What is our primary use case?
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.
How has it helped my organization?
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.
What is most valuable?
Being involved in building models, I appreciate the integrated version control, which obviates the need to manage different versions of my models manually.
I don't have to do a lot of experimenting; the version control is built-in.
Azure also offers additional features in the AI space, which are beneficial, making it convenient to have all tools in one place without the overhead of using different tools for different purposes.
Integration with Microsoft products is seamless, facilitating connections to tools like Tableau and enabling easy API creation.
What needs improvement?
One thing I find is that there are updates happening all the time, but they don't always roll out information about the changes. For example, the way to connect to Git repositories six months ago was different from how it is now.
It would be good if the platform incorporated some kind of announcement system, like "This process has changed, here's the revised method." That would be really helpful. So, update announcements should be there.
For how long have I used the solution?
I have been using it for two and a half years.
What do I think about the stability of the solution?
I would rate the stability an eight out of ten. It's quite stable, although there have been occasional downtimes affecting entire regions, but these issues are resolved quickly without causing significant business interruptions.
What do I think about the scalability of the solution?
Azure's scalability, particularly in terms of cores and threads, significantly enhances our work.
The ability to easily adjust capacity is crucial, especially when working with large geospatial datasets and running transformations or models requiring substantial computational resources.
In my department, there are around 40 end users using this solution.
I would rate the scalability an eight out of ten.
The platform's scalability meets our needs well.
How are customer service and support?
We haven't actually needed to contact Azure support very often. However, the Databricks team, specifically our Service Account Manager, has been very helpful. They've reached out a couple of times to understand the types of projects we're working on and suggest additional functionalities that might be beneficial.
There was also some communication with the GenAI team about potential use cases that could be integrated into our platform or future products.
I've never had to directly contact an Azure support representative.
The online documentation is very comprehensive, and there's a large, active community that we can leverage for troubleshooting.
Basically, all the documentation we need is readily available to resolve any queries.
Which solution did I use previously and why did I switch?
We also use Cloud Foundry, alongside Azure, for various tasks within our operations.
How was the initial setup?
Deployment is generally stable. But there are specific scenarios, especially in Asia Pacific, where there are limitations.
Microsoft hasn't fully rolled out services in all regions yet. This can cause problems when we try to deploy certain models in those specific regions where services are unavailable.
In contrast, if you have a self-deployed API, something you created in a native language and deployed on your own server, it would be more readily available across regions.
So, the lack of Microsoft services in certain regions can become a blocker.
What about the implementation team?
Troubleshooting and maintenance for Microsoft Azure products are handled by our internal IT team.
We have around 40 to 50 people, encompassing a mix of roles including administrators, engineers, and developers.
What other advice do I have?
I would recommend using this solution. Internally, we've always preferred it over Cloud Foundry. Here's why: Microsoft Azure has been a much more seamless experience. For example, Cloud Foundry isn't very scalable. It can't handle parallel computation well, and it frequently hangs. Plus, it's not very user-friendly as a platform.
So, for all those reasons, I would recommend Azure. It's highly scalable, supports parallel computation, and offers all the steps needed for a product lifecycle within one platform. That eliminates the need for separate storage, deployment, and production environments – everything's available from the start. Those are the key reasons for my recommendation.
Overall, I would rate the solution as eight out of ten because there are still areas for improvement, like integration with cross-platform.
Disclosure: My company does not have a business relationship with this vendor other than being a customer.