Updated Analytics and Big Data Comparison: AWS Vs. Azure
Here's everything you need to know about analytics services offered by AWS and Azure.
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Building upon my earlier post, today, I wanted to share with you updated graphics and links for the analytic and big data services offered by Microsoft Azure and Amazon Web Services.
It is my hope that this post will be a starting guide for you when you need to research these analytics and big data services. I have included relevant links for each service, along with some commentary, in the text of this post below. I’ve done my best to align the services, but there is some overlap between offerings.
I’m not going to do a feature comparison here because these systems evolve so quickly that I’d spend all day updating the info. Instead, you get links to the documentation for everything and you can do your own comparisons as needed. I will make an effort to update the page as frequently as possible. Let's get started.
Azure offerings: SQL Data Warehouse
AWS offerings: Redshift
It feels like these two services have been around forever. That’s because, in Internet years, they have. Redshift goes back to 2012, and SQL DW goes back to 2009. That’s a lot of time for both Azure and AWS to learn about data warehousing as a service.
Azure offerings: HDInsight
AWS offerings: Elastic MapReduce
Both services are built upon Hadoop, and both are built to hook into other platforms such as Spark, Storm, and Kafka.
These are true enterprise-class ETL services, complete with the ability to build a data catalog. Once you try these services, you will never BCP data again.
I didn’t list Event Hubs here for Azure, but if you want to stream data, you are likely going to need that service as well. And Kinesis is broken down into specific streams, too. (In other words, “Analytics” is an umbrella term, and is one of the most difficult things to compare between Azure and AWS).
Azure offerings: PowerBI
AWS offerings: QuickSight
I saw some demos of QuickSight while at AWS re:Invent last fall, and it looks promising. It also looks to be slightly behind PowerBI at this point. Of course, we all know most people are still using Tableau, but that is a post for a different day.
Elastisearch for both is just a hook to the Elastisearch open-source platform. For Azure, you have to get that from their marketplace (that’s what I link to because I can’t find it anywhere else). One of the biggest differences I know between the services is the number of languages supported. AWS CloudSearch claims to support 34, and Azure Search claims to support 56.
DeepLens is a piece of hardware, but I wanted to call it out because you will hear it mentioned. When you use DeepLens, you use a handful of AWS services such as SageMaker, Lambda, and S3 storage. I enjoyed using Azure Machine Learning Studio during my data science and big data certifications. But the same thing is true, you use associated services. This makes price comparisons difficult.
AWS offerings: Athena
Imagine a library without a card catalog and you need to find one book. That’s what your data looks like right now. I know you won’t believe this, but not all data is tracked or classified in any meaningful way. That’s why services like Athena and Data Catalog exist.
Azure Pricing calculator: https://azure.microsoft.com/en-us/pricing/calculator/
AWS Pricing Calculator: https://calculator.aws/
Same as the previous post, you will find it difficult to conduct an apples-to-apples comparison between services. Your best bet is to start at the pricing pages for each and work your way from there.
I hope you find this page (and this one) useful for referencing the many analytic and big data service offerings from both Microsoft Azure and Amazon Web Services. I will do my best to update this page as necessary and offer more details and use cases as I am able.
Published at DZone with permission of Thomas LaRock, DZone MVB. See the original article here.
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