What is our primary use case?
I did not work with Apache, particularly Keeper for Apache Kafka, but I started to work maybe five years ago and right now I don't do anything there.
In the last year, I did not work with Kafka. Currently, I am an architect, data architect, but I don't implement anything with Kafka or Confluence; it's a commercial product in the cloud.
I don't know the name of the product I am currently working with. I was working with Databricks, Snowflake, and DBT.
I prefer working with Microsoft Azure cloud because I need time for me and don't need to learn more clouds. For example, in a project, I am working with Snowflake into Azure and into Amazon, but I don't touch any technological stack or any services into Amazon; I only work with Azure.
What is most valuable?
I have been familiar with Databricks Data Intelligence Platform for maybe eight years.
Databricks Data Intelligence Platform is a cloud solution and a business solution, but the origin is Spark. Apache Spark is the base of Databricks Data Intelligence Platform, and the same person who created Spark now has the company Databricks, so all is in the cloud.
Databricks Data Intelligence Platform is faster and easier to work with for deploying the Medallion Lakehouse, while Fabric from Microsoft is better for Business Intelligence; combining them makes for great solutions.
Delta Lake is a great solution from Databricks; it's an open-source system but deployed by Databricks, and I think it's the standard for Microsoft.
I use the collaboration feature in Databricks Data Intelligence Platform, which is great collaboration, but I don't understand exactly what feature you mean by collaborate.
What needs improvement?
I need to pass my lesser two certificates because I study a lot and started to work, but I can't finish my official certificates; I have only two and needed more.
I don't know what features I would like to see in future updates because they innovate too much.
Pricing is something that could always be improved.
For how long have I used the solution?
I have been using Databricks Data Intelligence Platform for maybe eight years.
How was the initial setup?
The deployment of Databricks Data Intelligence Platform is not always easy; it depends on what your problem is. You need to have some experience because it's easier in the cloud, but it's more difficult to implement on-premise like Spark. You need to know what things you want to do there, configure clusters, and use services specific for the corresponding problem or the solution for the customer.
If you only use Databricks Data Intelligence Platform for implementing simple systems, it's easy and takes hours. But if you need to implement governance and Unity Catalog or use BI AI for deploying natural language dashboards for business insights, it can take more time. You can start working in two days or three days with the right help from IT.
What about the implementation team?
I participated in deployment in the configuration for eight years of experience with Databricks Data Intelligence Platform.
What other advice do I have?
There is a lot of information and support available now; you have access to many courses and support, so don't worry.
For creating big data with AI, I rate Databricks Data Intelligence Platform very high, maybe a nine or eight. I would rate my overall experience with this product a 9.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
