We performed a comparison between IBM Analytics Engine and Spark SQL based on real PeerSpot user reviews.
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop."The best part was that we could make minor changes in the way we were bifurcating the data, even at a very small scale. The accuracy of conversion was also very high."
"One of Spark SQL's most beautiful features is running parallel queries to go through enormous data."
"The performance is one of the most important features. It has an API to process the data in a functional manner."
"The stability was fine. It behaved as expected."
"The team members don't have to learn a new language and can implement complex tasks very easily using only SQL."
"The speed of getting data."
"Offers a variety of methods to design queries and incorporates the regular SQL syntax within tasks."
"This solution is useful to leverage within a distributed ecosystem."
"Spark SQL's efficiency in managing distributed data and its simplicity in expressing complex operations make it an essential part of our data pipeline."
"One area for improvement would be the initial setup stage, which took longer than expected."
"Anything to improve the GUI would be helpful."
"Being a new user, I am not able to find out how to partition it correctly. I probably need more information or knowledge. In other database solutions, you can easily optimize all partitions. I haven't found a quicker way to do that in Spark SQL. It would be good if you don't need a partition here, and the system automatically partitions in the best way. They can also provide more educational resources for new users."
"I've experienced some incompatibilities when using the Delta Lake format."
"It would be useful if Spark SQL integrated with some data visualization tools."
"The solution needs to include graphing capabilities. Including financial charts would help improve everything overall."
"It would be beneficial for aggregate functions to include a code block or toolbox that explains its calculations or supported conditional statements."
"There are many inconsistencies in syntax for the different querying tasks."
"In the next release, maybe the visualization of some command-line features could be added."
IBM Analytics Engine is ranked 8th in Hadoop with 1 review while Spark SQL is ranked 4th in Hadoop with 14 reviews. IBM Analytics Engine is rated 8.0, while Spark SQL is rated 7.8. The top reviewer of IBM Analytics Engine writes " Good solution for small and medium-sized businesses and highly stable". On the other hand, the top reviewer of Spark SQL writes "Offers the flexibility to handle large-scale data processing". IBM Analytics Engine is most compared with HPE Ezmeral Data Fabric, whereas Spark SQL is most compared with Apache Spark, IBM Db2 Big SQL, HPE Ezmeral Data Fabric, SAP HANA and Netezza Analytics.
See our list of best Hadoop vendors.
We monitor all Hadoop 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.
