The ecosystem and the way it works. Being able to implement and integrate what you currently use. Review collected by and hosted on G2.com.
I think getting started is a bit patchy but once you're familiar and used to it, it can be very helpful. Review collected by and hosted on G2.com.
I used it at my old company and it was easy to use. I was really used to using SQL beforehand but was easy to adapt to. Review collected by and hosted on G2.com.
The user interface is not my favorite. It is tough that there is not really that large of a community using it. Review collected by and hosted on G2.com.
Simplified abstraction for performing mapreduce... readable. Review collected by and hosted on G2.com.
Slow and clunky, especially when there are better options out there like Spark. Review collected by and hosted on G2.com.
1. SQL like syntax.
2. Ease of use.
3. Short learning curve
4. Ease of maintenance
5. Decrease in development time. This is the biggest advantage especially considering vanilla map-reduce jobs' complexity, time-spent and maintenance of the programs. Review collected by and hosted on G2.com.
1. Slow for larger queries
2. Errors need to be better
3. Support is less
4. Source and Sink need to be present
5. Especially the errors that Pig produces due to UDFS(Python) are not helpful at all. When something goes wrong, it just gives exec error in udf even if problem is related to syntax or type error, let alone a logical one. This is a big one. Review collected by and hosted on G2.com.
Chain multiple MR jobs into a single Pig job Review collected by and hosted on G2.com.
Less relevant now, Pig's popularity has definitely waned. Have moved to other tools Review collected by and hosted on G2.com.
I have used apache pig on my part time job which handles big data and apache pig scripts helped me a lot. I have created custom functions and it makes easier to handle complex and huge tasks and makes easier to maintain after configuration. Also the system optimization of pig script jobs helped me to focuse on semantics and so. The default mode i mean the map reduce mode is very efficient. Review collected by and hosted on G2.com.
Sometimes i feel that our data is not that big in order to be handled with pig script. Its documentation makes me sweat and takes a lot of time to get used to. Review collected by and hosted on G2.com.
Its super simple to learn. Probably within a day you can learn on syntax. It is nicer looking than complex hive queries. Step by step processing of data and ability to describe each relations is very useful. On top of that you can add UDF of your own. Review collected by and hosted on G2.com.
Pig on MapReduce is SLOW! Well, not really problem with Pig but overall its slow. Its stable and works but I think now its getting obsolete with introduction of Spark. Its a scripting language and now a fully blown programming language, so many of the basic features are not available and you will end up writing a lot of Java UDF. Review collected by and hosted on G2.com.
Pig is a great high level scripting language for operating with big datasets that work under the Apache's open-source project Hadoop. This software allows you to transform and optimize data operations into MapReduce, something that can be challenging with other platforms. I recommend this tool to my clients who need to manage a big list of users that will load a considerable amount of data daily. This can help you to clean, search, and declare independent execution plans easily. You can compare this tool with SQL programming, but the way this tool uses UDF helps you easily call the functions directly with Java, JS, Python, and of course, the big Ruby. Review collected by and hosted on G2.com.
At the beginning, it was a bit difficult to get used to working under its Pig Latin language; however, there is very good documentation online that allows you to manage your process. Apache Pig has many competitors, so they will need to optimize the system because sometimes the scripts won't get you the ideal results. Review collected by and hosted on G2.com.
It integrates quite well with a Hadoop cluster. I think is a must-have software to do big data things, such using it for map-reduce tasks Review collected by and hosted on G2.com.
The "programming" language, seems more a scripting language. It reminds me to ABAP Review collected by and hosted on G2.com.
Ease of use, its performance Review collected by and hosted on G2.com.
It got many competitor Review collected by and hosted on G2.com.
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