Apache Pig Reviews & Insights

Score8.4 out of 10

22 Reviews and Ratings

Community insights

TrustRadius Insights for Apache Pig are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Business Problems Solved

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 Reviews

1 Review

Mid-sized Companies (51-1,000 employees)

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

Rating: 7 out of 10

Incentivized

January 18, 2018

Use Cases and Deployment Scope

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.

Pros

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

Cons

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

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.

Verified User

Engineer in Engineering (Computer Software company, 51-200 employees)

Vetted Review

2 years of experience