We performed a comparison between Amazon EMR and Apache Hadoop based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We are using applications, such as Splunk, Livy, Hadoop, and Spark. We are using all of these applications in Amazon EMR and they're helping us a lot."
"One of the valuable features about this solution is that it's managed services, so it's pretty stable, and scalable as much as you wish. It has all the necessary distributions. With some additional work, it's also possible to change to a Spark version with the latest version of EMR. It also has Hudi, so we are leveraging Apache Hudi on EMR for change data capture, so then it comes out-of-the-box in EMR."
"The initial setup is straightforward."
"The solution is pretty simple to set up."
"In Amazon EMR it is easy to rebuild anything, easy to upgrade and has good fault tolerance."
"The initial setup is pretty straightforward."
"It has a variety of options and support systems."
"The solution is scalable."
"The most valuable features are the ability to process the machine data at a high speed, and to add structure to our data so that we can generate relevant analytics."
"Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges."
"The solution is easy to expand. We haven't seen any issues with it in that sense. We've added 10 servers, and we've added two nodes. We've been expanding since we started using it since we started out so small. Companies that need to scale shouldn't have a problem doing so."
"Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"The ability to add multiple nodes without any restriction is the solution's most valuable aspect."
"Hadoop File System is compatible with almost all the query engines."
"Hadoop is extensible — it's elastic."
"There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange."
"The most complicated thing is configuring to the cluster and ensure it's running correctly."
"There is no need to pay extra for third-party software."
"There is room for improvement in pricing."
"We don't have much control. If we have multiple users, if they want to scale up, the cost will go and increase and we don't know how we can restrict that price part."
"The initial setup was time-consuming."
"The problem for us is it starts very slow."
"As people are shifting from legacy solutions to other technologies, Amazon EMR needs to add more features that give more flexibility in managing user data."
"It would be good to have more advanced analytics tools."
"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"The solution needs a better tutorial. There are only documents available currently. There's a lot of YouTube videos available. However, in terms of learning, we didn't have great success trying to learn that way. There needs to be better self-paced learning."
"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."
"Since it is an open-source product, there won't be much support."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
Amazon EMR is ranked 8th in Cloud Data Warehouse with 20 reviews while Apache Hadoop is ranked 6th in Data Warehouse with 34 reviews. Amazon EMR is rated 7.8, while Apache Hadoop is rated 7.8. The top reviewer of Amazon EMR writes "Provides efficient data processing features and has good scalability ". On the other hand, the top reviewer of Apache Hadoop writes "Handles huge data volumes and create your own workflows and tables but you need to have deeper knowledge". Amazon EMR is most compared with Snowflake, Cloudera Distribution for Hadoop, Azure Data Factory, Amazon Redshift and Apache Spark, whereas Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata. See our Amazon EMR vs. Apache Hadoop report.
See our list of best Cloud Data Warehouse vendors.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
