There are many data skills that are required in huge amounts today by software developers. Edureify, the best AI learning app has compiled the best courses that will boost your data skills immediately and will also let you know about the present competition. The online Bootcamp coding courses are the ones that must be scrolled through for more details.
PIG Apache: Applications and Features
With Pig, we can operate more transparently. For instance, we might combine data from two or more sources. Writing a join as a map and reducing function is a pain and is generally not recommended. Pig is amazing because it makes complex jobs simpler. It provides a high-level scripting language that enables users to view their data flow more comprehensively.
Pig is especially excessive since it is expandable. Its extensibility will be highlighted in this article. We will be able to create PigLatin scripts at the conclusion of this post that runs Python code as part of a wider map-reduce workflow.
Description About PIG
A pig is made up of two main components:
- Pig Latin is a high-level data-flow language.
- Pig Latin scripts are run through an engine that analyses, improves, and executes them as a sequence of MapReduce tasks on a Hadoop cluster.
- Pig is a data transformation language that enables the processing of data to be defined as a series of transformations, making it simple to create, understand, and maintain. Through the usage of User Defined Functions, it is also extremely extendable (UDFs). The bootcamp online coding courses will be immensely helpful for understanding this term in a better way.
Individualized Operations (UDFs)
A Pig Python is only one of the many languages that UDF enables for specialised processing.
- It serves a purpose close to Pig. However, it is written in a tongue other than PigLatin.
- Pig enables the registration of UDFs for use in PigLatin scripts.
- A UDF must suit an exact prototype.
- The Extract, Transform, and Load (ETL) procedure is an example of a Pig application.
- That outlines how an application retrieves data from a data source, modifies the data, and then uses it for purposes for querying and analysis.
- Additionally, it loads the output onto the desired data repository.
- Pig may do projections, iterations, and other transformations when it loads the data.
- More complex algorithms can be helpful during the change phase thanks to UDFs.
- After Pig has finished processing the data, it might be returned to HDFS for storage.
Need of PIG
Pig is required because Map Reduce
has a lengthy development cycle, which is one of its limitations. It takes time to write the reducer and mapper, compile and package the code, submit the job, and retrieve the output. Apache Pig uses a multi-query strategy to speed up development. Pig is also useful for programmers without Java experience. Pig Latin may be used to write 200 lines of Java code in just 10 lines. SQL-savvy programmers found learning Pig Latin to be less difficult.
- It makes use of a query method, which shortens the code.
- Pig Latin is a language akin to SQL.
- It has a lot of built-in operators.
- The nested data types are offered (tuples, bags, map).
Features of Pig Apache
- Apache Pig offers a wide range of operators for carrying out various tasks, including filters, joins, and sorting.
- simple to read, write, and learn. Apache Pig is a godsend for programmers that specialise in SQL.
- Because Apache Pig is extensible, you can create your own custom processes and functions.
- In Apache Pig, joining operations are simple.
- fewer code lines.
- Splits in the pipeline are supported by Apache Pig.
- The data structure is richer, multivalued, and nested.
- Pig is capable of handling both organized and unstructured data processing.
Data Model Types in Apache Pig
It consists of the following 4 types of data models:
- Atom: An atomic data value is one that may be stored as a string. This model’s principal application is its dual functionality as a number and a string.
- Tuple: The fields are arranged in an ordered group.
- Tuples are gathered in the bag.
- A map is a collection of key-value pairs.
Some of the other concepts can be helpful for candidates who want to make their career in the field of data science. All of the concepts which are stated below will be covered extensively by the online coding courses.
Frequently Asked Questions for Pig Apache
Q:- What is Apache Pig used for?
Ans:- It is a tool/platform which is used to analyze larger sets of data representing them as data flows. Pig is generally used with Hadoop; we can perform all the data manipulation operations in Hadoop using Apache Pig. To write data analysis programs, Pig provides a high-level language known as Pig Latin.
Q:- Is Apache a Pig?
Ans:- Apache Pig is a high-level data flow platform for executing MapReduce programs of Hadoop. The language used for Pig is Pig Latin. The Pig scripts get internally converted to Map Reduce jobs and get executed on data stored in HDFS. Apart from that, Pig can also execute its job in Apache Tez or Apache Spark.
Q:- Is Apache Pig a language?
Ans:- It provides a high-level scripting language, known as Pig Latin which is used to develop the data analysis codes. First, to process the data which is stored in the HDFS, the programmers will write the scripts using the Pig Latin Language.
Q:- What is Apache Pig architecture?
Apache Pig architecture consists of a Pig Latin interpreter that uses Pig Latin scripts to process and analyze massive datasets. Programmers use Pig Latin language to analyze large datasets in the Hadoop environment.
