Apache Hive Faster and Can handle large sets of data
December 05, 2017

Apache Hive Faster and Can handle large sets of data

Tejaswar Rao | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Apache Hive

We use hive for analyzing big sets of data and for developing rule-based applications. And also for visualization tools and where we query on large sets of data using hive for desired visualization. Hive is fast and also can be imported/exported using other hadoop components. We can use SQL to access data in hive and with no need to learn a new language.
  • Can query on large sets of data and fast when compared to RDBMS
  • Can use SQL for data access and no need to learn new language
  • Can write custom functions (UDF) with python and also Java
  • Security roles for different users should be implemented
  • All the functionalities of SQL should be available
  • Positive impact for faster response time compared to other products
  • Can handle large sets of data and complex queries
  • Faster response time and also can handle complex analytical queries
  • Can able to write custom function using python and hive
  • Able to connect using hadoop components and also using R
  • Can handle different data formats
  • Can use Structured Query language to access the data
  1. To query on large sets of data
  2. Faster access compared to traditional Databases
  3. OLAP projects
  4. Data Warehousing project
  5. To get insights from GigaByte's or TeraByte's of data
  6. Rule based projects and also to identify the patterns in data
  7. For applying transformations on large sets of data
  8. Faster response time than traditional databases
  9. Also able to get connected with hadoop components
  10. For complex analytical and different types of data formats