We performed a comparison between Apache Hadoop and Oracle Autonomous Data Warehouse 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 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 scalability of Apache Hadoop is very good."
"Apache Hadoop can manage large amounts and volumes of data with relative ease, which is a feature that is beneficial."
"I liked that Apache Hadoop was powerful, had a lot of tools, and the fact that it was free and community-developed."
"One valuable feature is that we can download data."
"Hadoop is designed to be scalable, so I don't think that it has limitations in regards to scalability."
"Hadoop is extensible — it's elastic."
"We selected Apache Hadoop because it is not dependent on third-party vendors."
"The solution is self-securing. All data is encrypted and security updates and patches are applied automatically both periodically and off-cycle."
"The solution integrates well with Power BI."
"A very good integration feature that restricts access to unauthorized people."
"The performance and scalability are awesome."
"One advantage is that if you already have an Oracle Database, it easily integrates with that."
"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is probably the only feature we can improve a little bit because the terminal and coding screen on Hadoop is a little outdated, and it looks like the old C++ bio screen.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."
"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."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"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."
"Hadoop's security could be better."
"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."
"The solution lacks visibility options."
"It doesn't work well when you have unstructured data or you need online analytics. It is not as nice as Hadoop in these aspects."
"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."
"The initial setup was pretty complex. It was not easy."
"The solution could be improved by allowing for migration tools from other cloud services, including migration from Amazon Redshift, RDS, and Aurora."
Oracle Autonomous Data Warehouse is the world’s first and only autonomous database optimized for analytic workloads, including data marts, data warehouses, data lakes, and data lakehouses. With Autonomous Data Warehouse, data scientists, business analysts, and nonexperts can rapidly, easily, and cost-effectively discover business insights using data of any size and type. Built for the cloud and optimized using Oracle Exadata, Autonomous Data Warehouse benefits from faster performance and, according to an IDC report (PDF), lowers operational costs by an average of 63%.
Autonomous Database provides the foundation for a data lakehouse—a modern, open architecture that enables you to store, analyze, and understand all your data. The data lakehouse combines the power and richness of data warehouses with the breadth, flexibility, and low cost of popular open source data lake technologies. Access your data lakehouse through Autonomous Database using the world's most powerful and open SQL processing engine.
Apache Hadoop is ranked 6th in Data Warehouse with 9 reviews while Oracle Autonomous Data Warehouse is ranked 9th in Cloud Data Warehouse with 5 reviews. Apache Hadoop is rated 8.0, while Oracle Autonomous Data Warehouse is rated 8.4. 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 Oracle Autonomous Data Warehouse writes "Integrates well with other cloud services, provides great ROI and guarantees 99.995% up-time". Apache Hadoop is most compared with Microsoft Azure Synapse Analytics, Snowflake, Azure Data Factory, Oracle Exadata and AWS Lake Formation, whereas Oracle Autonomous Data Warehouse is most compared with Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake, Amazon Redshift and Vertica. See our Apache Hadoop vs. Oracle Autonomous Data Warehouse report.
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