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- Parser
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.
- Optimizer
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.
- Compiler
The compiler converts the logical plan from the optimizer into a series of MapReduce tasks.
Our brilliant apache pig homework helpers guarantee excellent solutions for your scripts questions
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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Programminghomeworkhelp.com is the only name that you should remember when you are stuck with homework on apache pig data types. Our help with the apache pig homework service is provided by professionals who have a thorough understanding of the data types supported by this scripting language. It will be in your best interest to entrust us with the responsibility of preparing your homework if your deadline is fast approaching and you have not worked on your homework.
Apache pig Latin supports the following data types:
1. Atomic data types
• Int
• Float
• Double
• Long
2. Non-atomic data types
• Map
• Tuple bag
• Map
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:
| Data type | Overview |
| 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
- Execute
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We are available and ready to put an end to your pig homework miseries. Our experts are the best people to contact when you need professional apache pig project help. We are associated with skilled programmers who have in-depth knowledge and hands-on experience in preparing stellar-quality projects. Their areas of specialization include the following topics:
1. Pig Latin statements
• Loading data
• Storing intermediate results
• Debugging Pig Latin
2. Running Jobs on Kerberos secured cluster
• Long-lived jobs
• Short-lived jobs
3. Relational operators
• LOAD operator
• CROSS operator
• Distinct operator
4. Eval functions
• AVG
• CONCAT
• TOKENIZE
5. Math functions
• ABS
• CBRT
• FLOOR
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