Introduction

Pig Apache is a reflection over MapReduce. It is a platform/tool which is utilized to dissect bigger arrangements of data addressing them as data flows. Pig in big data is by and large utilized with Hadoop; we can play out all the data control tasks in Hadoop utilizing Pig Apache.

To compose data analysis programs, Pig in big data gives a significant level of language known as Pig Latin Hadoop. This language gives different administrators utilizing which software engineers can build up their capacities for processing, writing, and reading data.

To examine data utilizing Pig, developers need to compose contents utilizing Pig Latin language. Every one of these contents is inside changed over to Map and Reduce errands.

  1. Need of Pig In Big Data
  2. Evolution of Pig
  3. Features of Apache Pig in big data
  4. Difference between Pig and MapReduce
  5. Applications of Apache Pig
  6. Types of Data Models in Apache Pig

1. Need of Pig In Big Data

It’s not difficult to learn, particularly in case you’re comfortable with Structured Query Language.

ig Latin is not difficult to read and write.

Pig’s multi-question approach diminishes the data of the occasion is scanned. This implies 1/20th the lines of code and 1/16th the improvement time when contrasted with writing raw MapReduce.

Pig gives data activities like ordering, joins, filters, and so on and settled data types like maps, bags, and tuples, that are absent from MapReduce. 

The presentation of the Pig in big data is comparable to raw MapReduce.

2. Evolution of Pig

The Pig was initially evolved by Yahoo in the year 2006, for scientists to have an ad-hoc method of executing and creating MapReduce jobs on exceptionally huge data collections. It was made to lessen the advancement time through its multi-inquiry approach. Pig is likewise made for experts from a non-Java background, to make their work simpler.

3. Features of Apache Pig in big data

Apache Pig accompanies the following highlights:

1. User-defined Functions: Pig in big data gives the ability to make UDFs in other programming languages like Java and embed or invoke them in Pig Scripts.

2. Handles a wide range of data: Apache Pig examines a wide range of data, both unstructured as well as structured. It stores the outcomes in the Hadoop Distributed File System.

3. Rich set of operators: It gives numerous operators to perform tasks like a filter, sort, join, and so on.

4. Extensibility: Using the current operators, clients can build up their capacities to write, process, and read data.

5. The simplicity of programming: Pig Latin is like Structured Query Language and it is not difficult to compose a Pig scripting on the off chance that you are acceptable at Structured Query Language.

6. Optimization opportunities: The assignments in Apache Pig enhance their execution naturally, so the software engineers need to focus just on the semantics of the language.

4. Difference between Pig and MapReduce

Recorded beneath are the significant differences between Pig and MapReduce:

  • Pig:
  1. There is no requirement for compilation. On execution, each Pig in big data administrator is changed over inside into a MapReduce work.
  2. Pig utilizes a multi-question approach, in this manner decreasing the length of the codes generally.
  3. Any novice developer with fundamental information on Structured Query Language can work advantageously with Apache Pig.
  4. Playing out a Join activity in Pig is quite straightforward.
  5. It is a high level of language.
  6. Pig is a data flow language.
  • MapReduce: 
  1. It has a long compilation measure.
  2. MapReduce will require right around multiple times more the number of lines to play out a similar errand.
  3. Openness to Java is an unquestionable requirement to work with MapReduce.
  4. It is very troublesome in MapReduce to play out a Join activity between datasets.
  5. MapReduce is low level and inflexible.
  6. MapReduce is a data preparing paradigm.

5. Applications of Apache Pig

A couple of the Pig in big data applications are: 

  1. Utilized by telecom organizations to de-identify the customer call data information.
  2. Cycle’s time-delicate data loads.
  3. Cycles a huge volume of data.
  4. Performs data handling in search stages.
  5. Supports fast prototyping and impromptu inquiries across huge datasets.

6. Types of Data Models in Apache Pig

A) Pig data types or Pig data model:

  1. Atomic: Atomic/Scalar data types are the fundamental data types that are utilized taking all things together with the languages like byte, char, double, long, float, int, string.
  2. Tuple: Tuple is an arranged arrangement of fields that may contain distinctive data types for each field.
  3. Bag: A bag is an assortment of a set of tuples and these tuples are a subset of rows or whole rows of a table.
  4. Map: A map is key-esteem sets used to address data components.

B) Pig Architecture or Apache Pig Architecture: 

The Pig Architecture or Apache Pig Architecture comprises of two segments: 

  1. Pig Latin, which is a language.
  2. A runtime environment, for running Pig Latin programs.

Pig Latin Program

                                                       Transformation                                

Logical Plan

                                                       Transformation                                

Physical Plan

                                                       Transformation                                

MapReduce Plan

                                                       Ready for Execution                        

Hadoop Execution

  • Execution modes:

Pig in HadoBop has two execution modes:

  1. Local mode
  2. MapReduce mode

Conclusion

Pig in big data is an aid to software engineers as it furnishes a stage with a simple interface, decreases code intricacy, and encourages them to effectively accomplish results. Twitter, LinkedIn, eBay, and Yahoo is a portion of the organizations that utilization Pig to deal with their enormous volumes of data.

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