MapR Competitors and Alternatives

The top MapR competitors are
  • Cloudera Distribution for Hadoop
  • Hortonworks Data Platform
  • Amazon EMR
  • DataStax
  • Apache Spark
Read reviews of MapR competitors and alternatives
Matthew Cloney
Real User
Data Science Engineer
Sep 27 2017

What is most valuable?

The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions. You can do it very easily and... more»

How has it helped my organization?

Well, I've been at two different companies and mostly I'll relate to my experience at HLI, Human Longevity, in San Diego. We used it for... more»

What needs improvement?

There were times where they would release new versions and it seemed to end up breaking old versions, which is very strange. It could have been... more»

Which other solutions did I evaluate?

No, not really. The reason that we used it at that company - when I got there, that's what they were using. It was because my boss was very big... more»

What other advice do I have?

I would say take advantage of the documentation that exists, there are a lot of tutorials, and there's a really good community. The... more»
Saravanan Ramaraj
Real User
Solution Architect at MIMOS Berhad
Feb 26 2017

What is most valuable?

* It's the one and only complete open source big data platform * Ambari-managed admin configuration for HDFS, YARN,... more»

How has it helped my organization?

* Maintenance of our own data lake in the enterprise-level * Storage and analysis of server logs * Applying Operational... more»

What needs improvement?

* Rolling upgrade * Disaster recovery features such as mirroring should be supported

What's my experience with pricing, setup cost, and licensing?

Completely use the community edition along with other features that can be implemented on top.

Which other solutions did I evaluate?

No previous solution was in place.

What other advice do I have?

Study, analyze, and compare with other big data platforms features according to your requirements before choosing the... more»
Abhijit Nayak
Consultant
Manager | Data Science Enthusiast | Management Consultant at a consultancy with 5,001-10,000 employees
Dec 10 2017

What do you think of Apache Spark?

Improvements to My Organization: Organisations can now harness richer data sets and benefit from use cases, which add value to their business functions. • Valuable Features: Distributed in memory processing. Some of the algorithms are resource heavy and executing this requires a lot of RAM and CPU. With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware. • Room for Improvement: Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing. • Use of Solution: Three to five years. • Stability Issues: At times when users do not know how to use Spark and request a lot of resources, then the underlying JVMs can crash, which is a big sense of worry.  • Scalability Issues: No...

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