{"id":251,"date":"2016-08-22T11:40:45","date_gmt":"2016-08-22T11:40:45","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=251"},"modified":"2016-08-22T11:40:45","modified_gmt":"2016-08-22T11:40:45","slug":"apache-hive-tutorial","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/apache-hive-tutorial\/","title":{"rendered":"Apache Hive – In Depth Hive Tutorial for Beginners"},"content":{"rendered":"

Apache Hive<\/strong>\u00a0is an open source data warehouse system built on top of Hadoop<\/strong> Haused for querying and analyzing large datasets stored in Hadoop files. It process structured and semi-structured data in Hadoop.<\/p>\n

This Apache Hive tutorial explains the basics of Apache Hive & Hive history in great details. In this hive tutorial, we will learn about the need for a hive and its characteristics. This Hive guide also covers internals of Hive architecture, Hive Features and Drawbacks of Apache Hive.<\/p>\n

So, let’s start Apache Hive Tutorial.<\/p>\n

What is Hive?<\/h2>\n

Apache Hive<\/strong> is an open source data warehouse system built on top of Hadoop Haused for querying and analyzing large datasets stored in Hadoop files.<\/p>\n

Initially, you have to write complex\u00a0Map-Reduce<\/strong>\u00a0jobs, but now with the help of the Hive, you just need to submit merely SQL<\/strong> queries. Hive is mainly targeted towards users who are comfortable with SQL.<\/p>\n

Hive use language called HiveQL<\/strong> (HQL), which is similar to SQL. HiveQL automatically translates SQL-like queries into MapReduce jobs.<\/p>\n

Hive abstracts the complexity of Hadoop. The main thing to notice is that there is no need to learn java for Hive.<\/p>\n

The Hive generally runs on your workstation and converts your SQL query into a series of jobs for execution on a Hadoop cluster<\/strong>. Apache Hive organizes data into tables. This provides a means for attaching the structure to data stored in HDFS<\/strong>.<\/p>\n

Apache History Hive<\/h2>\n

Data Infrastructure Team at Facebook developed Hive. Apache Hive is also one of the technologies that are being used to address the requirements at Facebook. It is very popular with all the users internally at Facebook.<\/p>\n

It is being used to run thousands of jobs on the cluster with hundreds of users, for a wide variety of applications.<\/p>\n

Apache Hive-Hadoop cluster at Facebook stores more than 2PB of raw data. It regularly loads 15 TB of data on a daily basis.<\/p>\n

Now it is being used and developed by a number of companies like Amazon, IBM, Yahoo, Netflix, Financial Industry Regulatory Authority (FINRA) and many others.<\/p>\n

Why Apache Hive?<\/h2>\n

Let’s us now discuss the need of Hive-
\nFacebook had faced a lot of challenges before the implementation of Apache Hive. Challenges like the size of the data being generated increased or exploded, making it very difficult to handle them. The traditional RDBMS<\/strong> could not handle the pressure.<\/p>\n

As a result, Facebook was looking out for better options. To overcome this problem, Facebook initially tried using MapReduce<\/strong>. But it has difficulty in programming and mandatory knowledge in SQL, making it an impractical solution.<\/p>\n

Hence, Apache Hive allowed them to overcome the challenges they were facing.<\/p>\n

With Apache Hive, they are now able to perform the following:<\/p>\n