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Apache Pig has proven to be an invaluable tool for data engineers working with large datasets in the Apache Hadoop ecosystem. Users have …
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What is Apache Pig?
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
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What is Apache Pig?
Apache Pig Technical Details
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TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Apache Pig has proven to be an invaluable tool for data engineers working with large datasets in the Apache Hadoop ecosystem. Users have found it to be an excellent high-level scripting language that simplifies the process of working with big data. With Apache Pig, data engineers can easily build pipelines for advanced analysis and machine learning purposes, allowing them to transform and optimize data operations into MapReduce.
One of the key advantages of Apache Pig is its ability to write complex map-reduce or Spark jobs without requiring deep knowledge of Java, Python, or Groovy. This feature has been highly appreciated by users who value the efficiency and simplicity it brings to their work. Additionally, Apache Pig's query language, Pig Latin, provides users with a straightforward way to build data pipelines, eliminating redundant data and supporting user-defined functions UDFs.
The software also gives users control over task execution, which is crucial in maintaining control in a distributed processing system. This control allows users to efficiently handle transportation problems and manage large volumes of data including data streaming from multiple sources and performing joins. Users have utilized Apache Pig to explore and process large datasets in big data analytics projects, performing various operations within a single Java Virtual Machine.
Another key use case for Apache Pig is the generation of aggregate statistics, running refinement and filtering on logs, as well as generating reports for both internal use and customer deliveries. Data science and data engineering teams also utilize Apache Pig for building big data workflows pipelines for ETL and analytics. The software simplifies the creation of these pipelines by providing native language support with Pig Latin, combining features from various database systems like Hive, DBMS, and Spark-SQL.
Overall, Apache Pig offers a versatile solution for handling big data tasks in a simple yet efficient manner. Its user-friendly query language and extensive capabilities make it a valuable tool for data engineers working in the Apache Hadoop ecosystem.
Users have provided several recommendations for using Pig as a tool for writing quick big data applications.
One recommendation is that Pig is a good starting point for developing ad-hoc analytics applications, especially for those with basic programming experience in Java.
Another recommendation is to use Pig as a base pipeline for parallelizing and utilizing User-Defined Functions (UDFs) on large datasets. The lazy evaluation feature of Pig allows for efficient program optimization.
Users also appreciate Pig's integration with Hadoop, which provides parallelization, fault-tolerance, and relational database features. This makes Pig suitable for applying statistics to datasets, and its functional programming paradigm aligns well with pipeline processes.
Additionally, users suggest considering Spark or Hive as alternative tools for developing pipelines. While Pig may be more suitable for developers with programming experience, it is free and has extensive online documentation available for learning purposes.
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Reviews
(1-9 of 9)Companies can't remove reviews or game the system. Here's why
May 23, 2022
A great ETL tool for your big data
We are working on a large data analytics project where we have to work on big data, large datasets, and databases. We have used Apache Pig as it helps to explore and process large datasets. It helps in performing several operations such as local execution environments in a single Java Virtual Machine. Apache Pig is somehow easy to learn and use and the data structures are nested and richer. We have used largely whenever we used the analytical insights for our sampling data.
April 09, 2022
"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."
Apache Pig is called Pig Latin—that it provides a high-level scripting language to perform data analysis, code generation, and manipulation. It is an excellent high-level scripting language for working with large data sets. That work under Apache's open-source project Hadoop. Because of this, we can transform and optimize the data operations into MapReduce, which can be difficult on other platforms. We quickly and easily built data pipelines using its query language. It eliminates redundant data, supports user-defined functions (UDFs), and controls data flow well. Its efficiency in writing complex map-reduce or Spark jobs without deep knowledge of Java, Python, or Groovy is what I like best about Apache Pig. Furthermore, with the assistance of a pig, it is simple to maintain control over the execution of a task.
April 07, 2022
Apache Pig
Score 7 out of 10
Vetted Review
Verified User
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig latin which helps to manage to code execution easily. It brings the important features of most of the database systems like Hive, DBMS, Spark-SQL.
April 28, 2021
Apache Pig - lot to improve
Apache Pig and its query language (Pig Latin) allowed us to create data
pipelines with ease and heavily used by our teams. The language is designed to reflect the way data
pipelines are designed, so it discards extraneous data, supports user
defined functions (UDFs) , and offers a lot of control over the data
flow.
March 20, 2020
Useful ETL scripting tool
Pig is used by data engineers as a stopgap between setting up a Spark environment and having more declarative flexibility than HiveQL while moving away from MapReduce. It solves the problem of needing to iteratively transform and migrate data between supported Hadoop environments while being able to debug the process at each step.
October 08, 2018
Apache pig - the easier to implement map reducer
Apache Pig is being used as a map-reduce platform. It is used to handle transportation problems and use large volume of data. It can handle data streaming from multiple sources and join them. This can be used to extract key findings, aggregate results and finally process output which is used for different types of visualizations.
June 22, 2018
My Apache Pig Review
As a requirement of a distributed processing system, we are using Apache Pig within our Information Technology department. I use it to an extent of generating reports with advanced statistical methods, both for internal use as well as external purposes. But our Data Science team and Data Engineering team use it to build pipelines in Big Data environment, to conduct further advanced analysis including for machine learning purposes.
Apache Pig is one of the distributed processing technologies we are using within the engineering department as a whole and we are currently using it mainly to generate aggregate statistics from logs, run additional refinement and filtering on certain logs, and to generate reports for both internal use and customer deliveries.
Yes, it is used by our data science and data engineering orgs. It is being used to build big data workflows (pipelines) for ETL and analytics. It provides easy and better alternatives to writing Java map-reduce code.