Amazon EMR (Elastic MapReduce) vs. Google Cloud Dataproc

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon EMR
Score 8.2 out of 10
N/A
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.N/A
Google Cloud Dataproc
Score 8.7 out of 10
N/A
Dataproc, on Google Cloud, is a fully managed and scalable service for running Apache Hadoop, Apache Spark, Apache Flink, Presto, and 30+ open source tools and frameworks. Dataproc is used for data lake modernization, ETL, and secure data science, at scale, integrated with Google Cloud. Key features Fully managed and automated big data open source software Serverless deployment, logging, and monitoring so users can focus on data and analytics, not on…N/A
Pricing
Amazon EMR (Elastic MapReduce)Google Cloud Dataproc
Editions & Modules
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Offerings
Pricing Offerings
Amazon EMRGoogle Cloud Dataproc
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon EMR (Elastic MapReduce)Google Cloud Dataproc
Best Alternatives
Amazon EMR (Elastic MapReduce)Google Cloud Dataproc
Small Businesses

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DBeaver
DBeaver
Score 9.2 out of 10
Medium-sized Companies
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Score 9.9 out of 10
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Score 9.2 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 7.1 out of 10
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Score 9.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon EMR (Elastic MapReduce)Google Cloud Dataproc
Likelihood to Recommend
8.0
(0 ratings)
-
(0 ratings)
Usability
7.0
(0 ratings)
-
(0 ratings)
Support Rating
9.0
(0 ratings)
-
(0 ratings)
User Testimonials
Amazon EMR (Elastic MapReduce)Google Cloud Dataproc
Likelihood to Recommend
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.
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Pros
  • 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.
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Cons
  • 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.
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Usability
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
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Support Rating
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.
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Alternatives Considered
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.
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Return on Investment
  • 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.
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ScreenShots