Name: Apache Pig
Rating: 8.4 (22 reviews)
Author: Apache

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### Data Processing and Analysis
Users generally praise Apache Pig for its efficient data processing and analysis capabilities. Reviewers highlight that Apache Pig excels in handling large datasets, performing data analytics, and processing structured and unstructured data effectively. The tool's ability to streamline data operations, provide insights through data analysis, and facilitate data understanding are commonly appreciated by users. Additionally, Apache Pig's features for data aggregation, transformation, and analytical processing receive positive feedback for their contribution to enhancing data processing workflows. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-05-18-00-01-15), [2](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [3](https://www.trustradius.com/reviews/apache-pig-2022-04-07-04-46-05), [4](https://www.trustradius.com/reviews/apache-pig-2021-04-23-17-16-45), [5](https://www.trustradius.com/reviews/apache-pig-2020-03-16-20-06-38))

### ETL (Extract, Transform, Load) Processes
Reviewers generally praise Apache Pig for its ETL (extract, transform, load) processes capabilities, highlighting its efficiency in handling and analyzing large volumes of data. Users find Apache Pig's ETL functionality to be user-friendly and accessible, particularly for those with prior SQL knowledge. However, some users note limitations in real-time analysis and the lack of support for OLTP (Online Transaction Processing). Despite these drawbacks, Apache Pig's ETL processes are recognized for their speed and performance in managing substantial datasets. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-05-18-00-01-15), [2](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [3](https://www.trustradius.com/reviews/apache-pig-2020-03-16-20-06-38))

### Data Pipelines and Workflows
Opinions on Apache Pig's data pipelines and workflows functionalities vary among users. While some users praise its capabilities in creating and managing data pipelines efficiently, others express mixed opinions about its performance in handling complex data flows. The ability to iterate quickly on ETL pipelines and scale up parallel processing are highlighted as strengths, but there are also mentions of challenges in certain workflow scenarios. Despite differing views, Apache Pig's data pipelines and workflows feature prominently in user discussions, showcasing its importance in data processing tasks. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-04-07-04-46-05), [2](https://www.trustradius.com/reviews/apache-pig-2022-05-18-00-01-15), [3](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [4](https://www.trustradius.com/reviews/apache-pig-2020-03-16-20-06-38))

### Scripting and Query Language
Reviewers consistently praise Apache Pig's scripting and query language capabilities. Users find Pig Latin to be a powerful high-level scripting language that simplifies data analysis and manipulation tasks. The language's ability to convert MapReduce tasks into SQL-like queries is highly valued, enabling users to work with large datasets efficiently. Despite some challenges in error debugging and a steeper learning curve compared to other tools, Apache Pig's scripting and query language remains a standout feature for many users. Its support for user-defined functions, data flow control, and template-friendly scripting make it a valuable asset for data pipeline development. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [2](https://www.trustradius.com/reviews/apache-pig-2022-04-07-04-46-05), [3](https://www.trustradius.com/reviews/apache-pig-2022-05-18-00-01-15), [4](https://www.trustradius.com/reviews/apache-pig-2021-04-23-17-16-45), [5](https://www.trustradius.com/reviews/apache-pig-2020-03-16-20-06-38))

### Debugging and Error Handling
Users commonly express frustration with Apache Pig's debugging and error handling capabilities. Reviewers highlight that debugging in Pig can be time-consuming and challenging, often consuming a significant portion of development time. Additionally, users find the error handling process in Pig to be complex and not very helpful in resolving production issues. Some users also note that Pig lacks support for advanced features compared to Apache Spark, making debugging and error resolution even more cumbersome. Despite these challenges, there are mixed opinions among users regarding the effectiveness of Apache Pig's debugging and error handling functionalities. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-04-07-04-46-05), [2](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [3](https://www.trustradius.com/reviews/apache-pig-2021-04-23-17-16-45))

### Performance Optimization and Execution
Reviewers consistently highlight Apache Pig's robust performance optimization and execution features. Users appreciate its ability to handle large datasets efficiently, execute tasks swiftly, and optimize parallel processing effectively. The platform's support for fast execution frameworks like MapReduce, Tez, and Spark contributes to its reputation for delivering high performance and scalability in data processing tasks. Additionally, the ease of scripting and the option for local and remote interoperability further enhance the overall execution experience for users. (Source Reviews: [1](https://www.trustradius.com/reviews/apache-pig-2022-04-07-04-46-05), [2](https://www.trustradius.com/reviews/apache-pig-2022-05-18-00-01-15), [3](https://www.trustradius.com/reviews/apache-pig-2022-04-08-15-15-18), [4](https://www.trustradius.com/reviews/apache-pig-2021-04-23-17-16-45), [5](https://www.trustradius.com/reviews/apache-pig-2020-03-16-20-06-38))

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.

Pig's error debugging process consumes a significant amount of development time due to its instability and lack of maturity. Some reviewers have expressed frustration with the frequent errors they encounter while using Pig, which not only hampers their progress but also necessitates additional time for troubleshooting.

Mastering Pig is more challenging compared to Hive, according to several users. They find it difficult to grasp the concepts and syntax of Pig, resulting in a steeper learning curve than that of Hive. This can be an obstacle for newcomers or individuals transitioning from other data processing frameworks.

The limited support from the Pig community makes it difficult to find assistance for related issues. Many users have reported difficulties in finding help or resources specific to Pig due to the small size of its community. This lack of support can hinder problem-solving and overall productivity.

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.

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

Apache Pig

Hadoop-Related

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The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.

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Atlassian Jira

Eclipse

Databricks Data Intelligence Platform

Database Software Engineer

Apache Pig 2022-04-08 15:15:18

Apache Flink

Software Consultant

Apache Pig 2020-03-16 20:06:38

Apache Hadoop

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Apache Pig 2022-05-18 00:01:15

Apache Spark MLib

Data Analyst

Apache Pig 2018-06-19 12:19:14

Apache Pig 2022-04-07 04:46:05

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Hadoop-Related Software

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.

Its performance, ease of use, and simplicity in learning and deployment.
Using this tool, we can quickly analyze large amounts of data.
It's adequate for map-reducing large datasets and fully abstracted MapReduce.

Pig's error debugging consumes most of its development time because it can be unstable and immature.
It is significantly more challenging to learn and master than Hive. It's a little slower than Spark.

Apache Pig makes it simple to handle any amount of data.
Apache Pig is easy to use and has many options.
Apache Pig simplifies the Map-reduce process.

Apache Pig's scripting language is template-friendly.
A lightweight framework, Apache Pig, is easy to learn and deploy.
It converts MapReduce tasks into SQL-like queries, useful for data analysis.
It reduces the amount of data and performs a few simple mathematical operations on the data.
Combining data is a huge advantage.

Apache Hive, Google BigQuery and Apache Spark

Jira Software, Databricks Lakehouse Platform (Unified  Analytics Platform), Eclipse

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.

Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."

General syntax for the FOREACH ... GENERATE feature is confusing for nested actions.
The docs are hard to navigate, but it is made up for by reasonable examples.
A version less than 1.0 doesn't instill confidence in the product that has been around for over half a decade (as of writing).

Iterate quickly on ETL pipelines.
Scale up parallel processing.
Easily templatable scripting language.

Apache Spark, Apache Flink and Apache Hive

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.

It provides great support to large datasets and ad-hoc reporting.
It has almost all the set of operators to perform actions such as Join, Sort, Merge, etc.
Anybody can use Apache Pig with some initial training and it is very much familiar with SQL.
It can handle almost all structured, and unstructured data.
Apache Pig is built using the data flows, users can easily see all the processes and information.

One of the most important limitations of Apache Pig is it does not support OLTP (Online Transaction Processing) as it only supports OLAP (Online Analytical Processing).
Apache Pig has very high latency as compared to Map Reduce.
Apache Pig is designed for ETL and thus not perfectly suited for real-time analysis.
The training materials are hard to learn and need improvements.

Apache Pig helps us in processing our large datasets for data analytics.
Apache Pig helps us process Map Reduce in a single script file.
Apache Pig has good training materials for users, although required some improvements.
It helps us in providing local and remote interoperability.

Apache Pig is best known for its fast execution of data processing (+ROI).
Scaled up large parallel processing on data.
It helps in saving our time in data processing (+ROI).
Large community base for quick resolutions (+ROI).
Compatibility with other 3rd parties applications and tools (-ROI).

Apache Hadoop, Azure Data Lake Storage, Amazon EMR (Elastic MapReduce), Presto (formerly Presto DB), Confluent Platform and Alteryx

Cloudera Data Platform, Alteryx, Apache Flink, Splunk Cloud, Google BigQuery, Databricks Lakehouse Platform (Unified  Analytics Platform)

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.

Long logics in Java? Apache Pig is a good alternative.
Has a lot of great features including table joins on many databases like DBMS, Hive, Spark-SQL etc.
Faster & easy development compared to regular map-reduce jobs.

UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors.
Being in early stage, it still has a small community for help in related matters.
It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement.

Return on Investments are significant considering what it can do with traditional analysis techniques. But, other alternatives like Apache Spark, Hive being more efficient, it is hard to stick to Apache Pig.
It can handle large datasets pretty easily compared to SQL. But, again, alternatives are more efficient.
While working on unstructured, decentralized dataset, Pig is highly beneficial, as it is not a complete deviation from SQL, but it does not take you in complexity MapReduce as well.

Apache Hive, Apache Spark and Apache Spark MLib

Apache Hive, Apache Spark, Apache Spark MLib

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.

Useful for map -reducing huge datasets
Easy to learn and deploy
Optimization is higher compared to relative products.

Pace of introducing new features is very slow.
Community is also relatively small because it is still in early stage.
Debug functionality is not there, also it is compile time

Easily process any size of data
Understanding schema is also very easy
Reduces complexity of implementing Map-Reduce

Inefficient Debugging
Writing UDFs is very challenging

Apache Pig

Overview

What is Apache Pig?