Apache Pig Training Course:-
Perfect Platform for Analyzing Data Flows
Apache Pig is an abstraction over MapReduce which is a tool or platform for analyzing larger data sets and envisioning them as data flows. Apache Pig is used with Hadoop for data manipulation operations. For writing data analysis programs, Pig has a high level language called Pig Latin. This provides numerous operators through which programmers can develop their own functions for reading as well as writing and processing data. For data analysis through Apache Pig, programmers must compose the script using Pig Latin Language. These scripts are converted to Map and Reduce tasks. Apache Pig has a component called Pig Engine accepts Pig Latin scripts as input and converts these into MapReduce jobs.
Apache Pig Training: Moving Beyond Map Reduce
- Apache Pig is a data flow language while Map Reduce involves a data processing paradigm
- In this Apache Pig Training, you will learn that, Apache Pig is a high level language while MapReduce is low level as well as rigid
- Join operation in Apache Pig is easy while it is quite tough in Map Reduce
- Apache Pig requires basic knowledge of SQL while exposure to Java is necessary to work with MapReduce.
Apache Pig- Adding to SQL
- In this Apache Pig Training, you will learn that, Pig Latin refers to a procedural language while SQL is a declarative language
- Within Apache Pig, the schema is optional whereby we can store data without designing the schema while schema is mandatory in SQL
Apache Pig – Flying Beyond Hive
- In this Apache Pig Training, you will learn that, Apache Pig and Hive have been used for creation of MapReduce jobs. Hive operates on HDFS in a way similar to Apache Pig. Apache Pig differs from Hive. It uses a Pig Latin called Apache Pig known as Pig Latin which was created at Yahoo originally
- Hive employs HiveQL originally created at FB
- Pig Latin is a data flow language while HiveQL is a query processing language
- Pig Latin is a procedural language and fits into pipeline paradigm
Apache Pig Latin: A Language with a Difference
- An interesting use of Hadoop is searching massive data set for the records to meet a certain search criterion. While three lines are shown in Pig, simply one is located within actual search. This script which is simple implements a cohesive flow but would use more code if there is implementation in the traditional Map Reduce perspective. This makes it easier to learn Hadoop and commence with data, commencing raw development.
- In this Apache Pig Training, you will learn that, Apache Pig Latin is a simple language executing statements. Statement refers to operation which takes input and emits a unitary bag as output. Bag is in relation to the relational database. A Pig Latin script follows a certain format whereby data read from a file system then results in numerous operations being performed on data and the resulting relation is composed back within the file system.
Benefits of Apache Pig Training:
In this Apache Pig Training, you will learn that, Apache Pig is a data flow language built atop Hadoop for making it simpler to process, clean and analysis of big data without writing Map Reduce jobs in Hadoop. Apache Pig solves different than relational database is its application to big data which can crunch large files. Companies that have data and big data for automating some of their processes can make them produce better products using Apache Pig Training.
- Decrease in development time is one of the topmost advantages of Map Reduce jobs complexity time spent and program maintenance
- The learning curve is easy and those with Map-Reducer or SQL can easily master it.
- In this Apache Pig Training, you will learn that, Apache Pig is procedural not declarative unlike SQL so it is simpler to follow commands and provide enhanced expressiveness in data at every step
Hadoop Map Reduce Vs Hive Vs Apache Pig
Hadoop Map Reduce is a compiled language while Apache Pig involves a scripting language and Hive involves SQL like query language
While Hadoop Map Reduce requires a lesser level of abstraction, higher levels of abstraction are needed by Apache Pig and Hive. Hadoop offers more lines of code while the other two have less lines of code
For the Hadoop Map Reducer, more development effort is needed. The development efficiency is less for Apache Pig and Hive.
On the flip-side however, code efficiency is less while development effort is also less.
Prerequisites of Setting up Apache Pig Training:
- The prerequisites for establishing Apache Pig and running Pig Scripts are as follows.
- Firstly, there should be the latest stable build of Hadoop of around 1.0.3
- Secondly, you need to install hadoop and the machine should have Java 1.6 installed
- Users also need to have basic knowledge of Java programming as well as SQL
- Essential knowledge of Linux will help in understanding linux commands in the tutorial.
- Pig tutorial assumes that users have Linux/Mac OS X. If the Windows is being used, Cygwin should be installed. Shell support in addition to required software is needed.
Apache Pig Training Conclusion:
Apache Pig is a sophisticated technology that can work wonders for any office or personal site.
This is ideal for ease of use and application across diverse settings. Apache Pig is a well-organized and efficient system for organizing and collating data. Apache Pig is also easy to code and learn.
In fact, the learning curve for this is the least steep as against other alternatives such as Hive and Hadoop. Apache Pig requires a simple and basic set up. The beauty of Apache Pig is the use of Pig Latin Language which is interesting in terms of usage and application.
Apache Pig is an abstraction atop Hadoop which provides top level programming language for data processing and it is widely accepted and used across large data sets for analyzing as well as evaluating programs.
Map Reduce requires programmers and the users must envision it in terms of map and reduce functions. Apache Pig is associated with high level analysis which is used by data scientists, statisticians and those sorting data.
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Section 1: Introduction to PIG
Getting Started with PIG
Section 2: Understanding Installation
2 Installation Process
3 PIG Latin
Section 3: Practical Examples
4 Uploading the File in HDFS
5 PIG Script
6 PIG Latin Basics
7 Up and Running with Pig
8 Loading and Storage
9 Loading and Storage Continue
10 Debugging
11 Grunt Shell
12 UDFs and Piggy Bank
