Pig vs Hive | Difference between Pig and Hive

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As we know both Hive and Pig are the major components of Hadoop ecosystem. However, every time a question occurs about the difference between Pig and Hive. Also, there’s a question that when to use hive and when Pig in the daily work?

So, in this pig vs hive tutorial, we will learn the usage of Apache Hive as well as Apache Pig. Moreover, we will discuss the pig vs hive performance on the basis of several features. But before all comparison between Pig vs Hive, we will also learn brief introduction of both Hive and Pig.

Introduction to Apache Pig and Hive?

Before we discuss pig vs hive, let’s discuss what is Apache Pig and Hive in detail:

a. What is Apache Hive?
Basically, for data analysis, Hive is an integral part of Hadoop Ecosystem. We use it only when we have structured data. However, first of all, we need to make the data structured then only we can inject in the Hive tables.

However, Hive can be easy for all those who are much familiar with SQL. Also, we can optimize Hive queries as similar to SQL query optimization. Moreover, in Hive, there are many other features. Such as Partition and bucketing. Especially, that makes your data analysis easy and quick.

It becomes one of the top Apache projects later but at first, it was developed at Facebook. Also, it gives the user flexibility by writing less code and do more with it. Moreover, it converts the queries into MapReduce execution.

However, we don’t have to worry about the backend processes much. Also, Hive uses a query language pretty much similar to SQL known as HQL (Hive query language).

In addition, to processing data stored in a distributed manner, unlike SQL which requires strict adherence to schemas while storing data, Apache Hive works well. Though, Hive has lots of functions which we can directly use, that makes our work easy.

Moreover, in Hive, we always have the option to create UDFs (user-defined function) if something is not available. That will definitely do your work. Mostly, business analysts, analysts prefer Hive.
In short, we can summarize Apache Hive as follows-

  • It is a data warehouse infrastructure
  • Hive uses a language called HQL, and it is quite similar to SQL.
  • For easy extraction, transformation, and loading of data, it offers several tools.
  • In Hive, we can use and define custom mapper and reducer.
  • For data analytics and reporting related work, it is most preferred.

b. What is Apache Pig?
In the year 2006, it was developed by Yahoo. Basically, to reduce the coding complexity with MapReduce we use Apache Pig. It renders to a simple language called Pig Latin as a high-level data flow system that. Especially, which is used for data manipulation and queries.

Moreover, to store the data we don’t need to create the schema in Pig. Also, we can directly load the files and start using it. However, in Pig we can also sue semi-structured data which is the benefit of Pig.

To be more specific, for Big Data Pig is kind of ETL (extract-transform-load). Also, it is quite useful and can handle large datasets. Moreover, to follow multiple query approach it allows developers. That reduces the data scan iteration. In addition, we can use multiple nested datatypes. Such as Maps, Tuples, and Bags. Also, we use it for the operations like Filter, Pig Join, and Ordering.

However, for the majority of MapReduce related work, there are many companies who use Pig.
In short, we can summarize Apache Pig as follows-

  • In other words, Pig is a high-level language called Pig Latin
  • Basically, those programmers who are familiar with scripting language prefers pig
  • Also, to store the data there is no need to create the schema.
  • Moreover, Pig’s compiler translates Pig Latin into sequences of MapReduce programs

Let’s explore the Difference between Pig and Hive.

Apache Pig vs Hive

Feature Wise Difference Between Pig and Hive:

Pig vs Hive - Major Components of Hadoop Ecosystem

Pig vs Hive – Major Components of Hadoop Ecosystem

a. Language Used

  • Apache Hive

In Hive, there is a declarative language called HiveQL which is like SQL.

  • Apache Pig

In Pig, there is a procedural language called Pig Latin.

b. Mainly Used for

  • Apache Hive

Mainly, data analysts use Apache Hive.

  • Apache Pig

Mainly, researchers and programmers use Apache Pig.

c. Data

  • Apache Hive

Basically, Hive allows structured data.

  • Apache Pig

However, Apache Pig allows both structured and semi-structured data.

d. Operates on

  • Apache Hive

Basically, Hive component operates on a server side of the cluster.

  • Apache Pig

However, Pig server operates on the client side of the cluster.          

e. ETL (Extract-Transform-Load)

  • Apache Hive

We can say, Apache Hive is helpful for ETL.

  • Apache Pig

Although, Pig itself is an ETL tool for Big Data.

f. Avro File Format support

  • Apache Hive

Usually, Apache Hive does not support Avro file format support. However, with the help of Serge “Org.Apache.Hadoop.Hive.serde2.Avro”, can be done.

  • Apache Pig

Hive does support Avro File.

g. Developed by

  • Apache Hive

Hive was first developed by Facebook.

  • Apache Pig

Pig was first developed by Yahoo.

h. Partition

  • Apache Hive

Apache Hive does support Partition.

  • Apache Pig

Pig does not support Partition.

i. Loading Speed

  • Apache Hive

Hive executed quickly, but cannot load it quickly.

  • Apache Pig

Pig can loads the data effectively and quickly.

j. UDFs (User-Defined Functions)

  • Apache Hive

It does support UDFs but much hard to debug.

  • Apache Pig

In Pig, it is very easy to write UDFs to calculate matrices.

Usage – Pig vs Hive

a. Usage of Hive
we can Hive in the following scenarios. Such as:

  • We can use Hive while we are familiar with SQL queries and concepts.
  • While we perform analytical querying of historical data
  • For Hive to fully unleash its processing and analytical prowess it is important to have structured data.
  • However, Hive does not support Real-time analysis. So, HBase is the alternative for real-time analysis.
  • Especially, for data analysts
  • When after data analysis you need to visualize it and create reports you can use Hive.
  • Then Pig, Hive is comparatively slower. 

b. Usage of Pig
As we discussed above that Pig is a scripting language, hence we can use it in the following scenarios. Such as:

  • While you know scripting language very well and you are a programmer.
  • Especially, for all the data load related work While you don’t want to create the schema.
  • Since it has many SQL-related functions and additionally you have cogroup function as well
  • It does support Avro Hadoop file format
  • Pig is faster than Hive

So, this was all about Pig vs Hive Tutorial. Hope you like our explanation of a Difference between Pig and Hive.


As a result, we have seen the whole concept of Pig vs Hive. Also, we have learned Usage of Hive as well as Pig. However, we hope you got a clear understanding of the difference between Pig vs Hive.
Although companies generally select one of both Hive and Pig.

We can say Hardly any company uses both in a production environment. However, they depend on the nature of data they have majorly. Mainly if a company has more historical data, they use Hive. So, this is all about Pig vs Hive. Still, if any doubt occurs, feel free to ask in the comment section.

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2 Responses

  1. Kedar Divekar says:

    Hello, Thank you for such wonderful article. I have already bookmarked it for future reference.
    I would like to know where exactly we need to use pig? Like any particular scenario? I understand if data is semi-structured then we can use pig? But as we can connect to hive from BI reporting tools like Tableau, how we can make use of pig?

  2. Rouahi Aouatef says:

    Hello, thank you for your clearly explained article.
    I have something that I couldn’t grasp 🙂
    What do you mean when you say Pig does not support Partition and hive does?
    Thank you for any clarification.

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