Apache Hadoop vs. Apache Pig

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Hadoop
Score 7.1 out of 10
N/A
Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.N/A
Apache Pig
Score 8.4 out of 10
N/A
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.N/A
Pricing
Apache HadoopApache Pig
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
HadoopApache Pig
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 HadoopApache Pig
Considered Both Products
Hadoop
Chose Apache Hadoop
  • For real-time streaming, use Spark; can provide a stark contrast to the way MR works
  • Hadoop offers a scalable, cost-effective and highly available solution for big data storage and processing.
  • Amazon Redshift is somewhat closer to Hadoop. But to analyze Petabytes of data Hadoop …
Chose Apache Hadoop
Hadoop provides storage for large data sets and a powerful processing model to crunch and transform huge amounts of data. It does not assume the underlying hardware or infrastructure and enables the users to build data processing infrastructure from commodity hardware. All the …
Apache Pig
Chose Apache Pig
- Provided better ways for optimized hadoop jobs than Hive but not anymore.
- Spark DSL is much more advanced and compute times are significantly less.
Top Pros
Top Cons
Best Alternatives
Apache HadoopApache Pig
Small Businesses

No answers on this topic

No answers on this topic

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 8.7 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 8.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache HadoopApache Pig
Likelihood to Recommend
8.9
(36 ratings)
8.2
(9 ratings)
Likelihood to Renew
9.6
(8 ratings)
-
(0 ratings)
Usability
8.5
(5 ratings)
10.0
(1 ratings)
Performance
8.0
(1 ratings)
-
(0 ratings)
Support Rating
7.5
(3 ratings)
6.0
(1 ratings)
Online Training
6.1
(2 ratings)
-
(0 ratings)
User Testimonials
Apache HadoopApache Pig
Likelihood to Recommend
Apache
Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.
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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.
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Pros
Apache
  • Handles large amounts of unstructured data well, for business level purposes
  • Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here.
  • Decent for large ETL pipelines and logging free-for-alls because of this, also.
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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.
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Cons
Apache
  • Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates
  • Not for small data sets
  • Data security needs to be ramped up
  • Failure in NameNode has no replication which takes a lot of time to recover
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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.
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Likelihood to Renew
Apache
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
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Apache
No answers on this topic
Usability
Apache
Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.
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Apache
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
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Support Rating
Apache
We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.
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Apache
The documentation is adequate. I'm not sure how large of an external community there is for support.
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Online Training
Apache
Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
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Apache
No answers on this topic
Alternatives Considered
Apache
Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.
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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.
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Return on Investment
Apache
  • There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me.
  • Hadoop is quite interesting due to its new and improved features plus innovative functions.
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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.
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