What is Pig?

There’s lots of software named after animals — an entire zoo of data science tools, if you like. You’ll probably come across Elk, Ant, Rhino, and Raptor at some point in your future data science career. There’s also Cheetah, Porcupine, Seagull, Squirrel, and GNU — named after a type of African antelope with an incredibly long neck, in case you were wondering! 

Apache Pig is one platform you’ll need to get accustomed to. That’s because it’s really useful for analyzing large and complex data sets as a data scientist! Get an answer to the question, ‘What is Pig?” below.

Pig Definition

Here’s a definition from Apache Pig itself:

“Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs.”

Here’s a simpler answer to the question, “What is Pig?” 

 It’s a platform for analyzing data sets that relies on a language called Pig Latin, hence its name. Apache Pig has a structure that supports parallelization — don’t worry, we’ll explain this next! — which allows you to handle large data sets. 

So what is parallelization? It’s the process of breaking down large computational tasks into smaller computational tasks. That lets you perform those jobs concurrently (at the same time) on multiple computer processors or cores (the “brains” of a computer processor). 

Apache launched Pig in 2006, first as a research project. Yahoo! created it to carry out MapReduce jobs. It became open-sourced through The Apache Incubator in 2007, and its first official release came out in 2008. However, it wasn’t until 2010 that Pig became an Apache top-level project.

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How Does Apache Pig Work?

You’ll discover much more about how Apache Pig works on a data science program, like those here at The Data Incubator. However, here are some quick notes: Apache Pig has various datasets for performing data operations. The caveat is that you need to learn and use the Pig Latin programming language to write scripts. Only then can you carry out tasks like:

  • Joining datasets
  • Loading datasets
  • Categorizing datasets
  • Grouping datasets
  • Sorting datasets
  • Filtering datasets


Pig then turns the scripts you write into MapReduce jobs. That simplifies your life as a data scientist. You won’t have to manually code tasks for MapReduce, which means more time analyzing data sets! 

Apache Pig Pros

Apache Pig’s advantages include ease of programming, optimization, and extensibility. Learn more about each of these benefits below:

Easy of Programming

Who wants to spend hours programming data analysis jobs? Probably not you, right? The great thing about Pig is that it streamlines parallelization, removing the need to code data transformations. Like we just said, that frees up time for the fun part of data science — analyzing data! Pig’s parallelization automation capabilities also make it easier to write, maintain, and, ultimately, understand data transformations. That’s definitely a good thing. 


Pig encodes data transformation tasks in a very specific way. That makes it unique from many other tools for analyzing large, complicated data sets. For instance, it lets you focus on semantics instead of efficiency. That basically means you can optimize data analysis and identify patterns and trends in data more easily. 


Extensibility means creating your own functions for special-purpose processing. And special-purpose processing means analyzing data sets from specific data sets, such as documents and spreadsheets. One of Pig’s strengths is enhancing extensibility.

It’s time to start your career in data science! Take a deeper dive into tools like Pig on our Data Science Bootcamp.

Apache Pig Cons

Like any platform, Apache has some disadvantages: 

  • Pig doesn’t enforce data schemas explicitly but implicitly, which is a major setback. Enforcing schemas results in clean and fully-transformed data sets.
  • Errors generated by Pig aren’t always helpful, leaving you wondering what went wrong. Errors can certainly lack context, making it more difficult to analyze data. 


Pig is open source, so you can’t call customer support if you have trouble with it. Loads of user-generated resources are available online, however. 

What are you waiting for?

Want to take a deep dive into the data science skills you need to become a successful data scientist? The Data Incubator has got you covered with our immersive data science bootcamp.

Here are some of the programs we offer to help you turn your dreams into reality:

  • Data Science BootcampThis program provides you with an immersive, hands-on experience. It helps you learn in-demand skills so you can start your career in data science.
  • Data Engineering BootcampThis program helps you master the skills necessary to effortlessly maintain data, design better data models, and create data infrastructures.

We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.


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