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."Data ingestion: It has rapid speed, if Apache Accumulo is used."
"The tool's stability is good."
"Two valuable features are its scalability and parallel processing. There are jobs that cannot be done unless you have massively parallel processing."
"High throughput and low latency. We start with data mashing on Hive and finally use this for KPI visualization."
"Initially, with RDBMS alone, we had a lot of work and few servers running on-premise and on cloud for the PoC and incubation. With the use of Hadoop and ecosystem components and tools, and managing it in Amazon EC2, we have created a Big Data "lab" which helps us to centralize all our work and solutions into a single repository. This has cut down the time in terms of maintenance, development and, especially, data processing challenges."
"Hadoop is extensible — it's elastic."
"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."
"It's good for storing historical data and handling analytics on a huge amount of data."
"The column-based technologies (basically all the database for ITP) are used for SAP IQ. It is used as a column-based solution."
"The product is easy to learn."
"Unbeatable speed and compression with a colummn-structured relational database."
"Columnar storage allows high compression, high load rates and high query performance."
"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."
"Valuable features for us include the compression, speed, fast response time, and easy object maintenance."
"It is very robust for ad hoc DW queries and its columnar compression is unique and valuable."
"The initial setup is easy."
"In the next release, I would like to see Hive more responsive for smaller queries and to reduce the latency."
"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 main thing is the lack of community support. If you want to implement a new API or create a new file system, you won't find easy support."
"There is a lack of virtualization and presentation layers, so you can't take it and implement it like a radio solution."
"The stability of the solution needs improvement."
"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."
"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 integration with Apache Hadoop with lots of different techniques within your business can be a challenge."
"The solution works best when combined with other SAP solutions. If the environment has other systems other options might be better."
"Concurrency and functional error messaging."
"The tool gets stuck sometimes."
"There is very little documentation available."
"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."
"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."
"I think the universe should be part of the Sybase IQ tool set."
Apache Hadoop is ranked 5th in Data Warehouse with 34 reviews while SAP IQ is ranked 17th in Data Warehouse with 19 reviews. Apache Hadoop is rated 7.8, while SAP IQ is rated 7.8. 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 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 AWS Lake Formation, whereas SAP IQ is most compared with Snowflake, SQL Server, SAP HANA, SAP BW4HANA and SAP Adaptive Server Enterprise. See our Apache Hadoop vs. SAP IQ report.
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