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 can accommodate Map Reduce in a single script and a single fine. IT has very much documentation present for easy learning. SQL like queries makes it easy to understand
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Chose Apache Pig
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 …
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 …
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 …
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 …
- Provided better ways for optimized hadoop jobs than Hive but not anymore. - Spark DSL is much more advanced and compute times are significantly less.