Your Complete Guide to Apache Hive Installation on Ubuntu Linux

Apache Hive is a data warehouse infrastructure facilitates data summarization, data analysis, and data querying to manage large datasets that reside inside distributed storage system. Hive is an open source Hadoop platform that converts data queries into MapReduce jobs for quick handling of voluminous datasets and easy to execute as well.

In this blog, we will discuss on various Hive concepts and why it is important to learn Hive technology for a quick career progression in the competitive IT world.

The main highlights of blog include –

• Hive Introduction,
• Difference between Hive and SQL,
• Hive Architecture, Hive installation on Ubuntu,
• Why it is important to learn Hive Technology, and
• How will Hive training help you to grow your career?

Let us explore each of the concepts in detail one by one for your reference.

Hive Introduction

Apache Hadoop is considered as the most popular technology when it comes to handling Big Data for enterprises. Hadoop is an ocean that offers a wide array of tools and technologies to work with Big Data effectively. One of the popular tools is Apache Hive that is deployed by data researcher to work on large datasets and data queries.

Apache Hive is a data warehouse infrastructure facilitates data summarization, data analysis, and data querying to manage large datasets that reside inside distributed storage system. Hive is an open source Hadoop platform that converts data queries into MapReduce jobs for quick handling of voluminous datasets and easy to execute as well.

Apache Hive

• Data Summarization
• Data Analysis
• Data Querying

Hive supports HiveQL query language that converts SQL-like queries into MapReduce jobs for easy execution and quick data handling. As a result, Hive technology is responsible to increase the flexibility of schema design and causes effective Data Serialization and De-serialization too.

Read: Apache Spark Interview Questions and Answers for 2024

Read More: Hive Interview Questions and Answers

Difference between SQL and Hive

Hive looks very much similar to SQL but Hive is based on Hadoop platform and MapReduce operations, so there are several key differences between two.

• Hive is popular for long sequential scans and you can expect a high latency when executing queries. It signifies that Hive is not suitable for the applications that need fast execution and response time but same can be achieved with traditional RDBMS system.
• Secondly, Hive is a read-based program and it is taken inappropriate for the applications that need frequent write operations.

Hive Architecture and its major components

In this section, we will discuss on major components of Hive framework and how they work together to process Big Data more effectively as needed by the organizations.

1). Metastore

As the name suggests, metastore is the repository for metadata that is responsible to store location and schema of various data tables. It also holds information for partition metadata that allows you monitor the progress of various distributed data nodes stored in the cluster. Metastore generally works as traditional RDBMS systems that keep track of data, replicates data, and assures data recovery too.

2). Driver

The Driver works similar to a controller where HiveQL sends queries or statements. It monitors the progress of various executions and their lifecycle too. As soon as, HiveQL statement is executed, it creates metadata for the executed statement. Further, the query is managed by the MapReduce jobs and Driver collects the final query results.

3). Compiler

A ‘Compiler’ is responsible to convert HiveQL queries into MapReduce inputs that include step by step guide to execute the tasks as output is further fed to MapReduce as required.

4). Optimizer

An ‘Optimizer’ is assigned the task of splitting data into small jobs or more optimized inputs that can be quickly processed by the Hive platform. As a result, the overall scalability and efficiency of the platform are enhanced.

Read: Big Data Hadoop Developer Career Path & Future Scope

5). Executor

The executor assigned the task of executing jobs when it is compiled and optimized. Further, Executor directly interacts with the Hadoop job tracker to schedule tasks that need to run.

6). CLI, UI and Thrift Server

The Command Line Interface and User Interface submit queries and monitors processes so that users can interact with the Hive Platform whenever required. At the same time, Thrift Server enables external clients to interact with the Hive platform.

Why is Hive technology so important for organizations?

Basically, Apache Hive is a data warehouse infrastructure facilitates data summarization, data analysis, and data querying to manage large datasets that reside inside distributed storage system. Here, are some popular reasons why Hive technology is gaining immense popularity by organizations.

• The three functionalities of Hive platform help to improve overall the productivity of a developer and increasing cost effectiveness too.
• Hive is very much similar to SQL database platforms and it was actually needed by large enterprises collecting voluminous data almost every day.
• Hive has plenty of user-defined functions that make it easy to design interactive applications that are user-friendly.
• Further, Hive can be connected to various Hadoop packages too like that makes data processing much easier and faster.
• Hive is a highly flexible platform where plenty of commodities can be added based on the requirements without degrading the performance of an application.

Hive Installation on Ubuntu

Step 1). Hive Installation 

• First of all, you should Download the Hive. 
• Now open ~/.bashrc and set the environment variables PATH and HIVE_HOME to point the installation.

• At this step, you need to activate the Hive setting as shown below in the screenshot.

Step 2). Hive Warehouse Directory Creation 

• Hive is based on Hadoop platform, so this is necessary to include Hadoop in PATH.
• Now use HDFS as well to create the Hive Warehouse Directory before you start creating a table in Hive.

Step 3). Hive Configuration 

• To configure Hive with Hadoop, you should use the following command to get your work done.

• Use the following command to edit the “hive-env.sh” file –

With these steps, Hive installation is complete on Ubuntu Linux and you just need an external database to configure Metastore.

Read: Hadoop Developer And Architect: Roles and Responsibilities

Why is it important to learn Hive technology?

Hive learning allows you to work with Hadoop in a very efficient way. Hive has the capability to manage large datasets that are distributed across the network and users are able to connect freely with Command Line Tools and JDBC driver. Hive is base don Hadoop platform and plenty of tools from Hadoop can be integrated with Hive platform to make it even more powerful and useful.

How will Hive learning give a right boost to your career?

Apache Hive is just the wonderful choice to master and it is popular across industries especially those handling voluminous every day. Most of the industries seeking for the right skills that can efficiently manage their Big data stored across the network and meaningful decisions can also be taken accordingly. Thankfully, Big data experts or Hive specialists enjoy higher salaries worldwide, so this would be a perfect choice learning Hive from reputed learning platform to give a right boost to your career.

Final Words:

With this blog, you clearly understood the concept the Apache Hive, its architecture, installation and why learning Hive is so important? Once you have gone through the blog, the time has come to take the right decision for your career. Let JanBask Training Hive Hadoop training and certification help you to boost your career.

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