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.
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It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.
Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Pig is more focused on scripting in its own PigLatin language rather than integrate into another language like Java/Scala/Python/SQL.
However, for batch ETL workloads, I find that I can write a Pig script quicker than setting up and deploying a Spark program, for example.
Apache Pig is picked up quickly and can be implemented with very little coding skills. Also the other languages require exact matching of versions during installations which made them somewhat less user-friendly. Also most of the tasks that are done in map reduce can be done quickly using a few lines of code from Apache Pig.
I use both Apache Pig and its alternatives like Apache Spark & Apache Hive. Apache Pig was one of the best options in Big Data's initial stages. But now alternatives have taken over the market, rendering Apache Pig behind in the competition. But it is still a better alternative to Map Reduce. It is also a good option for working with unstructured datasets. Moreover, in certain cases, Apache Pig is much faster than Hive & Spark.
Early on Apache Pig was a great tool for easily writing distributed processing applications without needing to write a complete Java MapReduce job from scratch, but as time as moved on there now better alternatives to get results faster for both ad-hoc analysis and for production systems. Apache Pig was used since it was what was available early on in the industry and since it has reached maturity, but at this point it feels a little long in the tooth.