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Apache Pig

Apache Pig

Overview

What is Apache Pig?

Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.

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Recent Reviews

TrustRadius Insights

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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TrustRadius Insights
TrustRadius Insights

Apache Pig

7 out of 10
April 07, 2022
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig …
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Product Details

What is Apache Pig?

Apache Pig Technical Details

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Comparisons

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Reviews and Ratings

(22)

Community Insights

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!

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.

Attribute Ratings

Reviews

(1-9 of 9)
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May 23, 2022

A great ETL tool for your big data

Verified User
Program Manager in Information Technology
Information Technology & Services Company, 201-500 employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
April 09, 2022

"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."

Sourov K Chowdhury | TrustRadius Reviewer
Sourov K Chowdhury
Database Software Engineer
Best Web Design Ltd. (Information Technology & Services, 11-50 employees)
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Apache Pig is a lightweight framework that is simple to learn and put into production. It converts MapReduce tasks into SQL-like queries. It also reduces the data and performs some simple mathematical functions. Combining data is incredibly beneficial. With Apache Pig's Data Time functions, we can get quicker results. It works on 150-180 GB monthly datasets and reduces them in a few minutes. However, it cannot perform sequential operations, such as comparing consecutive lines. And another flaw of this method is that it doesn't allow loops and nested loops to span more than one variable at a time. Then again, I'd say go for it!
April 07, 2022

Apache Pig

Verified User
C-Level Executive in Product Management
Entertainment Company, 51-200 employees
Score 7 out of 10
Vetted Review
Verified User
Likelihood to Recommend
Debugging the code for errors and functionalities is very time consuming leading to waste of development hours and low quality code. Since it is in early stage community support is also very less as compared to other products
April 28, 2021

Apache Pig - lot to improve

Verified User
Engineer in Engineering
Internet Company, 5001-10,000 employees
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Write complex map reduce jobs without having much deep knowledge of Java, Python, Scala. Advanced features such as secondary sorting, optimization algorithms, predicate push-down techniques are very useful. With Apache Pig it's easy to aggregate data at scale compared to other tools. It automates important Map Reduce tasks into SQL kind queries.


March 20, 2020

Useful ETL scripting tool

Jordan Moore | TrustRadius Reviewer
Jordan Moore
Software Consultant
Avalon Consulting (Information Technology and Services, 51-200 employees)
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
If someone wants to process data and doesn't have access to platforms such as Spark or Flink, and wants to do so in a minimal, portable fashion that requires simply requires learning a new scripting language, then Pig is great. It also supports running the same code against a cluster as a single developer machine for testing.

Pig is more suited for batch ETL workloads, not ML or Streaming big data use-cases.
October 08, 2018

Apache pig - the easier to implement map reducer

Subhadipto Poddar | TrustRadius Reviewer
Subhadipto Poddar
Research Assistant
Iowa State University (Higher Education, 5001-10,000 employees)
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
It is well suited when you are aggregating data but really difficult if you want to aggregate based upon line by line. Apache Pig can be picked up in a few days with a few demonstrations. Codes can be written quickly, however, it becomes difficult to take up complicated tasks using it.
June 22, 2018

My Apache Pig Review

Kartik Chavan | TrustRadius Reviewer
Kartik Chavan
Data Analyst
The University of Texas at Arlington (Electrical/Electronic Manufacturing, 1001-5000 employees)
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
It is one great option in terms of database pipelining. It is highly effective for unstructured datasets to work with. Also, Apache Pig being a procedural language, unlike SQL, it is also easy to learn compared to other alternatives. But other alternatives like Apache Spark would be my recommendation due to the high availability of advanced libraries, which will reduce our extra efforts of writing from scratch.
January 18, 2018

Apache Pig - Is it the tool for the job? Maybe, but probably not.

Verified User
Engineer in Engineering
Computer Software Company, 51-200 employees
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Apache Pig is well suited as part of an ongoing data pipeline where there is already a team of engineers in place that are familiar with the technology since at this point I would consider it relatively depreciated since there are more suitable technologies that have more robust and flexible APIs with the added benefit of being easier to learn and apply. For ad-hoc needs, I would recommend Hive or Spark-SQL if a SQL-esque language makes sense otherwise to make use of Spark + a Notebook technology such as Apache Zeppelin. For production data pipelines I would recommend Apache Spark over Apache Pig for its performance, ease of use, and its libraries.
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