Apache Pig vs Apache Spark

• Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
• Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
• Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."

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• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

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• 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

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• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

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• 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.

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• It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
• Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
• It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.

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