Amazon Athena vs. Azure SQL Database

Best suited for analyzing huge amounts of data by just querying on Amazon Athena. Amazon Athena is also best to integrate with Amazon Quickight for visualization and reporting of data. Easy to work with CSV, JSON, and columnar data formats like Parquet, and ORC. Less appropriate to work with AVRO data format and also stored procedures are not supported in Amazon Athena. The size of a single row is also limited to 32 MB.

Incentivized

Your upcoming app can be built faster on a fully managed SQL database and can be moved into Azure with a few to no application code changes. Flexible and responsive server less computing and Hyperscale storage can cope with your changing requirements and one of the main benefits is the reduction in costs, which is noticeable.

Incentivized

• Load Balance traffic analysis
• Big data report generation
• Micro services pattern query analysis

Incentivized

• Scalability is #1: if it used to be an almost no-win endeavour to try to modernize your server or migrate to other hardware, with Azure SQL Database it becomes a press of a button.
• All the tools simply work after you are on Azure SQL Database.
• The applications do not need changes in order to start using Azure SQL Database.
• Hybrid Cloud scenarios will work.
• Clustering and failover - already there.
• You can start monitoring the use and extract performance insights in a new way in Azure.

Incentivized

• Every dialect of SQL has some missing functions. I wish there was automated GROUP BY options here.
• There are connection problems back to Power BI occasionally.
• If you don't watch certain queries, it's possible that it takes a long time to run and charges you a lot of money.

Incentivized

• A little slow on processing complex or large Views. We use a lot of Views to feed our BI system, and the processing time could see some improvement, IMHO.
• Additional monitoring components would be nice too, automating some built in performance measurement tools would be a nice feature.
• Price can always be improved as well. It’s not bad, but room for improvement.

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

Easy to use. Scalable. Gets the job of data warehousing setup done. Using the datalake on S3 has become super convenient.

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

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 Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using Microsoft Power BI for reporting needs, you might want to consider going the Azure route.

Incentivized

I selected Azure SQL because it integrates nicely with the technology stacks we currently maintain. The pricing is right, and clients are happy with that. Scaling is easy. Most of our clients don't want to maintain a full-blown database server, and they don't need one. For them, Azure SQL is the right size.

Incentivized

• It's easy to store and query data on S3. Multiple teams can query the same data to generate their reports. It removes the need for a full-fledged data warehouse for a startup. Saves costs.
• Improved team efficiency on monitoring user activities by easy logging and reporting.
• As the dataset gets heavier on S3, one needs to understand partitioning and that leads to the requirement of expertise.

Incentivized

• Positive: very easy implementation!
• Positive: finally a good (easy) implementation of data masking, something we always neglected on premise.
• Positive: no maintenance!! No "server is down" messages on your phone at 3 'o clock in the morning.
• Negative: Too expensive to use for all other scenario's than a few very important applications.

Incentivized