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Apache Pig vs. IBM Analytics for Apache Spark

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Pig
Score 8.4 out of 10
N/A
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.
$0
IBM Analytics for Apache Spark
Score 8.0 out of 10
N/A
IBM Analytics for Apache Spark for Cloud is a service designed to provide the fast in-memory performance of Apache Spark without the hassle of self-managing Spark clusters, relying instead on the convenience of IBM Cloud.N/A
Pricing
Apache PigIBM Analytics for Apache Spark
Editions & Modules
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Offerings
Pricing Offerings
Apache PigIBM Analytics for Apache Spark
Free Trial
NoNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Apache PigIBM Analytics for Apache Spark
Best Alternatives
Apache PigIBM Analytics for Apache Spark
Small Businesses

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Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 7.2 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 7.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache PigIBM Analytics for Apache Spark
Likelihood to Recommend
8.2
(9 ratings)
-
(0 ratings)
Usability
10.0
(1 ratings)
-
(0 ratings)
Support Rating
6.0
(1 ratings)
-
(0 ratings)
User Testimonials
Apache PigIBM Analytics for Apache Spark
Likelihood to Recommend
Apache
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.
Verified User
Anonymous
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IBM
No answers on this topic
Pros
Apache
  • 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.
Sourov K Chowdhury | TrustRadius Reviewer
Sourov K Chowdhury
Database Software Engineer
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IBM
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Cons
Apache
  • 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.
Kartik Chavan | TrustRadius Reviewer
Kartik Chavan
Data Analyst
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IBM
No answers on this topic
Usability
Apache
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Subhadipto Poddar | TrustRadius Reviewer
Subhadipto Poddar
Research Assistant
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IBM
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Support Rating
Apache
The documentation is adequate. I'm not sure how large of an external community there is for support.
Jordan Moore | TrustRadius Reviewer
Jordan Moore
Software Consultant
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IBM
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Alternatives Considered
Apache
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.
Verified User
Anonymous
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IBM
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Return on Investment
Apache
  • 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.
Verified User
Anonymous
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IBM
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