Apache Pig vs. Cloudera Distribution Hadoop (CDH)

Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.

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Cloudera Distribution Hadoop (CDH) does a lot of things really well - especially on the analytical front. That being said the product is quite expensive. There are seemingly numerous applications that do the same thing on the functional level that are much more cost effecient for enterprise teams. If I were recommending this to a colleague I would let them know the product will absolutely be able to get the job done for their use case, but there are more efficient options

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• Its performance, ease of use, and simplicity in learning and deployment.
• Using this tool, we can quickly analyze large amounts of data.
• It's adequate for map-reducing large datasets and fully abstracted MapReduce.

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• Solid and robust set of integrations
• Easy to use and easy to deploy across the enterprise
• Reliability - never lost any info
• Simple and clean interface

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• UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors.
• Being in early stage, it still has a small community for help in related matters.
• It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement.

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• The price is quite high competitively speaking
• Hard to learn more robust functions and custom options without experience

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It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.

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The documentation is adequate. I'm not sure how large of an external community there is for support.

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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.

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In terms of functionality there's not much difference, both get the job done. Amazon was more cost-efficient for our team, but this could vary depending on the size of the business. One thing I did notice was that Cloudera seemed to management and spit out our deployments faster than AWS.

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• 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.

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• Saves time by automating typically manual processes (data management, lifecyle AI etc)
• Quick deployments and analytics allow for faster time-to-value

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