Problems Addressed by Apache Hive
Following are the Problems Addressed by Apache Hive.
- Facebook was facing the problem of handling huge amount of various data(structured, semi structured data), is addressed by Apache hive.
- Hive made easy for developers in writing code for complex Hadoop MapReduce jobs for ad-hoc requirements becuase HQL is very easy quary language.
- HQL(hive query language) is very easy for the SQL developers to learn and implement Hive Queries since HQL is similar to SQL.
- Hive reduces the complexity of MapReduce by providing an interface where the user can submit SQL queries.
- File can be accessed by different data stores(storing and managing collection of data) such as HDFS and HBase.
- It Process structured and semi-structured(CSV, XML and JSON) data.
- Hive supports different file formats like textfile, sequencefile, orc and rcfile (Record Columnar File)
- It supports writing, reading and managing huge volumes of datasets stored in a distributed environment using SQL.
- Hive supports ACID transaction: Atomicity, Consistency, Isolation, and Durability. ACID transactions are provided at the row levels, those are Insert, Delete, and Update options so that Hive supports ACID transaction
- Support to Analyses statical data(data cannot change) .
- Hive is built for OLAP(online analytical process) that is real time reporting of data.
- Loading of data is fast in hive because of read schema(schema does’t not verify loading data).
- Hive is used for creating reports in bigdata.
- Hive supports to works on the server side.
- Apache hive is combined with the HBase for querying the data in HBase.
- Apache hive supports Hue i.e Web interface for analyzing data.
Above Problems Addressed by Apache Hive and now Apache Hive going famous day by day as easy to learn and sql developer also can learn it easily.