What is Apache Pig used for?

Pig is a high level scripting language that is used with Apache Hadoop. Pig enables data workers to write complex data transformations without knowing Java. Pig’s simple SQL-like scripting language is called Pig Latin, and appeals to developers already familiar with scripting languages and SQL.

Where is pig used?

Pig is used for the analysis of a large amount of data. It is abstract over MapReduce. Pig is used to perform all kinds of data manipulation operations in Hadoop. It provides the Pig-Latin language to write the code that contains many inbuilt functions like join, filter, etc.

How does Apache Pig process data?

1. Step 1: You first need to first have the path where you need the data to be downloaded from.
2. Step 2: Upload the Data So That You Can Process.
3. Step 3: Open Pig to Create Your Script.
4. Step 4: Define Relation.
5. Step 5: Click Execute to Run the Script.

What are the components of pig execution environment?

Let us take a look at the major components.

• Parser. Initially the Pig Scripts are handled by the Parser.
• Optimizer. The logical plan (DAG) is passed to the logical optimizer, which carries out the logical optimizations such as projection and pushdown.
• Compiler.
• Execution engine.
• Atom.
• Tuple.
• Bag.
• Map.

Why pig is used in big data?

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

How pig data model will help in effective data flow?

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.