What is Pig?
Pig is an open-source technology that is part of the Hadoop ecosystem for processing the high volume of unstructured data. This is managed by the Apache software foundation. It has a high-level scripting language known as pig Latin scripts that help programmers to focus on data level operation, and it implicitly manages the map-reduce processes for data computation. It efficiently interacts with the Hadoop distributed file system(HDFS). It is implemented as an Extraction Transformation and Load (ETL) component in the Big Data pipeline. It supports several operators and User-Defined Functions (UDF) for complex data processing scenarios in the Big Data implementations.
Understanding Pig
It is a technology that allows you to write high level, but extremely granular scripts, which allows you to work with data where the schema is either unknown or inconsistent. It is an open-source technology that runs on top of Hadoop and is part of the extremely vibrant and popular Hadoop ecosystem.
It works well with unstructured and incomplete data, so you don’t have to have the traditional layout of rules and columns for everything.
It’s well-defined, and it can directly work on files in HDFS (Hadoop Distributed File System).
It will be your technology of choice when you want to get data from the source into a data warehouse.
For example, a visual pipeline of how data typically flows before you can use it to generate the nice charts that you use to make business decisions.
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The raw data comes from a variety of sources, such as sensors, mobile phones, etc. You will then use it to perform an ETL operation. ETL stands for extract, transform, and load, once these operations are performed, the cleaned-up data is stored in another database. An example of such a database would be HDFS, which is a part of Hadoop. Hive is a data warehouse that will run on top of a file system such as this. Hive is what you would use for analysis, to generate the reports, and to extract insights.
ETL is a very important step in data processing in order to get the raw data cleaned up and in the right form to be stored in a database. Extract refers to the operation of pulling unstructured, inconsistent data with missing fields and values from the original source. Transform stands for the series of operations that you would apply on the data in order to clean it up or get it.
Pre-computation of useful aggregate information, processing of fields to match a certain format, all of this is a part of data cleanup of the transform fields.
Finally, it performs the load operation where this clean data is stored in a database where it can be further analyzed. An example of a standard operation that Pig performs is to clean up log files.
Explain Pig Architecture
There are numerous parts in the Architecture, prefer:
- Parser: Parser deals with Pig Scripts as well as, checks the syntax of the script, will type checking, and various assorted checks. Additional, their result might be a DAG (Directed Acyclic Graph) which usually signifies the Pig Latin claims along with logical operators.
Also, the logical operators with the script will be shown like the nodes as well as data flows will be shown since edges through DAG.
- Optimizer: Later, the logical plan (DAG) is usually exceeded towards the logical optimizer. It performs the logical optimizations additional including projection and promotes low
- Compiler: As well, the compiler compiles that enhanced logical plan in a group of MapReduce works.
- Execution Engine: Ultimately, all of the MapReduce works will be posted to Hadoop within a sorted sequence. Eventually, this generates the required outcomes although these MapReduce works will be carried out with Hadoop.
- MapReduce: MapReduce was originally designed in Google as a way to process web pages so as to power Google search. MapReduce distributes computing across multiple machines in the cluster. MapReduce takes advantage of the inherent parallelism in the data processing. Modern systems, such as sensors, or even Facebook status updates generate millions of records of raw data.
An activity with this level can be prepared in two phases:
- Map
- Reduce
You decide what logic you want to implement within these phases to process your data.
- HDFS (Hadoop Distributed File System): Hadoop is allowing for an explosion of data storage and analysis at a scale in an unlimited capacity. Developers are using an application like Pig, Hive, HBase, and Spark to retrieve data from HDFS.
Features
It comes with the beneath different features:
- The Simplicity of Programming: Pig Latin is comparable to SQL and therefore it is quite simple for developers to create a Pig script. In case you have an understanding of SQL language, it is incredibly simple to learn Pig Latin language since it is just like SQL language.
- Rich Set of Operators: It includes a variety of rich set of operators to be able to execute procedures just like join, filer, sort and much more.
- Optimization Possibilities: The performance with the task in this can be instantly enhanced by the task itself; therefore the developers have to just concentrate on the semantics of this language.
- Extensibility: Utilizing accessible operators, users can simply develop their functions to read, process, and write data.
- User Define Functions (UDF’s): By using the service given by Pig of making UDF’s, we could produce User-Defined Functions on a number of development languages including Java as well as, invoke or embed all of them in Pig Scripts.
What is Pig Useful For?
It is utilized for examining as well as, executing responsibilities including ad-hoc handling. It can be used intended for:
Analysis with huge raw data collections prefers data processing to get search websites. Such as Yahoo, Google benefits to evaluate data collected via Google as well as Yahoo search engines. Handling large data collections just like web records, streaming online info, and so on. Even Facebook’s status updates generate millions of records of raw data.
How does this Technology help you grow in your career?
Many organizations are implementing Apache Pig incredibly quickly. This means Professions in Pig & Careers are raising daily. There has been huge progress in the development of Apache Hadoop within the last couple of years. Hadoop elements just like Hive, HDFS, HBase, MapReduce, and so on.
Although Hadoop offers came into their second decade at this time yet have exploded in recognition through the previous Three to Four years. A large number of software companies are applying Hadoop clusters incredibly commonly. This can be definitely the best part of big data. The aiming experts could turn into experienced in this excellent technology.
Conclusion
Apache Pig Expertise is in large requirement in the market and can continue to be for extended. By simply understanding the concepts as well as, getting experience with the best Apache Pig in Hadoop skills, the experts may engage in their Apache Pig profession perfectly.
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