What users are saying about

Apache Pig

18 Ratings

Hadoop

211 Ratings

Apache Pig

18 Ratings
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Score 7.3 out of 101

Hadoop

211 Ratings
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Score 8 out of 101

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Likelihood to Recommend

Apache Pig

Apache Pig is well suited as part of an ongoing data pipeline where there is already a team of engineers in place that are familiar with the technology since at this point I would consider it relatively depreciated since there are more suitable technologies that have more robust and flexible APIs with the added benefit of being easier to learn and apply. For ad-hoc needs, I would recommend Hive or Spark-SQL if a SQL-esque language makes sense otherwise to make use of Spark + a Notebook technology such as Apache Zeppelin. For production data pipelines I would recommend Apache Spark over Apache Pig for its performance, ease of use, and its libraries.
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Hadoop

Hadoop is not for the faint of heart and is not a technology per se but an ecosystem of disparate technologies sitting on top of HDFS. It is certainly powerful but if, like me, you were handed this with no prior knowledge or experience using or administering this ecosystem the learning curve can be significant and ongoing having said that I don't think currently there are many other opensource technologies that can provide the flexibility in the "big data" arena especially for ETL or machine learning.
Mark Gargiulo profile photo

Pros

  • Provides a decent abstraction for Map-Reduce jobs, allowing for a faster result than creating your own MR jobs
  • Good documentation and resources for learning Pig Latin (the Domain Specific Language of the Apache Pig platform)
  • Large community allows for easy learning, support, and feature improvements/updates
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  • Provides a reliable distributed storage to store and retrieve data. I am able to store data without having to worry that a node failing might cause the loss of data.
  • Parallelizes the task with MapReduce and helps complete the task faster. The ease of use of MapReduce makes it possible to write code in a simple way to make it run on different slaves in the cluster.
  • With the massive user base, it is not hard to find documentation or help relating to any problem in the area. Therefore, I rarely had any instances where I had to look for a solution for a really long time.
Muhammad Fazalul Rahman profile photo

Cons

  • Improve Spark support and compatibility
  • Spark and Hive are already being used main-stream, both of them have an instruction set that is easier to learn and master in a matter of days. While apache pig used to be a great alternative to writing java map reduce, Hive after significant updates is now either equal or better than pig.
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  • While its open source nature provides a lot of benefits, there are multiple stability issues that arise due to it.
  • Limited support for interactive analytics.
Tom Thomas profile photo

Likelihood to Renew

No score
No answers yet
No answers on this topic
Hadoop9.6
Based on 8 answers
Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Bhushan Lakhe profile photo

Usability

Apache Pig10.0
Based on 1 answer
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Subhadipto Poddar profile photo
Hadoop9.0
Based on 3 answers
I found it really useful during my academic projects. Data handling for large data sets was easy with Hadoop. It used to work really fast for bigger data sets. I found it reliable.
Tushar Kulkarni profile photo

Online Training

No score
No answers yet
No answers on this topic
Hadoop6.1
Based on 2 answers
Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
Bhushan Lakhe profile photo

Alternatives Considered

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.
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Fast and scalable. More reliable as compared to the other products I have used.
Gaurav Kasliwal profile photo

Collaboration and Sharing

No score
No answers yet
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Hadoop7.7
Based on 10 answers
Parallel processing can be effectively done with the help of Hadoop's distributed file system. Overall Hadoop is a great product.
Tushar Kulkarni profile photo

Data Integration

No score
No answers yet
No answers on this topic
Hadoop8.7
Based on 10 answers
According to me, the data source connectivity of Hadoop is really good. I had faced any issues while using Hadoop for data source connectivity.
Gaurav Kasliwal profile photo

Return on Investment

  • 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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  • You dont need to pay a heavy licensing fee for Hadoop. You save money.
  • It is open source technology so some times you need to purchase support from Cloudera or Hortonworks.
Ajay Jha profile photo

Pricing Details

Apache Pig

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Hadoop

General
Free Trial
Free/Freemium Version
Yes
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details