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 Cloud 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."
"It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database."
"I liked that Apache Hadoop was powerful, had a lot of tools, and the fact that it was free and community-developed."
"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."
"Data ingestion: It has rapid speed, if Apache Accumulo is used."
"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."
"The performance is pretty good."
"A very good integration feature that restricts access to unauthorized people."
"With Oracle Autonomous Data Warehouse, things are much simpler. Creating a structure, initializing the servers, extending the servers, those are all things that are very, very easy. That's the main reason we use it."
"The solution integrates well with Power BI."
"I loved the simplicity of loading the data and simply relying on the self-tuning capabilities of ADW."
"Self-patching and runs machine-learning across its logs all the time"
"It provides Transparent Data Encryption (TDE) capabilities by default to address data security issues."
"I really like the auto-tuning, auto-scaling, and the automatic load balancing and query tuning in the system."
"The performance and scalability are awesome."
"The key shortcoming is its inability to handle queries when there is insufficient memory. This limitation can be bypassed by processing the data in chunks."
"The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment."
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it."
"The solution is not easy to use. The solution should be easy to use and suitable for almost any case connected with the use of big data or working with large amounts of data."
"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."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency."
"It is very important the integration with other platforms be made to be as easy as it is with an on-premises deployment."
"My main suggestion for Oracle is the configuration and key values that come for JSON files. When we create a table, especially if you see in our RedShift or some other stuff, if I create a table on top of a JSON file with multiple array columns or superset columns, those column values create some difficulty in Oracle."
"Ease of connectivity could be improved."
"One of the major problem is creating custom tablespace. The ADB serverless option doesn't support custom tablespace creation, which could cause issues during on-premise database migration that requires specifically named tablespace. There should be an option to create customized tablespace."
"A lot of the tools that were previously there have now been taken away."
"The solution lacks visibility options."
"They should make the solution more user-friendly."
"Sometimes the solution works differently between the cloud and on-premises. It needs to be more consistent and predictable."
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Apache Hadoop is ranked 5th in Data Warehouse with 32 reviews while Oracle Autonomous Data Warehouse is ranked 10th in Cloud Data Warehouse with 16 reviews. Apache Hadoop is rated 7.8, while Oracle Autonomous Data Warehouse is rated 8.6. The top reviewer of Apache Hadoop writes "A file system for data collection that contains needed information and files". On the other hand, the top reviewer of Oracle Autonomous Data Warehouse writes "A tool for data warehousing that offers scalability, stability, and ease of setup". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas Oracle Autonomous Data Warehouse is most compared with Oracle Exadata, Snowflake, Microsoft Azure Synapse Analytics, BigQuery and Vertica. See our Apache Hadoop vs. Oracle Autonomous Data Warehouse report.
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