Azure Kubernetes Service (AKS) vs. Databricks Data Intelligence Platform

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

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

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

Incentivized

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

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

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

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

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

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

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

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

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

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

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized