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.

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 choice when working with lesser data.

Compared to IBM Analytics Engine, Amazon EMR is a much cheaper option to get the work down. And compared to Alluxio, Amazon EMR is much more user-friendly. The drawback is that amazon EMR would be very costly if the run failed.

Good choice for startup, open source and cost-effective and saves a lot of setup time.
Run times are reduced to minutes compared to hours on EC2 or other compute servers.
Easy to choose between hadoop or spark based EMR cluster, it can be used in combination with other AWS services, for example we can create budgets involving EMR and various other tasks in AWS data pipeline service.

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.

Apache Hadoop required us to do all the leg work and we did not have the resources for that. It was ideal that AWS offers a MapReduce solution as we use it to host various servers. It is one place for all our needs. Very convenient. Apache Hadoop is still a good product but for us, at the moment we are an AWS shop.

Compared to Databricks, Amazon EMR is a much cheaper option to get the work down. And compared to Amazon ec2, Amazon EMR is a much more powerful tool to get large datasets transformation down in a fairly short amount of time. The drawback is that amazon EMR would be very costly if the run failed.

NA

Amazon EMR is faster, cheaper, easier, and enjoyed more by our employees compared to Azure HDInsight. We selected Amazon because we saw an advertisement and wanted to try it out to see how it was. We will continue to use it until it is not around or until we find something that is better.

EMR is more suited for developers. Databricks feel more for data science-oriented with its notebooks and customs visualizations. With EMR you can more easily add additional capacity on-damnd on the instance. With others is a more cumbersome process. And then, you can also configure it to be dynamic and change depending on your usual flow of data.

Perhaps the biggest advantage Amazon Elastic MapReduce has over competing big data management software is the user base. Elastic MapReduce, compliments of its connection with Amazon, has a large user base to whom questions about functionality can be addressed. The software also has a very nice user interface. Additionally, Elastic MapReduce runs fairly quickly and the results are generally easier to manipulate. With this said, Elastic MapReduce is definitely not the easiest nor quickest tool for big data analytics.

The alternatives to EMR are mainly hadoop distributions owned by the 3 companies above. I have not used the other distributions so it is difficult to comment, but the general tradeoff is, at the cost of a longer setup time and more infra management, you get more flexible versioning and potentially faster access to newer versions of some frameworks such as Spark.

Having one of these enterprise edition license comes at its own costs. But, the flexibility to have the cluster spin up with the workbenches and code snippets on the same is really beneficial. Especially, if one had to move out of EMR and consider an option which reduces the debugging time in establishing connections to AWS resources, I would love to used the mentioned three resources on EC2. This would definitely make the processing time to reduce as there is a flexibility to test real time and execute the code snippet and look at the performance and monitor the snippet in real time.

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 figure out how to deploy projects in MapReduce.