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 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 ability to add multiple nodes without any restriction is the solution's most valuable aspect."
"It's good for storing historical data and handling analytics on a huge amount of data."
"One valuable feature is that we can download data."
"The tool's stability is good."
"As compared to Hive on MapReduce, Impala on MPP returns results of SQL queries in a fairly short amount of time, and is relatively fast when reading data into other platforms like R."
"What comes with the standard setup is what we mostly use, but Ambari is the most important."
"Hadoop File System is compatible with almost all the query engines."
"The ease of deployment is useful so clients are up and running quickly in comparison to other products."
"It's stable and reliable."
"The most valuable feature is the ease of uploading data from multiple sources."
"I mentioned it definitely, and this is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen. If the UI and UX can be improved slightly, I believe it will go a long way toward increasing adoption and effectiveness."
"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."
"I think more of the solution needs to be focused around the panel processing and retrieval of data."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"It would be good to have more advanced analytics tools."
"It could be more user-friendly."
"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."
"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."
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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