Spectacular SQL-like interface for accessing Hadoop
April 11, 2022
Spectacular SQL-like interface for accessing Hadoop
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Apache Hive
To manage and view Apache Hadoop data in a SQL-like format To be able to query databases across the organization, quickly To query data for the purpose of using on Spark projects To save queries
- Easy-to-use, interactive modern layout
- Easy to organize data and view tables and views from across the organization
- Fast speed for most queries
- Some queries, particularly complex joins, are still quite slow and can take hours
- Previous jobs and queries are not stored sometimes
- Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
- Sometimes, directories and tables don't load properly which causes confusion
- Ability to query Hadoop data with SQL commands
- Ability to see and share views and tables
- Ability to save and access past jobs and queries
- Good ROI for being able to access data easily across the network, we have large amounts of data and this is a good system to access it
- Good ROI for being easy to learn how to use for new employees, not much time spent which saves costs
- Good ROI for being able to integrate with Spark and other applications, hence data can be analyzed through programs
Apache Spark is similar in the sense that it too can be used to query and process large amounts of data through its Dataframe interface. Hive is better for short-term querying while Spark is better for persistent and long-term analysis. Another product is Impala. For our purposes, Impala and Hive were similar, but in general, Impala is better for real-time analysis.
Do you think Apache Hive delivers good value for the price?
Yes
Are you happy with Apache Hive's feature set?
Yes
Did Apache Hive live up to sales and marketing promises?
Yes
Did implementation of Apache Hive go as expected?
Yes
Would you buy Apache Hive again?
Yes