Azure Kubernetes Service (AKS) vs. Google Cloud Spanner vs. Azure SQL Database

AKS works very well for running containerized applications that require high availability and scalability. This includes systems like our HRIS platform and customer-facing web applications. AKS is a good choice when applications are broken into multiple services that need independent scaling and deployment. It provides the flexibility needed to manage these architectures effectively. But for single, low-traffic applications or simple internal tools, AKS can be overkill. For scenarios like that Azure App Service would be better.

Incentivized

Google Cloud Spanner is suited for limitless horizontal scaling while maintaining strong consistency which needs to support ACID. NoSQL databases work in scaling but no ACID support. RDBMS support ACID, but horizontal scaling is not as great. The API it provides result in some limitations to related areas of the code, such as connection pools or database linking framework. So high # of connection pools can vary.

Incentivized

We have found it's a great alternative for making older legacy applications work with online databases instead of only on-premises databases. We've converted over a dozen applications this way, and it has allowed our clients to have a distributed workforce using their applications without incurring the expense of a complete application rewrite.

Incentivized

• AKS makes it easier to replicate data to multiple regions
• Azure portal make it easier to manage the resources of the organization

Incentivized

• Super high availability
• Scales automatically
• High standard SLA

Incentivized

• Maintenance is always an issue, so using a cloud solution saves a lot of trouble.
• On premise solutions always suffer from fragmented implementations here and there, where several "dba's" keep track of security and maintenance. With a cloud database it's much easier to keep a central overview.
• Security options in SQL database are next level... data masking, hiding sensitive data where always neglected on premise, whereas you'll get this automatically in the cloud.

Incentivized

• Steep learning curve
• Expected charges are unclear until you see real production usage
• Operations teams need to learn an entirely new skill set

Incentivized

• Support for Views
• Support for more databases (schemas).
• More index types that can be supported (Functional)
• Backups (ie table/data backup) if data is deleted or truncate by accident.

Incentivized

• One needs to be aware that some T-SQL features are simply not available.
• The programmatic access to server, trace flags, hardware from within Azure SQL Database is taken away (for a good reason).
• No SQL Agent so your jobs need to be orchestrated differently.
• The maximum concurrent logins maybe an unexpected problem.
• Sudden disconnects.
• The developers and admin must study the capacity and tier usage limits https://docs.microsoft.com/en-us/azure/azure-subscription-service-limits otherwise some errors or even transaction aborts never seen before can occur.
• Only one Latin Collation choice.
• There is no way to debug T-SQL ( a big drawback in my point of view).

Incentivized

This is best solution as a DBA one could expect from a service provider and as a cloud service, it removes all your hassles.

Incentivized

As already said, the UI/CLI and even terraform are perfectly fine, but certain details could be documented better. For instance, if I want to secure the whole Azure Kubernetes Service (AKS) with my own managed keys, then it is very complex and hard to get there. Not really a single source that gives you the whole picture. Besides that, it is still good to use, in most cases intuitive but details mentioned as above can be tricky.

Incentivized

The interfaces are intuitive once you are familiar with all the functions. The ability to use different tools to interact with the platform, such as directly via a browser or code editors such as VS Code or Visual Studio is a great option and allows for integrating withn the project and other testing and developing tools.

Incentivized

Microsoft support was really good, whenever we raise any ticket they come back to us within a couple of hours.

Incentivized

We give the support a high rating simply because every time we've had issues or questions, representatives were in contact with us quickly. Without fail, our issues/questions were handled in a timely matter. That kind of response is integral when client data integrity and availability is in question. There is also a wealth of documentation for resolving issues on your own.

Incentivized

Amazon EKS stacked up very well and had better performance in some areas. However, Azure Kubernetes Service was a better fit given our Azure environment.

Incentivized

At that point, we were looking at something [that] can hold our relational database, [...] provide stable connection, and maintain high ACID transition. BigTable is for nonrelational database so it was out of our [sight] very quickly. BigQuery is a data warehouse that can hold huge amount of data but not ideal for transition. AWS RDS is [...] similar to Spanner but because most of our services are already on GCP, so we went with Spanner.

Incentivized

We moved away from Oracle and NoSQL because we had been so reliant on them for the last 25 years, the pricing was too much and we were looking for a way to cut the cord. Snowflake is just too up in the air, feels like it is soon to be just another line item to add to your Azure subscription. Azure was just priced right, easy to migrate to and plenty of resources to hire to support/maintain it. Very easy to learn, too.

Incentivized

• We had to spend more time on Azure Kubernetes Service (AKS) than on AWS and GCP to get our kubernetes cluster up and running
• The resources on nodes need to be left out unused, so effectively it is wasting money there
• It definitely made us spend more time into maintaining kubernetes

Incentivized

• Backups specifically if transactional data is deleted. Restoring made us lose time.
• Sharding on Horizontal level was quick and easy. Deployment and increasing nodes is easy
• Large dataset handling.
• ACID compliance

Incentivized

• Perfect for small and medium databases, being very cost effective.
• As a Platform as a Service, there is no concern about patches, upgrades and end of life.
• Be aware of security and network capabilities. The service cannot run in the VNET as Azure Virtual Machines do.