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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.

Pricing is very expensive.

I have been using Microsoft Azure for five years.

Cosmos DB offered better scalability. In Azure SQL, we had to carefully manage the storage tier, switching back and forth based on needs. But when we first implemented Cosmos DB, we had a good understanding of the required data volume over the past three months. This helped us select the appropriate tier because we had those figures.

If we had done it the other way around, it would have been much harder to estimate the data accurately. While you can get a rough idea, whether it works out in practice is more of a hit-and-miss approach, which we have already experienced with Cosmos DB. We applied the knowledge gained from that exercise to build a similar solution over Azure SQL.

On the DevOps side, our team consisted of five members, ranging from senior to junior roles. We would raise requests for specific services needed in the development environment. The process typically takes three to five business days, including all necessary approvals, from submitting the request to receiving the resources and verifying access.

Sometimes, even with the documentation available, understanding how it applies to our specific issues requires contacting a system engineer and creating a support ticket. The support engineer often provided additional details that were not included in the official documentation. This highlights the gap between the provided information and practical, real-world use cases.

Positive

Deployment was relatively straightforward. We didn’t encounter any issues whether we were using the GUI mode or working with our DevOps team, who utilized Terraform to provision resources as needed.

Most people use Azure Synapse for data onboarding and integration. Synapse is a big data analytics platform where data engineers and scientists can collaborate on a unified platform. Another service called API Management serves data outside Azure to downstream consumers or third parties. This is useful for cases where third parties can't be onboarded onto your system due to compliance or regulatory issues or need a specific slice of your data in near real-time. When we used Cosmos DB, we hosted near real-time data with a latency of about 15 minutes and served it via API M. With API M, authentication tokens were shared with the end users, allowing us to know precisely who was consuming data outside of our Azure environment. This setup was unique because it required providing near real-time information with minimal latency, even as the number of concurrent users increased. Cosmos DB worked well in this scenario due to its horizontal scalability. We also hosted similar data on Azure SQL, as Cosmos DB can be more expensive for cases where a smaller amount of data needed to be served, such as just the last one to three months of data. Azure SQL was a good alternative. The same API M interface was used to serve data from Cosmos DB and Azure SQL. Under the hood, one API would fetch data from Cosmos DB, and another would pull from Azure SQL. This architecture was tested and worked well for our client.

It was easy to maintain. Overall, I rate the solution an eight out of ten,

Generally, my use case is for front-end deployment itself, nothing more than that. In my professional career, I have used it for front-end as well as back-end deployment, not the back-end infrastructure, but communicating with the back-end and whole SQL queries and things of that nature. My main objective is to deploy a front-end where a person can see and download the files or navigate to other links. Most of the back-end is not deployed there. If possible, analytics would be beneficial for me.

As far as the use case, my general purpose is deploying the front-end. The process is easier, but the setup process can sometimes be lengthy if you're deploying and using the back-end itself for other things. Connecting the back-end to the front-end is sometimes difficult. Additionally, when you set up a custom domain, there are also some issues when I have to deploy a custom domain with Netlify Platform. It will take quite a lot of processing and is not very easy.

The feature I appreciate most is that I can host anything for free there and it doesn't have any cost up to a certain point. Netlify Platform has many features for deployment that are very easy to use. You can drag and drop the files and deploy them there. Beyond that, the back-end compatibility with other projects is also very good, including compatibility with AWS. These are the aspects I value about Netlify Platform.

Connecting the back-end to the front-end is sometimes difficult. Additionally, when you set up a custom domain, there are some issues when I have to deploy a custom domain with Netlify Platform. It will take quite a lot of processing and is not very straightforward.

I have been using Netlify Platform for one year in my career, as a professional for nine months.

I don't have any problem with lagging. Netlify Platform lags a little because you're using someone else's domain rather than your custom domain, so that will be expected, but other than that, it is smooth for me. Nothing feels very rigid and laggy. The overall experience is good for me.

Scalability depends on what type of scalability is being discussed.

It is not very difficult. You can scale by directly pushing the code or deploying. However, it will become difficult in the future. It will become hectic for you to maintain all these things if you are deploying too many features because you need to maintain different back-ends and different other things including authentication itself. At that time, I think it will become a challenging aspect.

I'm a customer who uses Netlify Platform for certain things in my professional and personal life. Other than that, I don't have any customer relationship with them.

It's easy. You can drag and drop the folder itself, or you can connect to GitHub and push. It is not very difficult for me. For all the platforms I use, the deployment is very easy and not too difficult a task, and I think anyone can do it with basic knowledge.

One person can do it themselves. You don't need a team. If you are deploying a very large website or you have multiple back-ends, then maybe you would be required to have more people, but other than that, I don't think you need a team of two or three people for deployment. One person with some basic knowledge and specializing in that field, such as DevOps, can do it very easily.

It completely depends on what sites I'm deploying. If I'm deploying any AI models, I will be maintaining them after one week and each time for training and other tasks. But if I'm deploying a static website, then I don't think it needs any maintenance if you have a small production, such as a CDN itself or something other than that on the websites.

I have used two alternatives: GitHub Pages and Vercel.

For GitHub Pages, I can have a direct repository there. I can update the repository without needing to update anything else itself. It will directly go live. I don't need to make any changes and pull requests for this, and it is very easy for me. There are no limitations or any pricing model there, so I can just use it and have a back-end model as well. The same applies to Vercel. It has a very easy-to-view interface and all the things are easy. GitHub attachment is also easy, as is the custom domain deployment. They also have a small DNS and CDN, so it is much better for security purposes.

It completely depends on what sites I'm deploying. If I'm deploying any AI models, I will be maintaining them after one week and each time for training and other tasks. But if I'm deploying a static website, then I don't think it needs any maintenance if you have a small production, such as a CDN itself or something other than that on the websites. I would rate my overall experience with Netlify Platform an eight out of ten.