We performed a comparison between Apache Hadoop and Aster Data Map Reduce based on real PeerSpot user reviews.
Find out in this report how the two Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The scalability of Apache Hadoop is very good."
"Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform."
"One valuable feature is that we can download data."
"The performance is pretty good."
"It's primarily open source. You can handle huge data volumes and create your own views, workflows, and tables. I can also use it for real-time data streaming."
"Hadoop is extensible — it's elastic."
"The tool's stability is good."
"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."
"The most valuable feature is the ease of uploading data from multiple sources."
"The ease of deployment is useful so clients are up and running quickly in comparison to other products."
"It's stable and reliable."
"It would be helpful to have more information on how to best apply this solution to smaller organizations, with less data, and grow the data lake."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"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."
"There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution."
"I would like to see more direct integration of visualization applications."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"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."
"The price could be better. I think we would use it more, but the company didn't want to pay for it. Hortonworks doesn't exist anymore, and Cloudera killed the free version of Hadoop."
"It is hard for some of our users to set up rules for cleansing and transforming data, so this is something that could be improved."
"From my perspective, it would be good if they gave better ITIN/R plugins to use the data for AI modeling, or data science modeling. We can do it now; however, it could be more elegant in terms of interfacing."
"There are some ways that the handling of unstructured data could be improved."
Apache Hadoop is ranked 5th in Data Warehouse with 33 reviews while Aster Data Map Reduce is ranked 19th in Data Warehouse with 3 reviews. Apache Hadoop is rated 7.8, while Aster Data Map Reduce is rated 7.4. 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". On the other hand, the top reviewer of Aster Data Map Reduce writes "Has good base product functionality of data storage and analytics but there should be an option to use it on the cloud ". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas Aster Data Map Reduce is most compared with . See our Apache Hadoop vs. Aster Data Map Reduce report.
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We monitor all 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.
