3.c) Write a short note on Apache Hive.
Answer:
Apache Hive
Apache Hive is a data warehousing tool built on top of the Hadoop framework. It provides an SQL-like query language called HiveQL for querying and managing large datasets stored in Hadoop’s HDFS. Hive simplifies the process of analyzing Big Data by abstracting the complexities of writing MapReduce jobs.
Key Features:
- SQL-Like Interface: HiveQL allows users familiar with SQL to query and analyze data without needing in-depth programming knowledge.
- Scalability: Processes massive datasets distributed across a Hadoop cluster.
- Extensibility: Supports user-defined functions (UDFs) for custom operations.
- Data Storage: Operates on structured and semi-structured data stored in HDFS, using formats like ORC, Parquet, and Avro.
- Integration: Works seamlessly with other Hadoop ecosystem tools like Spark and Pig.
Use Cases:
- Data Analysis: Summarizing and analyzing Big Data for business intelligence.
- ETL Operations: Extracting, transforming, and loading data in large-scale data pipelines.
- Reporting: Generating insights from complex datasets using HiveQL.
Hive is a powerful tool for businesses needing efficient and scalable solutions for querying and analyzing Big Data.
Access to files stored either directly in HDFS or in other data storage systems such as HBase Query execution via MapReduce and Tez (optimized MapReduce) Hive is also installed as part of the Hortonworks HDP Sandbox. To work in Hive with Hadoop, user with access to HDFS can run the Hive queries.
Simply enter the hive command. If Hive start correctly,it get a hive> prompt.
$ hive
(some messages may show up here) hive>
Hive command to create and drop the table. That Hive commands must end with a semicolon (;). hive> CREATE TABLE pokes (foo INT, bar STRING);
OK
Time taken: 1.705 seconds To see the table is created, hive> SHOW TABLES; OK
pokes
Time taken: 0.174 seconds, Fetched: 1 row(s) To drop the table,
hive> DROP TABLE pokes; OK
Time taken: 4.038 seconds