Amazon EMR Information Reviews & Insights

The AWS stack is a big component of the majority of our work. When necessary, EMR is employed in a number of these settings. When we need to process a large amount of data across several EC2 servers, our DevOps team implements it. For our customers, EMR is attractive since it is far less expensive to adopt than alternative solutions, which means that the overall cost savings are substantial.

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.

We use Amazon EMR (Elastic MapReduce) to run various types of algorithms related to health like calculation of body mass index, heart rate and similar parameters on vast amounts of data. We do this for developing a prototype of a health analysis device that users can wear on their body - something like a smart watch fitness tracker.

For some clients, we have our product hosted on several AWS products, and when it comes to retrieving big volumes of data we use the Amazon EMR service. It has aided us in becoming more productive and saving time and effort. AWS is our go-to service for most of our needs,

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.

Used as spark cluster to enable Big data ETL processes. Analysists and data scientists uses clusters for adhoc querying purposes. Raw data ingestion fro. RDBMS systems , APIs, file systems etc. Used elastic feature with different node types to optimize cost. Scope of the use case is a company wide big data platform.

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

May not be suitable for running ML workloads.

We are a certified AWS partner agency, and we use a lot of the AWS stack for most of our projects. EMR is used in several of them when required. It is implemented by our DevOps team and we pretty much use it when we need to process a lot of data throughout EC2 instances. EMR is very compelling to our customers because it is easier to implement (hence less dev cost) and it is way more efficient when managing the data VS other tools, so the overall cost reduction is considerable.

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.

We use Amazon EMR for big data storage and processing. It's cluster architecture with each department having different clusters. It's great for processing and storage of large volumes of data, specifically, the data which is unstructured and generates very rapidly, like network logs.

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.

EMR is being used by our department, not the whole organization. We use it as the infrastructure on which we run Spark jobs. Those jobs are mainly used for data I/O, data processing, and machine learning applications.

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.