Amazon EMR

You can use Amazon EMR if you wish to shift to the cloud and save money by using Apache Spark or Apache Hadoop on-premises. When the amount of work you have to handle data fluctuates a lot. Setting up flexible and scalable scenarios with AWS's EMR can assist you.

Our teams prefer using this service to deploy because it is simple to configure and scale even though it can be expensive at times. It also needs some training for new users to get familiar with all the functions and features. Experience matters a lot while using this platform.

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.

If someone is playing with a large dataset over AWS it's worth using it for a small kind of dataset it doesn't make sense as it's complex to manage.

It provides a nice graphical user interface to manage and work with big data map reduction tasks instead of manual configuration with hadoop or cli.it saves a lot of time and effort.We create big data monitoring system filters.

It provides a good GUI to manage and handle big data map reduction tasks and its configuration saves a lot of time and effort.

Amazon EMR (Elastic Map Reduce) is ideally suited for organisations that need to provide a scalable platform for data processing. Amazon EMR is perfect to handle data volumes of any size or volume and it is easy to configure and spin up an EMR cluster quickly. If you are cost-focused you need to be careful as an EMR cluster can end up costing a lot of money if misconfigured.

When I need to process large data and meaningful information. But it is very flexible where I can scale based on the data size and how I want to analyze it. But still can improve for nontechnical users as there is some jargon to learn to get the most out of the solution.

It is a go-to software for experienced developers and for a company that wishes to process huge amounts of data at scale.

It has its use case. When it comes to extremely large datasets' pulling and transformation, it is the best way to do it. Just need to be aware that if something is not set correctly in the first place, the cost could potentially be very large. Therefore, it is very critical to not do development work there.

Amazon EMR is well suited for Big data ETL, workflow management, eco system integration.

May not be suitable for running ML workloads.

Some scenarios where Amazon EMR is well suited include simplifying running big data frameworks, providing the Amazon EMR highlights, product details, and pricing information, and analyzing vast amounts of data. Some scenarios where Amazon EMR is less appropriate include assisting clients with problems on their servers, and coding our clients' many servers at our data centers.

For example, when you have Apache Spark on-premise deployments, or also Apache Hadoop, and you want to move to the cloud and reduce costs, EMR is the right tool. When you have lots of ups and downs in workload levels to process data. AWS's EMR can help you by setting up flexible/scalable scenarios.

Amazon is the big player in the data game right now as it even seems to push Google out of the way in some instances. Because of this you know they treat your data well and also deal with a ton themselves. That makes them good at a comparably smaller data set like most companies have.

Amazon Elastic MapReduce is useful in cases where two conditions are met. First, that you are planning on using multiple big data tools simultaneously to analyze big data sets. And second, that you need a tool that simplifies managing big data tools. If these two conditions are met, MapReduce does a great job. The user interface is simple. The program eliminates some programming requirements. The software also makes setting up big data analyses much easier. With these benefits acknowledged, MapReduce is not a good tool for "small" data analyses, given that there are other tools that do the job quicker and much more professional output. If you're on the fence, try out MapReduce with competing "small" data tools and see if you really need big data software.

If you don't have big data ..i.e petabytes of data with terabytes of data generating every day, then don't use Hadoop. Relational databases are enough for terabytes of data. Hadoop is not well suited for transactional systems or data.

Well suited if you quickly want to setup a distributed compute platform, such as Spark. But you have to be advanced enough that you really want to separate compute from data storage. For example, for certain applications packaged solution such as MPP databases (e.g. Redshift) is much easier to set up that Spark on EMR and S3 with the appropriate file formats.

EMR is suited if the jobs are long running and doesn't really need much monitoring. EMR is really flexible in processing the data on s3 as a developer doesn't need to spend time on debugging the connections to s3 from a big data framework as most of the configuration is taken care of by Amazon. Very cheap when compared to most of the solutions on the market and the ready to go configuration at the launch time reduces the amount of time required for admin tasks. So, considering the cheap cost, processing options on s3 and scalability via adding task nodes, EMR serves a better purpose for startups considering open source and cost efficient options.

However, EMR comes with its own disadvantages. There is no proper UI to track real time jobs which is however possible with Enterprise editions like Cloudera, Hortonworks etc. EMR could provide an interface to add workbooks and code snippets in the cluster as it would reduce the time to submit the tasks. EMR also lags the potential to automatically replace unhealthy nodes.

If the person using EMR does not need much customization, like debugging or other modifications, or the data is not entirely in another cloud, then Amazon Elastic MapReduce is a better option. Otherwise, there are other open source projects available like Cloudera that are available to be used. Products like Cloudera can also be deployed in any cloud, rather than having to stick with Amazon.