With Apache Hive, you can enter the world of Big Data
Updated July 06, 2022
With Apache Hive, you can enter the world of Big Data
Score 8 out of 10
Vetted Review
Verified User
Overall Satisfaction with Apache Hive
On-premises large data processing is handled by Apache Hive, which is running on Cloud ERA Servers. In order to use Apache Hive, you must have a distributed system that is query efficient and can perform queries quicker with parallel execution. Metrics like user information and purchase history are stored in HDFS and then accessed using queries built on top of Hive using Apache Hive.
- Reduce-based query language with a simple query language.
- Parallelism across a distributed system is provided.
- All cloud platforms have access to a tabular format and interfaces.
- Due to the shuffled data, complex joins may take a long time to complete.
- Execution is dependent on external storage and memory.
- Maintaining inquiries on the premise is simple.
- Hive may be rapidly learned by anyone who is already familiar with SQL.
- Parallel and distributed processing.
- Improved performance compared to a database management system.
- HDFS-based distributed processing greatly improves scalability.
- Improved query performance compared to oracle databases.
To query a huge, distributed dataset, Apache Hive was built by Facebook. Unlike Apache Hive, Apache Spark is an in-memory computation engine, which is why it is significantly quicker than Apache Hive at querying large amounts of data. In contrast to Apache HBase, Apache Hive is better suited for dealing with structured data stored on HDFS.
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