We performed a comparison between Apache Hadoop and SAP IQ 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."Since both Apache Hadoop and Amazon EC2 are elastic in nature, we can scale and expand on demand for a specific PoC, and scale down when it's done."
"One valuable feature is that we can download data."
"Most valuable features are HDFS and Kafka: Ingestion of huge volumes and variety of unstructured/semi-structured data is feasible, and it helps us to quickly onboard a new Big Data analytics prospect."
"Its integration is Hadoop's best feature because that allows us to support different tools in a big data platform."
"The scalability of Apache Hadoop is very 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."
"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."
"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."
"Unbeatable speed and compression with a colummn-structured relational database."
"The column-based technologies (basically all the database for ITP) are used for SAP IQ. It is used as a column-based solution."
"Valuable features for us include the compression, speed, fast response time, and easy object maintenance."
"The primary benefit of SAP IQ is its ability to limit the expansion of the costly SAP HANA database, which has limited storage capacity. This necessitates a form of data management that involves moving data from SAP HANA to SAP NLS, which is essentially archiving. This allows us to retain access to the data via a link whenever it is required."
"Columnar storage allows high compression, high load rates and high query performance."
"It is very robust for ad hoc DW queries and its columnar compression is unique and valuable."
"The solution is very expensive."
"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."
"We would like to have more dynamics in merging this machine data with other internal data to make more meaning out of it."
"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."
"General installation/dependency issues were there, but were not a major, complex issue. While migrating data from MySQL to Hive, things are a little challenging, but we were able to get through that with support from forums and a little trial and error."
"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 load optimization capabilities of the product are an area of concern where improvements are required."
"Real-time data processing is weak. This solution is very difficult to run and implement."
"I think the universe should be part of the Sybase IQ tool set."
"The room for improvement would be the marketing of the product, because this product is much better than advertised."
"The organization who owns the product does not support it well and appears not to be doing significant development for the future."
"Concurrency and functional error messaging."
"Multiplex is very problematic. There are consistency problems in the metadata, meaning it is possible to lose metadata consistency. You should make sure you have healthy backups."
"The solution works best when combined with other SAP solutions. If the environment has other systems other options might be better."
Apache Hadoop is ranked 5th in Data Warehouse with 32 reviews while SAP IQ is ranked 16th in Data Warehouse with 17 reviews. Apache Hadoop is rated 7.8, while SAP IQ is rated 8.0. 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 SAP IQ writes "Easy to use, highly stable, but integration could improve". Apache Hadoop is most compared with Azure Data Factory, Microsoft Azure Synapse Analytics, Oracle Exadata, Snowflake and Teradata, whereas SAP IQ is most compared with Snowflake, SAP HANA, SQL Server, SAP BW4HANA and SAP Adaptive Server Enterprise. See our Apache Hadoop vs. SAP IQ 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.
