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.
Read full review Amazon Kinesis is a great replacement for Kafka and it works better whenever the components of the solution are AWS based.
Best if extended fan-out is not required, but still price-performance ratio is very good for simplifying maintenance. I would go with a different option if the systems to be connected are legacy, for instance in the case of traditional messaging clients.
Read full review Pros Amazon Elastic MapReduce works well for managing analyses that use multiple tools, such as Hadoop and Spark. If it were not for the fact that we use multiple tools, there would be less need for MapReduce. MapReduce is always on. I've never had a problem getting data analyses to run on the system. It's simple to set up data mining projects. Amazon Elastic MapReduce has no problems dealing with very large data sets. It processes them just fine. With that said, the outputs don't come instantaneously. It takes time. Read full review Processing huge loads of data Integrating well with IoT Platform on Amazon Integration with overall AWS Ecosystem Scalability Read full review 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. Read full review Not a queue system, so little visibility into "backlog" if there is any Confusing terminology to make sure events aren't missed Sometimes didn't seem to trigger Lambda functions, or dropped events when a lot came in Read full review Usability I give Amazon EMR this rating because while it is great at simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data, it can be run slow, freeze and glitch sometimes. So overall Amazon EMR is pretty good to use other than some basic issues.
Read full review Support Rating There's a vast group of trained and certified (by AWS) professionals ready to work for anyone that needs to implement, configure or fix EMR. There's also a great amount of documentation that is accessible to anyone who's trying to learn this. And there's also always the help of AWS itself. They have people ready to help you analyze your needs and then make a recommendation.
Read full review The documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.
Read full review 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.
Read full review The main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use cases, with low latency, higher volume of data, real time decisions and integration with multiple sources and destination at a decent price, Kinesis is better.
Read full review Return on Investment Positive: Helped process the jobs amazingly fast. Positive: Did not have to spend much time to learn the system, therefore, saving valuable research time. Negative: Not flexible for some scenarios, like when some plugins are required, or when the project has to be moved in-house. Read full review Caused us to need to re-engineer some basic re-try logic Caused us to drop some content without knowing it Made monitoring much more difficult We eventually switched back to SQS because Kinesis is not the same as a Queue system Read full review ScreenShots