Apache Spark vs. Azure App Service

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

Incentivized

You may easily deploy your apps to Azure App Service if they were written in Visual Studio IDE (typically.NET applications). With a few clicks of the mouse, you may already deploy your application to a remote server using the Visual Studio IDE. As a result of the portal's bulk and complexity, I propose Heroku for less-experienced developers.

Incentivized

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

Incentivized

• Extremely easy to deploy and update from Visual Studio
• It integrates seamlessly with other Azure PaaS resources
• It has an in-depth integration with AppInsights, so you can understand errors and their root cause easily.
• Easy to create and delete, what is not the same case in a IaaS resource
• It escalates based on CPU workload and some other resource variables.
• Configuration changes are almost immediate
• Offers an excellent abstraction from hardware backend of the platform
• That's updated very often, saving time and the risk of a self-performed update over a IaaS
• That's really easy to develop for Web Apps
• It supports Function Apps and Web Apps into the same "cost black box"

Incentivized

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

Incentivized

• Jumps between resource sizes can get expensive
• You may wind up putting a lot of eggs in one basket--not necessarily a con but something to keep in mind (most of your data will likely be managed and processed through Microsoft products/services if you fully commit to Azure App Service).
• Learning new technology. If you're moving from on-premises to Azure App Service (or any cloud solutions), you'll likely have to rethink how things are done to achieve the same end results (and/or resources may become expensive quickly).

Incentivized

Capacity of computing data in cluster and fast speed.

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

Incentivized

I have given this rating because Azure App Service performs very well in terms of speed, reliability, and reducing overhead, and improves overall team productivity, with a little scope for improvement in complex testing scenarios and configurations, scalability concerns in a large setup, and similar tracking and audit needs.

Incentivized

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

Microsoft has always been known for providing a high standard in terms of customer support and Azure App Service (and as a matter of fact the whole Azure Platform) is no exception. Azure App Service never caused us any issues and we only contacted their customer support for questions regarding server locations and pricing. I feel pretty satisfied with how they treat their customers.

Incentivized

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

Incentivized

When we chose it, we did so because of its integration with Microsoft applications; now we need to integrate with AI, and Azure doesn't offer a good integration. That is the main reason to change it. It is still great to develop Windows- and Microsoft-based applications, but if we need to integrate with AI, Google wins by far.

Incentivized

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

Incentivized

• Deployment of ASP.NET apps at the organization has been sped up.
• An option to offer access to the version control system on a third platform so that we could easily deploy our apps.
• Because of Azure App Service's scalability capabilities, the costs of running the services are kept to a minimum. As a result, we may save hundreds of dollars each month compared to the expenses of traditional servers by using fewer resources during slack periods.

Incentivized