AWS CodePipeline vs. Azure Pipelines vs. Databricks Data Intelligence Platform

I think AWS CodePipeline is a great tool for anyone wanted automated deployments in a multi-server/container AWS environment. AWS also offers services like Elastic Beanstalk that provide a more managed hosting & deployment experience. CodePipeline is a good middle ground with solid, built-in automation with enough customizability to not lock people into one deployment or architecture philosophy.

Incentivized

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

• It is reliable and works without errors
• It integrates well with our repository and all other AWS functions as well as our end database

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

• Ease of use - things like CircleCI or other tools are a bit easier to learn.
• Ability to build from more sources.

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

Overall, I give AWS Codepipeline a 9 because it gets the job done and I can't complain much about the web interface as much of the action is taking place behind the scenes on the terminal locally or via Amazon's infrastructure anyway. It would be nicer to have a better flowing and visualizable web interface, however.

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

Our pipeline takes about 30 minutes to run through. Although this time depends on the applications you are using on either end, I feel that it is a reasonable time to make upgrades and updates to our system as it is not an every day push.

Incentivized

We didn't need a lot of support with AWS CodePipeline as it was pretty straightforward to configure and use, but where we ran into problems, the AWS community was able to help. AWS support agents were also helpful in resolving some of the minor issues we encountered, which we could not find a solution elsewhere.

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.

CodeCommit and CodeDeploy can be used with CodePipeline so it’s not really fair to stack them against each other as they can be quite the compliment. The same goes for Beanstalk, which is often used as a deployment target in relation to CodePipeline.

CodePipeline fulfills the CI/CD duty, where the other services do not focus on that specific function. They are supplements, not replacements. CodePipeline will detect the updated code and handle deploying it to the actual instance via Beanstalk.

Jenkins is open source and not a native AWS service, that is its primary differentiator. Jenkins can also be used as a supplement to CodePipeline.

Incentivized

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

• CodePipeline has reduced ongoing devops costs for my clients, especially around deployment & testing.
• CodePipeline has sped up development workflow by making the deployment process automated off git pushes. Deployment takes very little coordination as the system will just trigger based on what is the latest commit in a branch.
• CodePipeline offered a lot of out-of-the-box functionality that was much simpler to setup than a dedicated CI server. It allowed the deployment process to built and put into production with much less and effort and cost compared to rolling the functionality manually.

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