to the cloud. It's a transition to fully cloud-based solutions, dealing with legacy systems through temporary hybrid solutions until everything can be migrated.

Projects often leverage Azure for data handling, using Microsoft's data platform, Power BI, and Azure Synapse Analytics. Innovations with Azure are increasingly driven by integrations with OpenAI and tools like ChatGPT, enabling rapid project initiation and delivering substantial results in artificial intelligence applications.

It's very exhaustive and very comprehensive. Regarding the security features, the security stack of Microsoft has made a lot of improvements in the last two years. We really appreciate the offerings, particularly what they have done with Microsoft Sentinel, a platform that helps manage what is happening within your organization, which is very good. 

An improvement could be a version that is more adapted for small companies. Microsoft Sentinel is great for large companies but less so for smaller ones. I also appreciate what they have done with Microsoft Purview. The cybersecurity stack of Microsoft is very good.

Microsoft Azure is a ticketing system that provides support services to Microsoft partners based on their partnership tier. Partners can raise support tickets, particularly for production-related issues. When we had issues with our Cosmos DB instance in production, we would submit a support ticket with screenshots detailing the error. We usually receive a call from a support engineer the same day.

Once a support engineer was assigned, the process was well-organized. Each engineer worked shifts of about eight to nine hours, and before their shift ended, they would email us to let us know who would be taking over, for example, "John Doe." They would also provide a summary of the issue, progress made, and next steps, ensuring a smooth handover without the need to re-explain the problem to the next engineer. It was a very efficient process.

Microsoft Fabric is a new service that integrates various functionalities into one platform, such as data engineering, data science, and data visualization. While promising, some complexities and limitations become apparent only after you use the service.

Fabric is a comprehensive and expensive service with tier-based pricing. For someone new to the platform, like those coming from Snowflake or Databricks, it can be difficult to grasp the cost implications and potential limitations without extensive exploration. The documentation outlines these limitations.

We host all of our production workloads in Azure, including all environments like UAT, development, and production. 

All our mission-critical workloads run in Azure, and that includes Azure Kubernetes Service (AKS), Azure API Management (APIM), Application Gateway, Load Balancer, and a full suite of security products, including Azure Sentinel, virtual machines, Azure Monitor, and ExpressRoute for private network connectivity.

Additionally, we've recently begun using Azure Synapse for data warehousing, and our SaaS solutions include Office 365 and Dynamics 365.

We use so many services, and it's constantly evolving, so there are many features I like. 

For example, Azure DevOps is good for deployment.

Additionally, different Azure products integrate well to create unified security solutions.

I really like the security features in Azure. Defender integrates everything, so I can handle all security posture management tasks within a unified solution. It outperforms SIEM, CIEM, and even Security Access Broker solutions. It's a complete package with unified support within Azure.

For compliance, we follow specific industry standards and government regulations. Azure provides a strong baseline, making compliance maintenance easier.

Our primary use of the platform involves a variety of architecture frameworks, infrastructure, and system needs based on customer requirements. We use it for many AI projects, cognitive services, and infrastructure management.

The solution's most valuable features include scalability, stability, and comprehensive security tools. Its AI capabilities, including generative AI and tools like Azure GitHub Copilot, have been particularly beneficial in optimizing our operations and enhancing critical processes.

Microsoft Azure is a very dynamic cloud service that allows you to migrate your workload, applications, and data. We can use cloud services to run those environments or transform those applications into better services with high availability and resilience. It has multiple zones and multiple data centers.

When running any service on the cloud, the best advantage is that you can scale up and scale down. You can scale up your environment if there's a high-selling season for your application and services. Once that season is over, you can downscale and run operations.

The customer support can become pricey at times if you don't have proper plans.

Microsoft Azure provides many critical features, such as business continuity and disaster recovery, which come embedded out of the box. From my point of view, managing the whole infrastructure, these features have been very beneficial. The support is also amazing, with someone allocated to assist within a few minutes.

At the moment, we've found Azure Container Apps to be most beneficial for scalability. Previously, our resources were hosted on dedicated servers, which were tricky to scale automatically. With Azure Container Apps, which work like Kubernetes with additional features, scalability is built-in. This allows us to scale to almost zero when not in use, saving costs. Additionally, consumption-based features ensure we only pay when there's demand, and it auto-scales accordingly.

However, we've had issues with Databricks auto-scaling, where it sometimes spun up more clusters than needed, incurring extra costs. Azure has resolved these issues based on our feedback. As we often adopt new technologies early, we encounter some immaturity in features, but overall, for our application needs, we're quite satisfied. I haven't used AWS or other cloud providers, so I can't compare costs.

People use it for various reasons in my organization. Data engineers use it to target databases for data ingestion. 

As a data scientist, I primarily use it for modeling and analytics purposes. 

Databricks is extensively used for these purposes and also for deploying models into production. It's used across the entire pipeline in my division.

Azure offers broad compatibility with both structured and unstructured data. For example, we use PostgreSQL for storing Azure's official data and manage various types of data, including tabular and image data, accommodating the storage of all data types we handle. So, in many ways, Azure simplified the data storage and management needs.

However, we don't use  Azure for all our web hosting requirements. For example, for production and web hosting, we use a combination of Azure and other native languages and hosting services. So, it's not entirely dependent on Azure.