Big Data is a continually developing field. It has applications in various industries, including finance, tech, healthcare, etc.
To become a Big Data professional, you’d need to learn the various technologies used in analyzing Big Data. And Hadoop is a significant part of those Big Data technologies.
Apache Pig is one of the many essential components of Hadoop. If you want to analyze vast quantities of data fast, you’ll need to use Pig. In this article, we would be focusing on Apache Pig, the analyzing tool that not only helps you take care of big chunks of data but also saves your time while doing so.
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Apache Pig Tutorial: What is it?
Learning about Apache Pig (or Hadoop Pig) is crucial if you want to learn Hadoop. It’s a platform you can use to analyze vast sets of data. You can do so by representing the data sets as data flows.
We all know how popular Hadoop is in the Data Science world. And if you’re interested in mastering this open-source framework, you’ll need to learn about Apache Pig.
It is based on Map-Reduce, which is a significant component of Hadoop. As it enables you to analyze large data sets, you can work with higher efficiency while using this tool. You can use Apache Pig for data manipulation projects in Hadoop as well.
Pig is a high-level tool, which requires you to learn its advanced language called Pig Latin. Pig Latin helps you write data analysis programs. Read more about top hadoop tools. Through this language, you can write, read, and process data while developing specific functions for these tasks.
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The scripts you write in Pig Latin will automatically convert in Map-Reduce operations. Apache Pig’s Engine (called Pig Engine) helps you convert your written scripts into those operations. Learning this tool will help you considerably in performing Big Data Analytics.
It simplifies the different processes and helps you save time through its fast scripting language. While it does have a learning curve, once you get past that, you’ll realize it’s one of the most straightforward tools to work with.
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History of Apache Pig
In 2006 at Yahoo, Apache Pig was created for performing MapReduce operations on numerous datasets. Through Apache Incubator, Apache Pig became open-sourced in 2007. A year after that, its first release entered the market.
Finally, in 2010, Apache Pig became an Apache high-level project. Since then, it has become quite an essential tool for Big Data professionals. Now that you know about the origin of Pig, we can start discussing why it’s so popular and what are its advantages.
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Features of Apache Pig
Pig is rich with features. Its wide variety of functions are what make it a valuable and irreplaceable tool for experts.
Here are its features:
- Pig has many operators you can use for simplifying your programming operations.
- It lets you create your functions depending on your specific requirements. These functions are called UDFs (User Defined Functions), and you can write them in any programming language, including Python, JRuby, Jave, etc.
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- Pig is capable of handling all kinds of data. That means, it can feel, structured, semi-structured, as well as unstructured data values.
- It automatically optimizes your operations before executing them.
- It lets you work on the entire project at hand without worrying about separate Map and Reduce functions.
Why is Apache Pig so Popular?
Apache Pig comes with plenty of features and advantages that make it a necessity for any Big Data professional.
Moreover, because it removes the need for learning Java for data analytics, it quickly becomes the preferred choice for those programmers who aren’t adept at using that language.
Here are some reasons why Apache Pig is so important and popular:
- You can use MapReduce and perform its tasks without having to learn Java.
- You can perform primary operations with fewer lines of code by using Pig. When you’re using Pig for performing MapReduce operations, you write 20 times fewer lines of code than you would’ve written if you weren’t using Pig.
- Pig saves you a lot of time while working on MapReduce projects.
- It has an extensive range of operations such as Join, Extract, Filters, etc.
- Pig has plenty of data types in its model which are absent in Mapreduce. These include bags, tuples, and some others.
Now that you know why it’s so popular, we should now focus on some common causes of confusion regarding Pig and other tools and languages.
Difference Between MapReduce and Apache Pig
Even though Apache Pig is an abstraction over Hadoop’s MapReduce, their overlapping functions can confuse anyone. They both are related to performing MapReduce tasks. But even with such a similar applications, they both are entirely different from each other.
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Here are the main differences between Pig and MapReduce:
- Apache Pig is a high-level data-flow language. On the other hand, MapReduce is simply a low-level paradigm for data processing.
- You can perform a Join task in Pig much smoothly and efficiently in comparison to MapReduce. The latter doesn’t have many options for simplifying a Join operation of multiple datasets.
- You don’t need to compile anything when you’re using Apache Pig. All MapReduce operations require a significant compilation process.
- You need to have some (at least novice-level) knowledge of SQL if you want to work with Pig. On the other hand, you need to be familiar with Java for using MapReduce.
- Pig enables multi-query functionality, which makes your operation more efficient as you write very few lines of code. MapReduce doesn’t have this ability. You would need to write 20 times more lines of code for performing the same operation in MapReduce in comparison to Pig.
Difference Between SQL and Apache Pig
A considerable confusion among novice Big Data professionals is of SQL and Apache Pig. They don’t know the significant differences between the two.
Here are the differences between Apache Pig and SQL:
- Apache Pig’s data model is nested relational while SQL’s data model is flat relational. A nested relational model has atomic and relational domains. A flat relational model only has a single table for storing values.
- Schema is optional in Apache Pig, but it’s mandatory in SQL. This means you can store your data in Apache Pig without using Schema while you can’t do so with SQL.
- Pig doesn’t have many features and options for Query optimization. SQL has plenty of options in this regard.
- Apache Pig uses Pig Latin, which is a procedural language. On the other hand, SQL is a declarative language. So, while Pig Latin executes the required tasks, SQL focuses on describing what the system has to perform.
- You can perform ETL functions, which are, Extract, Transform, and Load, in Apache Pig. You can’t do so with SQL.
- Pig lets you store data in any location in the pipeline, but SQL doesn’t have this capability.
Difference Between Hive and Pig
‘Hive vs Pig’ is a popular topic for debate among professionals. Once you know the difference between the two, you wouldn’t be a part of them. Both of them are parts of the Hadoop Ecosystem. They both are necessary for working on Big Data projects, and they facilitate the functionality of other Hadoop components as well.
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To avoid confusion between the two, you should read the following differences:
- Apache Pig uses Pig Latin, which is a procedural programming language. Hive uses a declarative language called HiveQL, which is similar to SQL.
- Pig can work with semi-structured, structured, and unstructured data. Hive works with structured data in most cases.
- You would use Pig for programming while you’d use Hive for generating reports.
- Pig supports the Avro file format, which Hive doesn’t.
- Pig works on the client-side of the cluster while Hive works on the server-side of the same.
- Pig finds applications mainly among programmers and researchers. On the other hand, Hive finds applications among data analysts.
What Apache Pig Does
Apache Pig uses Pig Latin as its language for analyzing data. It’s a high-level language you use for data processing, so it requires a little extra effort for learning.
However, it gives you many data types along with operators for performing your tasks. The first step for using Pig is to write a Pig script, which you would write in the Pig Latin language.
After that, you will need to use one of its various execution systems for executing the task. The different execution options in Pig include Embedded, Grunt Shell, and UDFs.
After that, the framework of Pig transforms the scripts according to the requirements for generating the output.
Apache Pig converts Pig Latin Scripts into MapReduce tasks. This way, your job as a programmer becomes a lot easier.
Apache Pig Architecture
Now that you know what Apache Pig does and how it does it, let’s focus on its different components. As we mentioned earlier, the Pig scripts undergo various transformations for generating the desired output. For doing that, Apache Pig has different components which perform these operations in stages.
We’ll discuss each stage separately.
First Stage: Parser
The Parser handles the early stage of analyzing the data. It performs a variety of checks including type checks and syntax checks, on the script. The output Parser generates called DAG (directed acyclic graph).
DAG shows the logical operators and Pig Latin statements. It shows logical operators as nodes and data flows as edges.
Second Stage: Optimizer and Compiler
Parser submits the DAG to the Optimizer. The Optimizer performs logical optimization of the DAG, which includes activities such as transform, split, and so on.
It performs multiple functions for reducing the quantity of data in the pipeline when it processes the generated data. It performs automatic optimization of the data and uses functions such as PushUpFilter, MapKeyPruner, Group By, etc.
You have the option of shutting down the automatic optimization feature as a user. After the Optimizer, comes the Compiler, which compiles the resultant code into MapReduce tasks. The Compiler handles the conversion of Pig Script into MapReduce jobs.
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Third Stage: Execution Engine
Finally comes the Execution Engine where the MapReduce jobs are transferred to Hadoop. Once they are transferred there, Hadoop gives the required results.
You can see the result of the data by using the ‘DUMP’ statement. Similarly, if you want to store the output in HDFS (a core component of Hadoop), you will have to use the ‘STORE’ statement.
Applications of Apache Pig
The primary uses of the Pig are as follows:
- For processing massive datasets such as online streaming data and Weblogs.
- For processing the data of search platforms. Pig can handle all data types, which makes it very useful for analyzing search platforms.
- For analyzing time-sensitive data. This involves data which is updated continuously, such as tweets on Twitter.
A great example of this would be analyzing tweets about a particular topic on Twitter. Maybe you want to understand customer behaviour regarding that specific topic. Tweets contain media of various forms. And Pig can help you analyze them for getting the required results.
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Pig Tutorial: Where to go from here?
Apache Pig is undoubtedly one of the most critical areas of Hadoop. Learning it isn’t easy, but once you get the hang of it, you’ll see how much simpler it makes your job.
There are many areas in Hadoop and Big Data, apart from Pig.
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What is Apache Pig used for?
Apache Pig can be conceptualized as an abstraction layer over Hadoop's MapReduce. It is a platform or tool that helps analyze huge sets of data, demonstrating them as data streams. Apache Pig is used along with Hadoop. It is a boon for those programmers who are not very comfortable working with Java and struggle with Hadoop and MapReduce functions. Using Pig, they can now perform MapReduce functions easily without the need to write complex programs in Java; Apache Pig simplifies their job. Pig Latin, the language, is very similar to SQL and easy to learn, so that makes it faster for them to start working seamlessly.
How is MapReduce different from Apache Pig?
MapReduce is a Hadoop function that is used to efficiently access Big Data stored in HDFS (Hadoop Distributed File System). MapReduce is the core module of Hadoop that effectively segments huge data sets into smaller chunks and processes them in parallel. On the other hand, Pig is a tool or platform that works as an abstraction layer over MapReduce and the Hadoop ecosystem for better processing of Big Data. While MapReduce helps distribute and store data across server networks, Pig is used much like SQL to manipulate the stored data. So, while Pig is a high-level data-flow language, MapReduce is a low-level data processing language.
Is Big Data a part of data science?
Both Big Data and data science are buzzwords. Data science is the superset of Big Data which refers to massive volumes of data, both unstructured and structured data. Data science is a vast and complex field that involves particular technological skills and domains and consists of practices linked to various data-related techniques and processes. More precisely, Big Data is a specialized application of data science, where huge voluminous and complex datasets must overcome logistical hurdles to be processed. The main concern here is to increase efficiency in storing, accumulating, extracting, and analyzing insights and information from these massive sets of data.