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Apache pig was founded on Apache Hadoop ecosystem MapReduce. It was developed to handle a variety and a mammoth of a dataset. Apache Pig was created by Yahoo and was later converted into an open-source project. Mentioned below are some of the fundamental components of Apache Pig:
This component handles the script. It performs functions such as checking the syntax, type, etc. The parser provides the user with an output in the form of directed acyclic graphs. The statements in these graphs are from Pig Latin.
The logical optimizer components perform operations such as projection and pushdown. These operations also depend on the logic plan and are represented using directed acyclic graphs.
The compiler converts the logical plan from the optimizer into a series of MapReduce tasks.
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Apache pig is a scripting language that supports Extract, Transform, and Load (ETL) operations. It can also be used for raw data analysis. Like SQL scripting and query language, Pig Latin can also load and dump data in the structure needed. Pig Latin only does this after applying a variety of filters and constraints. Programs created by this scripting language require a Java Runtime Environment (JRE). Hadoop handles all the operations. It transforms the operations to the modules of Map and Reduces.
Pig scripts written in Pig Latin are initially submitted to the execution environment. Pig scripts can be executed in the three ways listed below:
- Grunt shell: This is the interactive shell that executes all the Apache Pig’s scripts
- Pig server: The Pig commands written in the script file are executed in this server
- Embedded script: User-defined functions can be created using various programming languages. These functions can be embedded in the Pig Latin script file and then executed
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Apache pig Latin supports the following data types:
1. Atomic data types
2. Non-atomic data types
• Tuple bag
Pig Latin is a fully nested data model. Atomic data types are the primitive or scalar data types. They are considered the basic data types that are supported by Pig Latin. On the other hand, non-atomic data types are more complex data types. Our help with the apache pig homework caters to homework on all the data types mentioned above and many more. Below is a brief description of the non-atomic data types:
|Tuple||This is a record that has been created by a set of fields that are ordered. A tuple can be likened to the rows in a Relational Database Management System (RDBMS). The set of fields can also be of any type. |
|Bag||This is a set of non-ordered tuples. A tuple is flexible and can have any number of fields. A bag can be likened to a table in a Relational Database Management System (RDBMS). |
|Map||Sometimes referred to as a data map, this is a set of key-value pairs. It must be of type char array and unique. A map can also have a value of any type. |
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Can anyone do my apache pig homework involving running scripts in MapReduce mode?
If you are faced with an homework that requires you to run scripts in the MapReduce mode, then all you have to do is to follow these simple steps:
- The first step is to go to the pigtmp directory
- Copy and paste the excite.log.bz2 file to the HDFS directory
- The PIG_CLASSPATH environment variable should be set to the cluster configuration directory’s location
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1. Pig Latin statements
• Loading data
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2. Running Jobs on Kerberos secured cluster
• Long-lived jobs
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• LOAD operator
• CROSS operator
• Distinct operator
4. Eval functions
5. Math functions
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