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
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
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.
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.
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.
|Where do our learners come from?|
|Professionals from around the world have benefited from eduCBA’s Hadoop Ecosystem Masterclass – Up and Running with Apache PIG courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.|