Difference between Hive and HBase
Hive: Hive is a data warehousing package built on the top of Hadoop. It is mainly used for data analysis. It generally targets users already comfortable with Structured Query Language (SQL). It is very similar to SQL and is called Hive Query Language (HQL). Hive manages and queries structured data. Moreover, hive abstracts the complexity of Hadoop. Hive was developed by Facebook in 2007 to handle the massive amount of data. It does not support:
- Not a full database.
- Not a real-time processing system.
- Not SQL-92 compliant.
- Does not provide row-level inserts, updates, or deletes.
- Doesn’t support transactions and limited sub-query support.
- Query optimization in an evolving stage.
Hbase: HBase is a column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS). It is well suited for sparse data sets, which are common in many big data use cases. It is an open-source, distributed database developed by Apache software foundations. Initially, it was named Google Big Table, afterwards, it was re-named HBase and is primarily written in Java. It can store a massive amount of data from terabytes to petabytes. It is built for low-latency operations and is used extensively for reading and writing operations. It stores a large amount of data in the form of tables.
Difference between Hive and HBase:
| S. No. | Parameters | Hive | HBase |
|---|---|---|---|
| 1. | Basics | Hive is a query engine that uses queries that are mostly similar to SQL queries. | It is Data storage, particularly for unstructured data. |
| 2. | Used for | It is mainly used for batch processing (that means OLAP-based). | It is extensively used for transactional processing (that means OLTP). |
| 3. | Processing | It cannot be used for real-time processing since immediate analysis results are unable to obtain. In other words, the operations in Hive require batch processing, they normally take a long time to complete. | It can be used to process data in real-time. Transactional operations are faster than non-transactional operations ( since HBase stores data in the form of key-value pairs). |
| 4. | Queries | It is used only for analytical queries. It is mostly used to analyze Big Data. | It is used for real-time querying. It is mostly used to query Big Data. |
| 5. | Runs on | Hive runs on the top of Hadoop. | HBase runs on the top of HDFS (Hadoop Distributed File System). |
| 6. | Database | Apache Hive is not a database. | It supports the NoSQL database. |
| 7. | Schema | It has a schema model. | It is free from the schema model. |
| 8. | Latency | Made for high latency operations as batch processing takes time. | Made for low-level latency operations. |
| 9. | Cost | It is expensive as compared to HBase. | It is cost-effective as compared to Hive. |
| 10. | Query Language | Hive uses HQL (Hive Query Language). | To conduct CRUD (Create, Read, Update, and Delete) activities, HBase does not have a specialized query language. HBase includes a Ruby-based shell where you can use Get, Put, and Scan functions to edit your data. |
| 11. | Level of Consistency | Eventual consistency | Immediate consistency |
| 12. | Secondary Indexes | It does not support Secondary Indexes. | It supports Secondary Indexes. |
| 13. | Example | Hubspot |
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