Hive Tutorial for Beginners: Learn with Examples in 3 Days

Hive Tutorial Summary

Apache Hive helps with querying and managing large datasets real fast. It is an ETL tool for the Hadoop ecosystem. In this Apache Hive tutorial for beginners, you will learn Hive basics and important topics like HQL queries, data extractions, partitions, buckets, and so on. This Hive tutorials series will help you learn Hive concepts and basics.

What should I know?

To learn this Hive query tutorial, you need basic knowledge of SQL, Hadoop and knowledge of other databases will be of an additional help.

Hive Course Syllabus


👉 Lesson 1 What is Hive? — Architecture & Modes
👉 Lesson 2 Download & Install HIVE — How to Download & Install HIVE on Ubuntu
👉 Lesson 3 HIVE Metastore Configuration — Why to Use MySQL?
👉 Lesson 4 Hive Data Types — Create & Drop Database in Hive

Advanced Stuff

👉 Lesson 1 Hive Create Table — Types and its Usage
👉 Lesson 2 Hive Partitions & Buckets — Learn with Example
👉 Lesson 3 Hive Indexes and View — Learn with Example
👉 Lesson 4 Hive Queries — Learn with Example
👉 Lesson 5 Hive Join & SubQuery Tutorial — Learn with Example
👉 Lesson 6 Hive Query Language Tutorial — Built-in Operators
👉 Lesson 7 Hive Function — Built-in & User Defined Functions
👉 Lesson 8 Hive ETL — Loading JSON, XML, Text Data Examples

Introduction to Hive

Hive evolved as a data warehousing solution built on top of Hadoop Map-Reduce framework.

The size of data sets being collected and analyzed in the industry for business intelligence is growing and in a way, it is making traditional data warehousing solutions more expensive. Hadoop with MapReduce framework, is being used as an alternative solution for analyzing data sets with huge size. Though, Hadoop has proved useful for working on huge data sets, its MapReduce framework is very low level and it requires programmers to write custom programs which are hard to maintain and reuse. Hive comes here for rescue of programmers.

Hive engine compiles these queries into Map-Reduce jobs to be executed on Hadoop. In addition, custom Map-Reduce scripts can also be plugged into queries. Hive operates on data stored in tables which consists of primitive data types and collection data types like arrays and maps.

Hive comes with a command-line shell interface which can be used to create tables and execute queries.

Hive query language is similar to SQL wherein it supports subqueries. With Hive query language, it is possible to take a MapReduce joins across Hive tables. It has a support for simple SQL like functions– CONCAT, SUBSTR, ROUND etc., and aggregation functions– SUM, COUNT, MAX etc. It also supports GROUP BY and SORT BY clauses. It is also possible to write user defined functions in Hive query language.

What is Hive?

Apache Hive is a data warehouse framework for querying and analysis of data stored in HDFS. It is developed on top of Hadoop. Hive is an open-source software to analyze large data sets on Hadoop. It provides SQL-like declarative language, called HiveQL, to express queries. Using Hive-QL, users associated with SQL can perform data analysis very easily.

Hive Vs Map Reduce

Prior to choosing one of these two options, we must look at some of their features.

While choosing between Hive and Map reduce following factors are taken in consideration;

  • Type of Data
  • Amount of Data
  • Complexity of Code

Hive Vs Map Reduce?

Feature Hive Map Reduce
Language It Supports SQL like query language for interaction and for Data modeling
  • It compiles language with two main tasks present in it. One is map task, and another one is a reducer.
  • We can define these task using Java or Python
Level of abstraction Higher level of Abstraction on top of HDFS Lower level of abstraction
Efficiency in Code Comparatively lesser than Map reduce Provides High efficiency
Extent of code Less number of lines code required for execution More number of lines of codes to be defined
Type of Development work required Less Development work required More development work needed
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