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 …
Apache Pig is called Pig Latin—that it provides a high-level scripting language to perform data analysis, code generation, and …
We mainly use Apache Pig for its capabilities that allows us to easily create data pipelines. Also it comes with its native language Pig …
Apache Pig and its query language (Pig Latin) allowed us to create data pipelines with ease and heavily used by our teams. The language …
Pig is used by data engineers as a stopgap between setting up a Spark environment and having more declarative flexibility than HiveQL …
Apache Pig is being used as a map-reduce platform. It is used to handle transportation problems and use large volume of data. It can …
As a requirement of a distributed processing system, we are using Apache Pig within our Information Technology department. I use it to an …
Apache Pig is one of the distributed processing technologies we are using within the engineering department as a whole and we are …
Yes, it is used by our data science and data engineering orgs. It is being used to build big data workflows (pipelines) for ETL and …
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Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
Companies can't remove reviews or game the system. Here's why
- 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'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.
- Inefficient Debugging
- Writing UDFs is very challenging
- 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
- Iterate quickly on ETL pipelines.
- Scale up parallel processing.
- Easily templatable scripting language.
- 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
- 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.