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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Oracle Autonomous Database
Score 8.5 out of 10
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Oracle Autonomous Database provides a self-driving, self-securing, self-repairing cloud service that eliminate the overhead and human errors associated with traditional database administration. Oracle Autonomous Database takes care of configuration, tuning, backup, patching, encryption, scaling, and more.
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Pricing
Amazon EMR (Elastic MapReduce)
Oracle Autonomous Database
Editions & Modules
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Offerings
Pricing Offerings
Amazon EMR
Oracle Autonomous Database
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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More Pricing Information
Community Pulse
Amazon EMR (Elastic MapReduce)
Oracle Autonomous Database
Features
Amazon EMR (Elastic MapReduce)
Oracle Autonomous Database
Database Development
Comparison of Database Development features of Product A and Product B
Amazon EMR (Elastic MapReduce)
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Ratings
Oracle Autonomous Database
7.2
24 Ratings
16% below category average
Version control tools
00 Ratings
6.213 Ratings
Test data generation
00 Ratings
5.714 Ratings
Performance optimization tools
00 Ratings
8.224 Ratings
Schema maintenance
00 Ratings
9.023 Ratings
Database change management
00 Ratings
7.015 Ratings
Database Administration
Comparison of Database Administration features of Product A and Product B
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.
Pro - Stability. Does everything anyone could need. If it's not there it will be on the next update. There is plenty of support for it. It's been around for a long time and it's reliable. The support is well documented and has a great reputation. Cons - Errors have been found in the documentation provided by Oracle with guidelines, etc. Oracles salespeople have a reputation of being obnoxious and condescending.
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.
There is no access to the physical host of the DB. This is expected from a managed DB. Everything must be done through the console or via API calls. This is a new learning curve for the DBAs.
Due to the lack of physical host access, certain features are not supported, such as Transportable tablespaces and Oracle LogMiner.
Certain special data types, (such as XMLType) are not allowed; be sure the app vendor certifies their product on this platform.
Because it does exactly what we need: it enables us to manage our development and testing database environments in a quick and simple way without requiring support from a database administrators team.
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
The product is continuously evolving and new features are added frequently. Management options through the OCI (Oracle Cloud Infrastructure) console and through the command line and API are being enhanced frequently.
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 found Oracle Autonomous Database very secure to store data and private information.I always feel secure with Oracle Autonomous Databases disaster recovery features.It is very effective to build applications for mobile and desktop devices lesser code using a low code development framework namely Oracle Application Express (ApEx).
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.