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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Cloudera Manager
Score 9.9 out of 10
N/A
Cloudera Manager is a management application for Apache Hadoop and the enterprise data hub, from Cloudera. Its automated wizards let users quickly deploy a cluster, no matter what the scale or the deployment environment, complete with intelligent, system-based default settings.
$0.07
per hour CCU
Pricing
Amazon EMR (Elastic MapReduce)
Cloudera Manager
Editions & Modules
No answers on this topic
Data Hub
$0.04/CCU
Hourly rate
Data Engineering
$0.07/CCU
Hourly rate
Data Warehouse
$0.07/CCU
Hourly rate
Operational Database
$0.08/CCU
Hourly rate
Flow Management on Data Hub
$0.15/CCU
Hourly rate
Machine Learning
$0.17/CCU
Hourly rate
DataFlow
$0.30/CCU
Hourly rate
Offerings
Pricing Offerings
Amazon EMR
Cloudera Manager
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Pricing is per Cloudera Compute Unit (CCU) which is a combination of Core and Memory. CCU prices shown for each service are estimates and may vary depending on actual instance types. The prices reflected do not include infrastructure cost, networking costs, and other related costs which will vary depending on the services you choose and your cloud service provider.
More Pricing Information
Community Pulse
Amazon EMR (Elastic MapReduce)
Cloudera Manager
Considered Both Products
Amazon EMR
Verified User
Analyst
Chose Amazon EMR (Elastic MapReduce)
EMR provides dynamic cluster size, lots of documentation, and integration with other Amazon Web Services which are some of the things that Cloudera distribution for Hadoop lacked. Some products are hard to learn but EMR was much easier and helped save time spent on trying to …
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.
It would be suited for customers who feel more comfortable with using a GUI. It is less appropriate for developers or engineers who are comfortable with command line
EMR does well in managing the cost as it uses the task node cores to process the data and these instances are cheaper when the data is stored on s3. It is really cost efficient. No need to maintain any libraries to connect to AWS resources.
EMR is highly available, secure and easy to launch. No much hassle in launching the cluster (Simple and easy).
EMR manages the big data frameworks which the developer need not worry (no need to maintain the memory and framework settings) about the framework settings. It's all setup on launch time. The bootstrapping feature is great.
It would have been better if packages like HBase and Flume were available with Amazon EMR. This would make the product even more helpful in some cases.
Products like Cloudera provide the options to move the whole deployment into a dedicated server and use it at our discretion. This would have been a good option if available with EMR.
If EMR gave the option to be used with any choice of cloud provider, it would have helped instead of having to move the data from another cloud service to S3.
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
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.
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 have not used any competitors, such as Hortonworks, because Cloudera Manager just works and meets all my customer's needs. I only have deployed Hadoop using command line, which is not easy to use and manage.
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.
Cloudera Manager has allowed our organization to deploy Apache Hadoop to operations quicker and with less training versus using the command line exclusively.