Overall Satisfaction with Apache Hive
I have used Hive at an enterprise company where I interned. It was being used by the IT department to improve analysis of large datasets stored in the company's Hadoop HDFS. It was also being used because of its support for HiveQL which is a SQL like language enabling queries on large datasets. It also reduced the learning curve for handling big data because of HiveQL's similarity to SQL.
- Supports SQL like queries
- Various storage types including RCFile, HBase, ORC, etc.
- Supports indexing for acceleration
- HiveQL does not have all the features of SQL
- No support for transactions
- Reduced learning curve for new trainees and users
- Support for other client server databases provided improved compatibility of systems
Hive is very well suited for large enterprise businesses that rely on Hadoop for efficient processing of big data in a distributed cluster. HiveQL also brings familiarity of SQL which speeds up the learning process for new users. However, Hive is not an ideal option for a business where data is frequently changing and dynamic.
Using Apache Hive
Hive's support SQL like queries improves its usability since almost every potential user of Hive would have had experience with SQL.