"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"What I like about Apache Hadoop is that it's for big data, in particular big data analysis, and it's the easier solution. I like the data processing feature for AI/ML use cases the most because some solutions allow me to collect data from relational databases, while Hadoop provides me with more options for newer technologies."
"The scalability of Apache Hadoop is very good."
"We selected Apache Hadoop because it is not dependent on third-party vendors."
"Hadoop is extensible — it's elastic."
"The performance is pretty good."
"The most important feature is its ability to handle large volumes. Some of our customers have really large volumes, and it is capable of handling their data in terms of the core volume and daily incremental volume. So, its processing power and speed are most valuable."
"The parallel load features mean that Greenplum is capable of high-volume data loading in parallel to all of the cluster segments, which is really valuable."
"It's super easy to deploy and it also supports different languages and analytics."
"A very good, open-source platform."
"The integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"From the Apache perspective or the open-source community, they need to add more capabilities to make life easier from a configuration and deployment perspective."
"The solution is very expensive."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"It requires a great deal of learning curve to understand. The overall Hadoop ecosystem has a large number of sub-products. There is ZooKeeper, and there are a whole lot of other things that are connected. In many cases, their functionalities are overlapping, and for a newcomer or our clients, it is very difficult to decide which of them to buy and which of them they don't really need. They require a consulting organization for it, which is good for organizations such as ours because that's what we do, but it is not easy for the end customers to gain so much knowledge and optimally use it."
"What could be improved in Apache Hadoop is its user-friendliness. It's not that user-friendly, but maybe it's because I'm new to it. Sometimes it feels so tough to use, but it could be because of two aspects: one is my incompetency, for example, I don't know about all the features of Apache Hadoop, or maybe it's because of the limitations of the platform. For example, my team is maintaining the business glossary in Apache Atlas, but if you want to change any settings at the GUI level, an advanced level of coding or programming needs to be done in the back end, so it's not user-friendly."
"Hadoop's security could be better."
"Extra filters would be helpful."
"The initial setup is somewhat complex and the out-of-the-box configuration requires optimization."
"They should add more analytics. Their documentation could also be improved so that I don't have to bother my co-workers and tech support so often."
Apache Hadoop is ranked 6th in Data Warehouse with 7 reviews while VMware Tanzu Greenplum is ranked 9th in Data Warehouse with 3 reviews. Apache Hadoop is rated 7.8, while VMware Tanzu Greenplum is rated 9.0. The top reviewer of Apache Hadoop writes "Has good processing power and speed and is capable of handling large volumes of data and doing online analysis". On the other hand, the top reviewer of VMware Tanzu Greenplum writes "Powerful external data integration and parallel load capabilities, with good technical support". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Snowflake, Oracle Exadata, Azure Data Factory and Teradata, whereas VMware Tanzu Greenplum is most compared with Oracle Exadata, Snowflake, Oracle Database Appliance, Amazon Redshift and Teradata. See our Apache Hadoop vs. VMware Tanzu Greenplum report.
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