Hadoop Hive Pig

Hive vs Pig- When to use Apache Hive and When Apache Pig [Infographic]

Hive and Pig are the two major ecosystems of Hadoop. But every time you will see people asking about Hive vs Pig. When to use hive and when Pig in the daily work?

I will try to answer this question- When to use Apache Hive and when to use Apache Pig and also with the help of below infographic, we will see the difference between Apache Hive and Apache Pig- Hive vs pig.

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Hive vs Pig Infographic

Let’s see the infographic and then we will go into the difference between hive and pig.

Hive Vs. Pig

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Let me share some details about the Hive and Pig and why we are using these.

Apache Hive

Hive is an integral part of Hadoop ecosystem used for data analysis. It can be used only when you have structured data else first you need to make the data structured and then you can inject in the Hive tables.

For all those who are much familiar to SQL, Hive can be easy for them. Similar to SQL query optimization, you can also optimize Hive queries. There are many other features like Partition and bucketing in Hive which makes your data analysis easy and quick.

The hive was developed at Facebook and later it becomes one of the top Apache projects. It gives the user flexibility to write less code and do more with it. Of course, it also converts the queries into MapReduce execution but you don’t have to worry about the backend processes much. Hive uses a query language pretty much similar to SQL known as HQL (Hive query language).

Apache Hive works well when it comes to processing data stored in a distributed manner, unlike SQL which requires strict adherence to schemas while storing data. There are lots of functions in Hive which can be directly used makes your work easy.

If something is not available, you always have the option to create UDFs (user-defined function) in Hive which will do your work. Generally business analysts, analysts prefer Hive.

In short, we can summarize Apache Hive as follows-

  • A Data Warehouse Infrastructure
  • Quite similar to SQL and uses a language called HQL
  • Provides us with various tools for easy extraction, transformation, and loading of data.
  • You can use and define custom mapper and reducer in Hive
  • Preferred for data analytics and reporting related work

Apache Pig

Apache Pig was developed by Yahoo in the year 2006 with the intention to reduce the coding complexity with MapReduce. It is a high-level data flow system that renders to a simple language called Pig Latin which is used for data manipulation and queries.

You don’t need to create the schema in Pig to store the data and you can directly load the files and start using it. You can also sue semi-structured data in Pig which is the benefit over Pig.

Pig is kind of ETL (extract-transform-load) for Big data and is quite useful and can handle large datasets. Apache Pig allows developers to follow multiple query approach, which reduces the data scan iterations. You can use multiple nested datatypes like Maps, Tuples, and Bags and is used for the operations like Filter, Pig Join, and Ordering.

There are many companies who use Pig for their majority of their MapReduce related work.

In short, we can summarize Apache Pig as follows-

  • It is a high-level language called Pig Latin
  • Programmers familiar with scripting language prefer Pig
  • There is no need to create the schema to store the data
  • Its compiler translates Pig Latin into sequences of MapReduce programs

Now as we know what is the major differences between Apache Hive and Apache Pig- Hive vs Pig, let’s see when to use Hive and when to use pig.

When to use Apache Hive and When to use Apache Pig?

We will discuss both one by one here, starting with Hive. I write Hive first because I love working on Hive (personal opinion as I am from DataBase background).

When to use Hive?

Use Hive in the following scenarios but it is not restricted to-

  • When you are familiar with SQL queries and concepts
  • When you have to do analytical querying of historical data
  • You should have structured data which allows Hive to fully unleash its processing and analytical prowess
  • Real-time analysis is not preferred with Hive (HBase is the alternative for real-time analysis)
  • Mainly used by data analysts
  • Majorly used when after data analysis you need to visualize it and create reports. Many major visualization tools such as Tableau allows direct integration to Hive.
  • Apache Hive is comparatively slower than Apache Pig

When to use Pig?

Pig is a scripting language and can be used in the following scenarios-

  • When you are a programmer and know scripting language very well
  • When you don’t want to create schema for all the data load related work
  • Pig is a complete ETL tool for Big Data
  • Pig runs on the client side of the Hadoop cluster
  • Pig has a procedural data flow language called Pig Latin
  • It has many SQL-related functions and additionally you have cogroup function as well
  • Pig supports Avro Hadoop file format
  • It is comparatively faster than Hive

Wrapping it up!

This was all about Hive vs Pig and when to use Hive and when to use Pig. I hope you got a clear understanding of the difference between Hive and Pig.

Usually, companies select one of the Hive and Pig and hardly any company uses both in a production environment. They decide it depending on the kind of data they have majorly. Mainly if a company has more historical data, they use Hive.

Which one do you use in your company and personal work?

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