Azure Data Lake Analytics services are beneficial when working with a lot of data. It can process enormous amounts of data extremely quickly. Service is secure and easy to set up, build, scale, and run on Azure. Regarding big data analytics and reporting, parallel processing has a significant impact. It consolidated our analytics from multiple systems and increased our analysis productivity. This tool has excellent support for reporting tools like Power BI and is very quick when performing analytics.
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.
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.
There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
While training materials are available online, Adoption rate - Yet to pick up.
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).
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.
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.
We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact that we have more people that already have Azure Cloud knowledge.
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.
