PIG – Advanced

In this course, you will be learning about Apache PIG in details. You will learn pg basics, installation, pig script, loading and storage, debugging, grunt shell etc

293

 2h 13m | 12 Videos | 73633 Views |   Expert| English[Auto-generated]

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 is perfect for all programmers. The Pig Latin programming language ensures that programmers can perform MapReduce tasks without complex Java codes. Apache Pig Training follows a multiple query approach reducing the length of codes. For instance, an operation requiring 200 lines of code in Java needs just 10 lines of code in Apache Pig. Estimates of by how much Apache Pig reduces development time is almost around 15 to 16. Pig Latin is an SQL like language and it is simple to learn Apache Pig when there is familiarity with SQL. In this Apache Pig Training, you will learn that, Apache Pig provides built in operators for supporting data operations such as joins, ordering, filters and more so. It also provides nest data types such as maps, bags and tuples missing from MapReduce. Apache Pig comes equipped with the following features:

Rich set of operators for performing operations such as join, sort, filter etc.

In this Apache Pig Training, you will learn that, Pig Latin is akin to SQL and it is simple to write this script if you are familiar with the latter

Tasks in Apache Pig optimize execution automatically so programmers need to be oriented towards semantics of language.

Through these existing operators, own functions can be developed for reading, writing and processing of data

UDFs or User Defined Functions can be used in programming languages such as Java and invoked in Pig scripts as well

Apache Pig analyzes different kinds of data which are structured and unstructured resulting in HDFS

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
• In this Apache Pig Training, you will learn that, Apache Pig uses a multiple query approach and length of codes is reduced to a massive extent; Map Reduce will need 20 times more the number of lines to perform a certain task
• Currently, there is no need for compilation because on execution, each Apache Pig operator is converted on an internal basis into Map Reduce job
• Map Reduce has a massive compilation process unlike Apache Pig

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
• In this Apache Pig Training, you will learn that, Data model in Apache Pig is a nested relational while data model used in SQL is flat relational
• Apache Pig offers limited opportunity for query optimization while there is query optimization for SQL
• In this Apache Pig Training, you will learn that, Apache Pig Latin also allows splits in the pipeline while allowing developers to store data within the pipeline, declares execution plans and providing operators to perform the following functions- extract, load and transform.

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
• In this Apache Pig Training, you will learn that, Apache Pig is used for unstructured and semi structured data while Hive is used for structured data
• In this Apache Pig Training, you will learn that, Apache Pig is used by data scientists for performing adhoc processing and quick prototyping. Apache Pig is used for processing huge data sources and perform data processing for search platforms as well as processing time sensitive data loads.
• In 2006, Apache Pig has developed a research project at Yahoo for creating and executing Map Reduce jobs for every dataset
• In the year 2007, Apache Pig was open sourced within the Apache incubator while in the year 2008, first release of Apache Pig was released
• In 2010, Apache Pig graduated as a Apache top level project
• Popularity of Hadoop is growing with a spurt in its ecosystem while programming Hadoop apps is one area where advanced programming is needed.
• In this Apache Pig Training, you will learn that, While programming Map and Reduce apps are not increasingly complex, for which experiencing software development is a must, Apache Pig reduces this by creating an easy procedural linguistic abstraction over MapReduce to reveal a more SQL type interface for Hadoop apps
• Instead of composing a separate Map Reduce app, one can come up with a unitary script in Pig Latin automatically paralleled and distributed within the clustering.

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.
• In this Apache Pig Training, you will learn that, Pig refers to a varied set of data types supporting top level concepts as well as easy data types. Within the simple kinds, one finds a varied range of arithmetic operators in partnership to a conditional operator referred to as bincond operating.  While Pig Latin statements are referred to as relational operators. While there is no comprehensive list of operators within Pig Latin, it has key operators to process increased data sets.
• In this Apache Pig Training, you will learn that, Pig can be used in one of dual modes the first being the Local medium which uses HDFS while everything is executed within a unitary Java virtual machine within the perspective of local file system. The other is Map Reduce mode where a Hadoop file system and cluster is used. Within the Local mode, commence Pig and indicate Local mode which allows the formation of interactive Apache Pig statements. For the Map Reduce Mode, it must first be ensured that Hadoop is functioning. Performing file list operations on the root of the Hadoop file tree system is the easiest way to do this. Code will result in listing of one/more files, in case Hadoop is successfully running.
• In this Apache Pig Training, you will learn that, Apache Pig supports numerous diagnostic operators which can be used for debugging scripts. Apache Pig can be made more powerful through UDFs or user defined functions. Apache Pig scripts can try functions for defining those items such as parsing input data or formatting output data and even operators. UDFs are composed in Java language and permit Apache Pig custom processing. UDFs are an easy way to extend Pig to the specific application domain. Apache Pig is powerful tool for asking data in a Hadoop cluster. Apache Pig also ensures it is easier for non-developers to perform big data processing within a Hadoop cluster. Hadoop has increased in the face of big data and its ever growing use.

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
• In this Apache Pig Training, you will learn that, Data flow is the point where everything is about data though control structures such as for loop or if structures. This ensures developers can think about data and no more. You can create control structures and obtain data transformation as a side show. Due to data, data transformation is facilitated.
• As this is procedural, one can control the execution of each step and if you write UDF- User Defined Function and inject in one specific part of the pipeline, Apache Pig is the best choice. Unless that output file is produced or does not output any message, it does not receive evaluation. This optimizes program beginning to end and the optimizer can produce an efficient plan for execution. Hadoop offers everything from parallelization to fault tolerance with relational database aspects. This is essential for unstructured and large datasets and it is the best tool for making large data into a more structured format.
• In this Apache Pig Training, you will learn that, UDFs can be parallelized and utilized for large amounts of information and Pig as the base pipeline performs all the hard work.
• Pig’s programming language known Pig Latin is a coding approach that produces massive level of abstraction for MapReduce programming yet the code is procedural not declarative. Pig Latin code can be extended through numerous UDFs written in Java, Groovy, Ruby and Python.
• In this Apache Pig Training, you will learn that, Pig also has immense number of tools for data storage as well as execution and manipulation.
• In this Apache Pig Training, you will learn that, Pig Latin is promoted by Yahoo as Pig is used for processing data on hadoop clusters across the globe.
• In this Apache Pig Training, you will learn that, Pig Latin is one of the most general processing concepts of SQL such as selecting, filtering, ordering and grouping.
• Syntax is different when it comes to Pig Latin as opposed to SQL. While the users of the latter are needed to make conceptual adjustments to acquire knowledge of Pig, Apache Pig needs intricate coding as compared to Apache Hive and it offers more control on data flow and optimization than Hive. Learning curve for Java MapReduce is more as compared to that of Pig Latin or Hive QL. Higher level languages such as Pig Latin or Hive Query Language hadoop developers and analysts can write with less development effort.
• In this Apache Pig Training, you will learn that, Writing Pig Latin script needs only a fraction of the development effort as against the Hadoop Map Reduce program where runtime performance fell by 50%. Pig and Hive coding approaches are not as quick as Hadoop when it comes to speed, but they are the top choice for enhancing productivity of data analysts and engineers.
• Pig Latin or Hive query can be rewritten using Map Reduce code making this approach faster. However, performance penalty of Apache Pig can be overcome through the use of extra machines.

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.

Hadoop Map Reducer has a code efficiency which is higher compared to Apache Pig and Hive

Apache Pig is an interpreter for scripting language Pig Latin; this is akin to SQL yet there are differences. Pig Latin is a data flow language and is of a lower level than SQL. What is required differs as for simple programs, complex Pig scripts may be difficult to match in SQL.

In this Apache Pig Training, you will learn that, Pig Latin is akin to SQL and scripting languages and having basic knowledge of either will help in acquiring deeper understanding of the former system. All programming or scripting languages have some primitives yet Pig Latin is not an exception. These are similar to Java primitives or classes as primitive types in Pig Latin. There are numerous built in commands which involve 4 fields namely the ngram, year of occurrence and match_count or number of appearances er annum as well as volume count or number of books in a year.  There is a need to make a script to search for how many occurrences of the word there are in the file. The word can be put to search for a variable representing multiple words. Word search prevents hard coding into the pig script.

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.

Back to top ▴