HiveQL, Almost SQL, but not quite.
April 20, 2016

HiveQL, Almost SQL, but not quite.

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Apache Hive

Hive is being used to put an SQL interface to our Hadoop cluster. This works well because most of our organization is very SQL friendly, so when introducing a new technology, such as Hadoop, the technical users are easily able to adapt to the new technology with no problem.
  • Run SQL queries to an Hadoop cluster.
  • Many different consoles can use it.
  • Users don't have to write map reduce.
  • Hive needs more SQL support.
  • Enabling more date functions.
  • Enabling more SQL table functions, such as inserting into a temp table.
  • Hive can get the job done.
  • It works well with big scheduled jobs for ETL purposes.
  • More SQL support will be added in the near future, which will lead it to be a more efficient tool.
Apache Hive was one of the first ways to query data out of a Hadoop cluster using SQL. However, there are many other tools out there that I believe will make Apache Hive a thing of the past. I have been working with in-memory data warehouses and other technologies that do not create a data bottleneck like Hive does.
Apache Hive is well suited for pulling data for reporting environments or ad-hoc querying analysis to an Hadoop cluster. I believe Apache Hive is not well suited for running large big data jobs when needing fast performance. It can be best utilized on scheduled jobs where fast performance is not required. However, this can greatly depend on how the Hadoop cluster is set up.