Amazon EMR is a cloud-native big data platform for processing vast amounts of data quickly, at scale. Using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi (Incubating), and Presto, coupled with the scalability of Amazon EC2 and scalable storage of Amazon S3, EMR gives analytical teams the engines and elasticity to run Petabyte-scale analysis.
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Microsoft Azure
Score 8.5 out of 10
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Microsoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.
$29
per month
Pricing
Amazon EMR (Elastic MapReduce)
Microsoft Azure
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Developer
$29
per month
Standard
$100
per month
Professional Direct
$1000
per month
Basic
Free
per month
Offerings
Pricing Offerings
Amazon EMR
Microsoft Azure
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
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Community Pulse
Amazon EMR (Elastic MapReduce)
Microsoft Azure
Considered Both Products
Amazon EMR
Verified User
Anonymous
Chose Amazon EMR
Amazon EMR (Elastic MapReduce) compares well against Microsoft Azure and Microsoft SQL servers in terms of performance and ease of use. This also means you pay more for the service. Amazon EMR is a great tool for handling large amounts of data. SQL Server would be a better …
Amazon EMR (Elastic Map Reduce) compares well against GCP and Azure - but you need to be careful of the costs involved in spinning up such a cluster. It is easy to configure however and it is my preferred platform to deploy our solutions because of its ease of use.
AWS is competitor and it's leading in cloud space with his wide sprawl of offerings and services. AWS is a ocean once you login you get everything on one console. AWS leads this space with all his offerings and capabilities. But Azure is not behind, it is competing and is …
We are running it to perform preparation which takes a few hours on EC2 to be running on a spark-based EMR cluster to total the preparation inside minutes rather than a few hours. Ease of utilization and capacity to select from either Hadoop or spark. Processing time diminishes from 5-8 hours to 25-30 minutes compared with the Ec2 occurrence and more in a few cases.
Actually, migrating to Microsoft Azure is a good solution for almost any situation, especially when all components of your network are ready to become cloud-based. The only drawback I personally encounter frequently is that older software packages cannot always be easily picked up and moved to Microsoft Azure in an optimal manner.
The cluster size of MapReduce is very dynamic and therefore scalability is good for EMR.
It also works well with other Amazon Web Services like Amazon Simple Storage Service, which means that data can be taken from those services and written back to them.
I tried using the in-house hosting at the university I work in, but there would be a lot of complications with technical support required. For Amazon, the support and documentation was good to solve these problems faster.
Azure simply provides end to end life cycle. Starting from the development to automated deployment, you will find [a] bunch of options. Custom hook-points allow [integration] on-premise resources as well.
Excellent documentation around all the services make it really easy for any novice. Overall support by [the] community and Azure Technical team is exceptional.
BOT Services, Computer Vision services, ML frameworks provide excellent results as compare to similar services provided by other giants in the same space.
Azure data services provide excellent support to ingest data from different sources, ETL, and consumption of data for BI purpose.
Sometimes bootstrapping certain tools comes with debugging costs. The tools provided by some of the enterprise editions are great compared to EMR.
Like some of the enterprise editions EMR does not provide on premises options.
No UI client for saving the workbooks or code snippets. Everything has to go through submitting process. Not really convenient for tracking the job as well.
In our experience, Azure Kubernetes Survice was difficult to set up, which is why we used Kubernetes on top of VMs.
Azure REST API is a bit difficult to use, which made it difficult for us to automate our interactions with Azure.
Azure's Web UI does a good job of showing metrics on individual VMs, but it would be great if there was a way to show certain metrics from multiple VMs on one dashboard. For example, hard drive usage on our database VMs.
We have been very satisfied with Windows Azure and now a lot of our business depends on it as more teams are now deploying their applications into Azure. Our next step is to have our Infrastructure team move their resources to Azure. It will take awhile for that to happen but we are positive that it will.
Documentation is quite good and the product is regularly updated, so new features regularly come out. The setup is straightforward enough, especially once you have already established the overall platform infrastructure and the aws-cli APIs are easy enough to use. It would be nice to have some out-of-the-box integrations for checking logs and the Spark UI, rather than relying on know-how and digging through multiple levels to find the informations
Microsoft Azure's overall usability has been better than expected. Often times vendors promise the world, only to leave you with a run-down town. Not the case with our experience. From an implementation perspective, all went perfect, and from the user-facing experience we have had no technical issues, just some learning curve issues that are more about "why" than "how"
I give the overall support for Amazon EMR this rating because while the support technicians are very knowledgeable and always able to help, it sometimes takes a very long time to get in contact with one of the support technicians. So overall the support is pretty good for Amazon EMR.
Support is easy with all the knowledge base articles available for free on the web. Plus, if you have a preferred status you can leverage their concierge support to get rapid response. Sometimes they’ll bounce you around a lot to get you to the right person, but they are quite responsive (especially when you are paying for the service). Many of the older Microsoft skills are also transferable from old-school on-prem to Azure-based virtual interfaces.
As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.
Snowflake is a lot easier to get started with than the other options. Snowflake's data lake building capabilities are far more powerful. Although Amazon EMR isn't our first pick, we've had an excellent experience with EC2 and S3. Because of our current API interfaces, it made more sense for us to continue with Hadoop rather than explore other options.
I feel that Microsoft Azure typically outperforms Google Cloud Platform in hybrid cloud capabilities, integration aspects, and, primarily, security compliance features. Azure offered superior integration with Microsoft's enterprise software ecosystem, and it's second to none in my opinion. This made it the natural choice for most, especially if heavily invested in Windows, Office 365, or Active Directory deployments. We chose Azure over GCP because we simply needed Windows workload support as a strong driver, more access to global regions, and let's not forget that most tech teams in an organization are Microsoft Certified, which makes skillset transfer from on-prem to cloud a minimal learning curve over shifting to a different provider.
It was obviously cheaper and convenient to use as most of our data processing and pipelines are on AWS. It was fast and readily available with a click and that saved a ton of time rather than having to figure out the down time of the cluster if its on premises.
It saved time on processing chunks of big data which had to be processed in short period with minimal costs. EMR solved this as the cluster setup time and processing was simple, easy, cheap and fast.
It had a negative impact as it was very difficult in submitting the test jobs as it lags a UI to submit spark code snippets.
Times and growth went into it. By balancing on-premises maintenance with continuous cloud improvements, we’ve budgeted and planned endlessly increased capacity.
In today’s world of cyber-crime, clients can put even more faith in what they’ve heard. We built an innovative single-sign-on hub for all users. Also, other business platforms use Azure application gateways, reducing worker switching time and increasing productivity.
Its step can automate to improve the investment. In addition, we can integrate our organization’s credentials into an authorization for other systems.
