Apache Hive Features and Easy User Guide

Apache Hive serves as a dedicated tool employed by data scientists and analysts to transform raw data into actionable insights, thereby unlocking its potential for informed decision-making. In this review we will be discovering all about Apache Hive.

With the increased volumes of structured and unstructured data, advanced large tools face many challenges. The tract of data encompasses not only its sheer volume but also the increasing diversity and authenticity of information. Within this landscape, Hive, operating atop Hadoop, emerges as a crucial solution to sustain the importance inherent in Big Data.

More information for Hive: Hive Project Management Software for Teamwork Optimization

What is Apache Hive?

Apache Hive is open-source data warehouse software designed to read, write, and manage large datasets  expelled from the Apache Hadoop Distributed File System (HDFS) , one aspect of a larger Hadoop Ecosystem.

With mass Apache Hive documentation and continuous updates, Apache Hive continues to innovate data processing in an ease-of-access way.

In other words: Apache Hive is an open source ETL and data warehousing framework that processes structured data on Hadoop. It facilitates reading, writing, summarizing, searching and analyzing large datasets stored in distributed storage systems using structured query languages. Hive is helpful in performing frequent data warehousing tasks such as ad-hoc-query, data encapsulation and analysis of large datasets stored in distributed file systems such as HDFS (Hadoop Distributed File System).

Apache Hive enables analysis at a massive scale and enhances fault tolerance, performance, scalability and loose coupling with its input formats. What makes Hive unique is its ability to abstract the complexity of MapReduce jobs. Instead of writing complex MapReduce jobs, we can write simple SQL-like queries, which reduces the overhead of remembering complex Java code.

Facebook created Hive to process their massive amounts of data (about 20TB per day), but it was later adopted by the Apache Software Foundation. Also, MNCs like Amazon and Netflix use it to search and analyze their data.

The History of Apache Hive

Apache Hive is an open source project that was conceived of by co-creators Adam Mills and Jackson  during their time at Facebook. Hive started as a subproject of Apache Hadoop, but has graduated to become a best—level project of its own. With the growing limitations of Apache Hadoop and Map Reduce jobs and the increasing size of data from 10s of GB a day in 2006 to 1TB/day and to 15TB/day within a few years. The engineers at Facebook were unable to run the complex jobs with ease, giving way to the creation of Hive.

Apache Hive was created to achieve two goals – an SQL-based declarative language that also allowed engineers to be able to plug in their own scripts and programs when SQL did not run, which also enabled most of engineering world (SQL Skills based) to use Hive with minimum disruption or retraining compared to others.

Second, it shifts a centralized metadata store (Hadoop based) of all the datasets in the organization. While at first developed in the walls of Facebook, Apache Hive is used and developed by other companies such as Netflix. Amazon maintains a software fork of Apache Hive accessory in Amazon Elastic MapReduce on Amazon Web Services.

Features of Apache Hive

Apache Hive supports analysis of large datasets stored in Hadoop’s HDFS and balanced file systems such as Amazon S3, Azure Blob Storage, Azure Data Lake Storage, Google Cloud Storage, and Alluxio.

It provides an SQL-like query language called HiveQL with schema-on-read and transparently converts queries to Apache Spark, MapReduce, and Apache Tez jobs. Other features of Hive include:

Hive data functions help in processing and searching large datasets. Some of the functionalities provided by these functions include string manipulation, date manipulation, type conversion, conditional operators, arithmetic functions and others.—How to Use Trello Project Management: Simple Steps for Success

How Does Apache Hive Work?

Data Ingestion

The first step is getting your data into HDFS or another supported storage system. This could involve loading data from various sources or converting existing data into a format that Hive understands.

Table Creation

Once the data is in the storage system, you create a table in Hive to define the structure of your data. This includes specifying the columns, data types, and how the data is delimited.

Metadata Storage

When you create a table, the metadata describing the table’s structure is stored in the Hive Metastore. This information is crucial for efficiently managing and querying data.

Querying Data

Users can then write queries in HiveQL to retrieve, analyze, and transform the data. Hive translates these queries into a series of MapReduce or Tez jobs that are executed on the Hadoop cluster.

Execution on Hadoop Cluster

The Hive Execution Engine takes care of translating the high-level queries into low-level operations that can be performed in parallel across the Hadoop cluster. This agrees for the efficient processing of large datasets.

Results Retrieval

Once the execution is complete, users can retrieve the results of their queries for further analysis or reporting.

Apache Hive is a powerful data warehousing and SQL-like query language tool that simplifies data analysis on large datasets. Built on top of the Hadoop Distributed File System (HDFS), Hive provides an easy-to-use interface for managing and querying structured data. In this guide, we’ll walk through the basics of how to use Apache Hive without getting lost in technical jargon.


Before diving into Hive, ensure you have Hadoop installed on your system, as Hive relies on it. Familiarize yourself with basic SQL concepts, as Hive’s query language, HiveQL, shares similarities with SQL.

Getting Started:

Launching Hive

Open your terminal and type “hive”  to enter the Hive shell.

Creating a Database

  • Start by creating a database to organize your tables. Type CREATE DATABASE dbname; where “dbname” is your desired database name.

Switching Databases

  • To work within a specific database, type USE dbname; replacing “dbname” with your chosen database.

Creating Tables

  • Define the structure of your data by creating tables. Use the following command:

CREATE TABLE table_name (

  column1 datatype,

  column2 datatype,



Replace “table_name,” “column1,” and so on with your chosen names.

Loading Dat

Load data into your table using the LOAD DATA LOCAL INPATH ‘path/to/data’ INTO TABLE table_name; command. Adjust the path and table name accordingly.

Querying Data

1. Basic SELECT Statements

  • Retrieve data from a table using the standard SQL SELECT statement:

2.Filtering Data

  • Add conditions to your SELECT statement to filter results based on specific criteria:

3.Aggregating Data

  • Use aggregate functions like COUNT, SUM, AVG, etc., to analyze data:

4.Joining Tables

  • Combine data from multiple tables using JOIN operations. For example:

Apache Hive vs. Apache Spark Compare 

An analytics framework designed to process high volumes of data across different datasets, Apache Spark provides a powerful user interface capable of supporting various languages from R to Python.

Apache Hive Features and Full User Guide

Hive provides an abstraction layer that represents the data as tables with rows, columns, and data types to ask and analyze using an SQL interface called HiveQL. Apache Hive supports ACID transactions with Apache Hive LLAP. Transactions secure consistent views of the data in an environment in which multiple users/processes are accessing the data at the same time for Create, Read, Update and Delete (CRUD) operations.

Databricks offers Delta Lake, which is similar to Hive LLAP in that it supplies ACID transactional guarantees, but it offers several other benefits to help with performance and reliability when accessing the data. Spark SQL is Apache Spark’s module for interacting with structured data illustrate as tables with rows, columns, and data types.

Spark SQL is SQL 2003 attending and uses Apache Spark as the distributed engine to process the data. In amalgamation to the Spark SQL interface, a DataFrames API can be used to interact with the data using Java, Scala, Python, and R. Spark SQL is similar to HiveQL.

Both use ANSI SQL syntax, and the majority of Apache Hive functions will run on Databricks. This covers Hive functions for date/time conversions and parsing, collections, string manipulation, mathematical operations, and conditional functions.

There are some functions fixed to Hive that would need to be converted to the Spark SQL equivalent or that don’t exist in Spark SQL on Databricks. You can hope all HiveQL ANSI SQL syntax to work with Spark SQL on Databricks.

This covers ANSI SQL aggregate and analytical functions. Hive is optimized for the Optimized Row Columnar (ORC) file format and further supports Parquet. Databricks is optimized for Parquet and Delta, however it also supports ORC. We evermore recommend using Delta, which uses open-source Parquet as the file format.

Apache Hive vs. Presto Compare

Apache Hive Features and Full User Guide

A project originally established at Facebook, Presto DB, commonly known as Presto, is a distributed SQL query engine that allows users to process and analyze petabytes of data at a rapid speed. Presto’s infrastructure supports the integration of both relational databases and non-relational databases from MySQL and Teradata to MongoDB and Cassandra.


What is Apache Hive used for?

Apache Hive is a data stow and SQL-like query language system built on top of Hadoop. It enables users to analyze, search, and summarize data on large datasets stored in Hadoop’s distributed storage, making it easier to work with big data.

What are the differences between Hadoop and Apache Hive?

A. Hadoop is a framework that facilitates big data processing across distributed storage and clusters. At the same time, Apache Hive is a data warehousing and querying tool that provides a SQL-like interface to search and manage data stored in Hadoop’s HDFS.

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