Difference between Pig and Hive

There is no limit to the amount of data that may be generated nowadays. As an example, it might be in the form of a report, email, photo, or video. Big Data is the collective term for all of stuff. Traditional databases can't manage the volume of data in this system, as its name indicates. Hadoop is a framework that may be used for this purpose. To handle and analyze all of this Big Data, Hadoop turns to Hive and Pig. Users also have a lot of questions: Is one better than the other, and if so, when? In this Hive vs. Pig post, we answer these questions.


Day in and day out, without even realizing we are creating loads and loads of data via the Social Media platforms like Facebook, LinkedIn and etc. With the advent of such platforms, data is growing exponentially with such huge volumes. To understand the data that gets created on a daily basis, the categorization of this data is very important –

Structured Data: The data that can be stored in a database, with a specific set of columns in an organized manner.

Semi-structured Data: The data that can be partially saved on to a database and the rest of the data has to reside on a different means of storage. Example, XML records that can be partially saved on to a database.

Unstructured Data: Any data that doesn’t fit in the first two categories of data will be qualified to be coined as unstructured data (which is also commonly referred to as “Big Data”)


Accelerate your career with Hadoop Training and become an expert in Apache Hadoop.     


Comparison Between Pig and Hive

What is Big Data?

With the necessary details and the introduction provided, we can now safely introduce the framework named Apache Hadoop as the framework that is used for processing the Big Data. It is also a very famous framework that serves the need of storing, analyzing bigger volumes of Big Data. This is the latest buzzword that you would hear that deals with analyzing, processing TB’s (Terabytes), PB’s (Petabytes) or even ZB’s (Zettabytes) of data. The Hadoop ecosystem typically serves the following two purposes:

Storing enormous amounts of data:

This is attained by adding more and more nodes to the Hadoop cluster. Generally, the block size within the Hadoop Distributed File System (HDFS) is either 64MB or 128MB as against the usual of 128KB.

Bringing computation to data:

In a very traditional way, the data is extracted to the clients for the actual computational processes. But the opposite works fine with Apache Hadoop as the data stored is such huge that it is more efficient to the opposite of the traditional way – mainly happens by the MapReduce jobs.

The Hadoop ecosystem contains more than 20 components to it are complete fully functional working and we will look at the only two components that matter for this article – Apache Pig & Apache Hive.

Checkout Hadoop Interview Questions

What is Apache Hadoop Hive?

Hive is very much similar to a SQL Interface in Hadoop and all the DML operations of a traditional RDBMS be compared with the Hive specific functions like - Hive select, where, group by, and order by clauses. Data is stored in the HBase component within the Hadoop ecosystem and is accessible via HiveQL. Hive is a simple solution for those who do not have much introduction to using MapReduce jobs on Hadoop, internally the queries are transformed into MapReduce jobs.

Hive can thus be described as the following:

  • Data warehouse infrastructure
  • Provider of HiveQL similar to SQL
  • Provider of utility tools for data extraction, transformation and loading of data
  • Allows embedding customized mappers, reducers
  • Provider of powerful statistics functions
  • Hive is gaining its popularity as it is supported by Hue

MindMajix YouTube Channel

Related Article: Hive Vs Impala

Checkout Hadoop Tutorial

What is Apache Hadoop Pig?

Pig is an ad-hoc method to create or execute MapReduce jobs on big datasets. Prime motto behind developing Pig was to curtail the time that is required for development via queries. It is a high-level data flow system which helps to render simple language platform that is termed as Pig Latin – helps in manipulating data and queries. The following are the reasons that make Pig much popular amongst the components available in Hadoop ecosystem:

  • Follows multi-query approach to avoid multiple scans of the datasets.
  • Pig is easy if you are well aware with SQL
  • Pig provides nested data types like Maps, Tuples and Bags which are not available for usage in MapReduce
  • Pig also provides support to major data operations like Ordering, Filters and Joins
  • Performance wise Pig surpasses that of raw MapReduce
Explore Hadoop Sample Resumes! Download & Edit, Get Noticed by Top Employers!Download Now!

Differences between Apache Pig and Apache Hive

There are lots of factors that define these components altogether and hence by its usage, and also by its purpose, there are differences between these two components of the Hadoop ecosystem. Hence let us try to understand the purposes for which these are used and worked upon.

Hadoop HiveHadoop Pig
Hadoop Hive component is mainly used by the Data AnalystsPig Hadoop component is generally used by the Researchers and Programmers
Hive is used against completely structured dataPig is used against semi-structured data
Hive makes use of SQL expertise and hence a very small learning curve for the SQL developersThough Pig is as well an SQL like a variant, there is a great variation associated with it and hence the learning curve to learn Pig is considerably big
Hive has a declarative SQL like language termed as HiveQLPig has a procedural data flow like language termed as Pig Latin
Hive is basically used for generation/creation of reportsPig is basically used for programming
Hive operates on the server side of an HDFS clusterPig operates on the client side of an HDFS cluster
Hive is very helpful in the areas of ETLPig is a wonderful ETL tool for Big Data (for its powerful transformation and processing capabilities)
Hive has an ability to start an optional thrift based server which is used to send queries from any part to the Hive Server directly to executePig does not provide any such provision for this feature
Hive leverages upon the SQL DLL language with definitions to tables upfront and storing the schema details on a local databaseThere is no provision of maintaining a dedicated metadata database and hence the schemas/data types are defined in the actual scripts itself in Pig
There is no provision from Hive to support AvroPig provides support to Avro
There is no provision of installation for Hive as it is completely shell based for interactionPig on the other had can be installed very easily
Hive provisions partitions on the data to process subsets based on dates or in chronological ordersPig do not provision anything like partitions directly but the feature can be achieved using Filters
There is no provision in Hive for illustrationsPig renders sample data for each of its scenarios through Illustrate function
There is a provision in Hive to access raw dataRaw data access is not possible with Pig Latin scripts as fast as HiveQL
In Hive, a user can join data, order data and even can sort data dynamically (in an aggregated manner though)There is a provision from Pig to perform OUTER JOINS using the COGROUP feature.


To come to a conclusion after having gone through all the necessary details to understand Big Data, Hadoop ecosystem and the components of choice of this article – Hive and Pig, it is clearly understood that there is no battle between Hive and Pig as such. Each of this provides a unique way of analyzing Big Data with reasonable affection towards Pig from the programmer community and Hive being admired by database developers.

List of Other Big Data Courses:

 Hadoop Administration MapReduce
 Big Data On AWS Informatica Big Data Integration
 Bigdata Greenplum DBA Informatica Big Data Edition
 Hadoop Hive Impala
 Hadoop Testing Apache Mahout
Course Schedule
Hadoop TrainingJun 15 to Jun 30View Details
Hadoop TrainingJun 18 to Jul 03View Details
Hadoop TrainingJun 22 to Jul 07View Details
Hadoop TrainingJun 25 to Jul 10View Details
Last updated: 03 Apr 2023
About Author

Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

read less