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18 Ratings
235 Ratings
18 Ratings
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Score 7.6 out of 101
235 Ratings
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Score 8.3 out of 101

Likelihood to Recommend

Apache Pig

It is one great option in terms of database pipelining. It is highly effective for unstructured datasets to work with. Also, Apache Pig being a procedural language, unlike SQL, it is also easy to learn compared to other alternatives. But other alternatives like Apache Spark would be my recommendation due to the high availability of advanced libraries, which will reduce our extra efforts of writing from scratch
Kartik Chavan profile photo

Hadoop

  • Less appropriate for small data sets
  • Works well for scenarios with bulk amount of data. They can surely go for Hadoop file system, having offline applications
  • It's not an instant querying software like SQL; so if your application can wait on the crunching of data, then use it
  • Not for real-time applications
Bharadwaj (Brad) Chivukula profile photo

Pros

Apache Pig

  • 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
No photo available

Hadoop

  • Hadoop is a very cost effective storage solution for businesses’ exploding data sets.
  • Hadoop can store and distribute very large data sets across hundreds of servers that operate, therefore it is a highly scalable storage platform.
  • Hadoop can process terabytes of data in minutes and faster as compared to other data processors.
  • Hadoop File System can store all types of data, structured and unstructured, in nodes across many servers
No photo available

Cons

Apache Pig

  • 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.
No photo available

Hadoop

  • Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
  • Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
  • Hadoop cannot be used for running interactive jobs or analytics.
Mrugen Deshmukh profile photo

Likelihood to Renew

Apache Pig

No score
No answers yet
No answers on this topic

Hadoop

Hadoop 9.6
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 Pig

Apache Pig 10.0
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

Hadoop

Hadoop 9.0
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

Apache Pig

No score
No answers yet
No answers on this topic

Hadoop

Hadoop 6.1
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

Apache Pig

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 to Map Reduce. It is also a good option for working with unstructured datasets. Moreover, in certain cases, Apache Pig is much faster than Hive & Spark.
Kartik Chavan profile photo

Hadoop

We considered using Relationship database with Oracle Database and Java applications to process our data but ended up with Hadoop despite it being almost new. However, it proved to be the correct solution, we just need a little time to get started with Hadoop and it allows it to save cost on license and EC2 cost as we configure DataNode to be on-demand or spot instance, it also provides high performance and easy to implement as Map-Reduce function is quite simple.
Hung Vu profile photo

Return on Investment

Apache Pig

  • 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.
No photo available

Hadoop

  • Hadoop was thought to be cheap, but it is actually a very expensive proposition.
  • Support is required for Hadoop, so it is not free from a support perspective.
  • The overall benefit of Hadoop is extensive scale out storage and processing, but it is difficult to tie it to ROI in a major corporation.
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Pricing Details

Apache Pig

General

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

Hadoop

General

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

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