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."We selected Apache Hadoop because it is not dependent on third-party vendors."
"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."
"It's good for storing historical data and handling analytics on a huge amount of data."
"The most valuable features are powerful tools for ingestion, as data is in multiple systems."
"Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform."
"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."
"Hadoop is extensible — it's elastic."
"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."
"The most valuable feature is the ease of uploading data from multiple sources."
"It's stable and reliable."
"The ease of deployment is useful so clients are up and running quickly in comparison to other products."
"Hadoop's security could be better."
"I think more of the solution needs to be focused around the panel processing and retrieval of data."
"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."
"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."
"In certain cases, the configurations for dealing with data skewness do not make any sense."
"Based on our needs, we would like to see a tool for data visualization and enhanced Ambari for management, plus a pre-built IoT hub/model. These would reduce our efforts and the time needed to prove to a customer that this will help them."
"I would like to see more direct integration of visualization applications."
"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."
"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.
