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Apache Pig is part of the Hadoop ecosystem. It is used to analyze a large amount of data without using complex java or python code. Apache pig architecture is helpful when we don’t have sharp programming knowledge. But, having knowledge of SQL-like language. It will be fine for you.  We need to understand about the primary pig and big data concepts and their use cases to better understand Apache pig architecture.

Apache pig architecture

In Apache pig architecture there are 4 major components:

• Pig Latin Scripts
• Apache Pig
• MapReduce
• Hadoop

Apache Pig converts pig Latin scripts into a series of MapReduce jobs. So, it makes it easy for programmers. He doesn’t need to write map-reduce jobs in a programming language.

Pig Latin Scripts

To perform particular operations using Apache Pig we need to write pig Latin script. We can execute the pig Latin script using one of the multiple mechanisms like grunt shell for interactive execution. UDFs (User Defined Functions) in Java programming language or in an Embedded fashion.

Apache Pig

In the Apache Pig framework, there are multiple components as below:

Parser

Parser is used to read pig Latin script and analyze the pig Latin script syntax and type checking and some other checks. The output of this parser is DAG which is a Directed Acyclic Graph. It contains pig Latin statements and logical operators among statements. These logical operators are called nodes and data flows through them are called edges.

Optimizer

The optimizer plays a crucial role in the data processing pipeline. Once the Parser creates a Directed Acyclic Graph (DAG) of statements, the optimizer takes over and improves the logical statements for better speed and efficiency. It uses different ways to improve the action plan, such as forecasts and pushdowns. By using these optimizations, the optimizer makes sure that data is processed faster and more efficiently, which improves speed and makes better use of resources in the total data processing workflow.

Compiler

The compiler is one of the most important parts of the data processing pipeline. It turns the optimized rational plan that the planner made into a set of instructions that can be run. These instructions are put together into a number of MapReduce jobs, which are then run on a distributed computer cluster to process and analyze the data quickly. The compiler’s job is to bridge the gap between the logical plan and the real performance. This makes sure that the data processing tasks are done efficiently and correctly.

Execution Engine

After successful compilation generated MapReduce jobs will execute in a sorted order in Apache Hadoop using the execution engine and generates the desired result. The generated output will get stored on hdfs (Hadoop distributed file system).

MapReduce

MapReduce is a popular way to handle large amounts of data. It is used in Apache Hadoop. It gives a group of computers a way to work together to process big data sets in case something goes wrong. When an Apache Pig script written in Pig Latin is run, MapReduce is used behind the scenes.

Pig Latin is a high-level scripting language that hides the complexity of MapReduce. This makes it easier for users to write down data transformations and analysis jobs in a way that is clear and simple. Under the hood, Pig Latin lines are turned into a set of MapReduce jobs. This lets data be processed quickly and in different places.

Users can get the most out of their big data by combining the power of MapReduce and Pig Latin. They can do this by using parallel processing and automatic optimization to handle complex data changes and analytics jobs. This seamless integration makes it possible to handle data effectively and get useful insights from big datasets, which gives organizations the power to make choices based on data.

Hadoop

Apache Hadoop is an effective framework made to handle and store large amounts of data. It uses the MapReduce computing technique to analyse a lot of data quickly and well. Hadoop’s distributed file system, HDFS, makes it possible to store data across a collection of computers in a safe and scalable way. This makes it perfect for processing and analyzing large amounts of data.

Conclusion

As we have seen while using Apache pig its architecture is very useful to perform pig script execution. Hope it will be helpful to understand the working of the Apache pig script. Let us know if any further help is required.

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