Table of contents
Simplicity
Apache Pig provides a high-level scripting language called Pig Latin that is much easier to write and understand than complex MapReduce code, enabling faster development time.
Abstracts Hadoop Complexity
Pig abstracts the complexity of Hadoop, allowing developers to focus on data processing rather than worrying about the intricacies of Hadoop’s underlying mechanisms.
Extensibility
Pig allows user-defined functions (UDFs) to process various types of data, giving users the flexibility to extend its functionality according to their specific requirements.
Optimized Query Execution
Pig includes a rich set of optimization techniques that automatically optimize the execution of scripts, thereby improving performance without needing manual tuning.
Error Handling and Debugging
The platform has an extensive error handling mechanism and provides the ability to make debugging easier through logging and stack traces, making it simpler to troubleshoot issues.
Apache Pig is a valuable tool for data professionals working within a Hadoop environment, especially those who prefer or require a language more accessible than Java. However, its utility might be overshadowed by newer technologies such as Apache Spark, which offers more extensive functionality and faster processing speeds.
We have collected here some useful links to help you find out if Apache Pig is good.
Check the traffic stats of Apache Pig on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
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The latest comments about Apache Pig on Reddit. This can help you find out how popualr the product is and what people think about it.
Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / almost 3 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / almost 4 years ago
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