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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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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)Companies can't remove reviews or game the system. Here's why
May 23, 2022
A great ETL tool for your big 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.
April 09, 2022
"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."
- 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.
April 07, 2022
Apache Pig
Score 7 out of 10
Vetted Review
Verified User
- 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
April 28, 2021
Apache Pig - lot to improve
- Data pipeline and aggregation
- Log parsing and reporting
- Combine Map Reduce jobs
- Pig lacks in supporting the advanced features that Apache Spark provides
- Well outdated
- Debugging in Pig is a complex part
March 20, 2020
Useful ETL scripting tool
- 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).
October 08, 2018
Apache pig - the easier to implement map reducer
- Fast
- Easy to implement
- Can process data of almost any size
- Easy to learn schema
- It can only work on trivial arithmetic problems.
- No or very difficult provision of looping across data
- Sequential checks are almost impossible to implement
June 22, 2018
My Apache Pig Review
- 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.
- Provides a decent abstraction for Map-Reduce jobs, allowing for a faster result than creating your own MR jobs
- Good documentation and resources for learning Pig Latin (the Domain Specific Language of the Apache Pig platform)
- Large community allows for easy learning, support, and feature improvements/updates
- May not fit every need and a SQL-like abstraction may be more effective for some tasks (look at Spark-SQL, Hive, or even an actual DBMS)
- All Pig jobs are written in a Domain Specific Language so not a lot of transferable knowledge
- Writing your own User Defined Functions (UDFS) is a nice feature but can be painful to implement in practice
- Apache pig DSL provides a better alternative to Java map reduce code and the instruction set is very easy to learn and master.
- It has many advanced features built-in such as joins, secondary sort, many optimizations, predicate push-down, etc.
- Improve Spark support and compatibility