Azure Pipelines vs. Databricks Data Intelligence Platform

It is good tool if you are doing continuous improvements in your code and you wish it goes live whenever you push code to GitHub. So integrating Azure Pipeline, it automatically does CI/CD in the background once you push code/merge code and it is live in few minutes. It also does some automated tests if you have wrote scripts

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

• Integration with SonarQube
• Integration with Azure DevOps
• Integration with GitHub

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

• The errors which we got sometimes are not clearly enough.
• There are some let's say hidden options, they could be more visible
• When the process is running we have to remember about manually refreshing to see the current status because it doesn't work automatically

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

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

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.

We have used the GitHub CI/CD. Earlier we were using the Azure Pipelines but after GitHub had their actions, we integrated that for CI/CD. It runs the tests and makes a production build which can be live. GitHub CI/CD is more useful because we have to make script only once then just by few changes we can deploy it onto Azure, AWS, Google anywhere so we found it more convenient

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 have had outages from Azure in the past

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