Apache Pig vs. HPE Ezmeral Data Fabric (MapR)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Pig
Score 8.2 out of 10
N/A
Apache Pig is a programming tool for creating MapReduce programs used in Hadoop.N/A
HPE Ezmeral Data Fabric (MapR)
Score 9.4 out of 10
N/A
HPE Ezmeral Data Fabric (formerly MapR, acquired by HPE in 2019) is a software-defined datastore and file system that simplifies data management and analytics by unifying data across core, edge, and multicloud sources into a single platform. Just as a loom weaves multiple threads into a single piece of fabric, HPE Ezmeral Data Fabric weaves distributed data into a single enterprise-wide data layer that ingests, processes, and stores data once and then makes it available for reuse across multiple…N/A
Pricing
Apache PigHPE Ezmeral Data Fabric (MapR)
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Apache PigHPE Ezmeral Data Fabric (MapR)
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache PigHPE Ezmeral Data Fabric (MapR)
Considered Both Products
Apache Pig

No answer on this topic

HPE Ezmeral Data Fabric (MapR)
Chose HPE Ezmeral Data Fabric (MapR)
I don't believe there is as much support for MapR yet compared to other more widely known products.
Top Pros
Top Cons
Best Alternatives
Apache PigHPE Ezmeral Data Fabric (MapR)
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache PigHPE Ezmeral Data Fabric (MapR)
Likelihood to Recommend
8.0
(9 ratings)
7.2
(4 ratings)
Usability
10.0
(1 ratings)
-
(0 ratings)
Support Rating
6.0
(2 ratings)
-
(0 ratings)
User Testimonials
Apache PigHPE Ezmeral Data Fabric (MapR)
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.
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Hewlett Packard Enterprise
MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
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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.
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Hewlett Packard Enterprise
  • MapR had very fast I/O throughput. The write speed was several times faster than what we could achieve with the other Hadoop vendors (Cloudera and Hortonworks). This is because MapR does not use HDFS, which is essentially a "meta filesystem". HDFS is built on top of the filesystem provided by the OS. MapR has their filesystem called MapR-FS, which is a true filesystem and accesses the raw disk drives.
  • The MapR filesystem is very easy to integrate with other Linux filesystems. When working with HDFS from Apache Hadoop, you usually have to use either the HDFS API or various Hadoop/HDFS command line utilities to interact with HDFS. You cannot use command line utilities native to the host operation system, which is usually Linux. At least, it is not easily done without setting up NFS, gateways, etc. With MapR-FS, you can mount the filesystem within Linux and use the standard Unix commands to manipulate files.
  • The HBase distribution provided by MapR is very similar to the Apache HBase distribution. Cloudera and Hortonworks add GUIs and other various tools on top of their HBase distributions. The MapR HBase distribution is very similar to the Apache distribution, which is nice if you are more accustomed to using Apache HBase.
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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.
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Hewlett Packard Enterprise
  • It takes time to get latest versions of Apache ecosystem tools released as it has to be adapted.
  • When you have issues related to Mapr-FS or Mapr Tables, its hard to figure them out by ourselves.
  • Sometime new ecosystem tools versions are released without proper QA.
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Usability
Apache
It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
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Hewlett Packard Enterprise
No answers on this topic
Support Rating
Apache
The documentation is adequate. I'm not sure how large of an external community there is for support.
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Hewlett Packard Enterprise
No answers on this topic
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.
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Hewlett Packard Enterprise
I don't believe there is as much support for MapR yet compared to other more widely known products.
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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.
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Hewlett Packard Enterprise
  • Increased employee efficiency for sure. Our clients have various levels of expertise in their deployment and user teams, and we never receive complaints about MapR.
  • MapR is used by one of our financial services clients who uses it for fraud detection and user pattern analysis. They are able to turn around data much faster than they previously had with in-house applications
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