Overview
What is Apache Pig?
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
A great ETL tool for your big data
"Apache Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."
Apache Pig
Apache Pig - lot to improve
Useful ETL scripting tool
Apache pig - the easier to implement map reducer
My Apache Pig Review
Apache Pig - Is it the tool for the job? Maybe, but probably not.
Apache Pig - a good toolkit to have in your hadoop ETL toolbox
Product Details
- About
- Tech Details
What is Apache Pig?
Apache Pig Technical Details
Operating Systems | Unspecified |
---|---|
Mobile Application | No |
Comparisons
Compare with
Reviews and Ratings
(22)Community Insights
- Business Problems Solved
- Recommendations
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)A great ETL tool for your big data
- 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 Pig Is A Fantastic High-level Scripting Language To Operate With Big Data Sets."
- 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 Pig
- Inefficient Debugging
- Writing UDFs is very challenging
Apache Pig - lot to improve
- Doesn't support all kinds of SQL-like abstraction
- It's DML based scripting requires lot of training
- Error handling is not helpful in debugging production issues
Useful ETL scripting tool
- Iterate quickly on ETL pipelines.
- Scale up parallel processing.
- Easily templatable scripting language.
Apache pig - the easier to implement map reducer
- Positive includes quicker solutions to basic problems
- Negative can be we also had to incorporate other softwares for advanced work.
- Another positive is time saving
My Apache Pig Review
- 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.
- Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache
- Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team
- As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with.
- The ROI was definitely positive in the beginning, but hard to say the same now due to advancements in Hive and Spark.