Hitachi Lumada vs. HPE Data Fabric

Organizations may use sensors like RFID and break beams to automatically monitor components as they travel through the assembly. Real-time data from IoT may help managers and supervisors monitor the performance of their teams. With this level of transparency, bottlenecks can be identified, problems can be pinpointed, and progress can be made more quickly.

Incentivized

If you need Hadoop and just need raw speed for I/O and have a Hadoop savvy group of engineers who don't need/like web UIs, then MapR is a great fit for you. If you are new to Hadoop or have DevOps folks that are not Hadoop gurus, choosing MapR as your Hadoop vendor will have a steeper learning curve as you will need to do more training and build more admin consoles for them.

Incentivized

• Remotely production monitoring.
• Track workforce productivity.
• Asset optimization.

Incentivized

• MapR allows easy integration with HBase and MapR DB.
• Easy trial server setup for product testing.
• Excellent training program to help new users get up-to-date with MapR and related products.

Incentivized

• Data security can be improved.
• Adoption flexibility.
• Steep learning curve.

Incentivized

• I think MapR's main problem is name recognition. Hortonworks and Cloudera both are big names in the industry, but their deployment mechanisms are a little more difficult to use, especially when trying to fully automate it's deployment.
• Documentation could always be better. But really, if that's your main weakness, it's everybody's weakness.

Incentivized

A lot of components are available and able to use what you need. Module library, Advanced analytics. Batch data processing. Variety of ways to integrate. Cost-effective. These are some of the ways Hitachi Lumada ranks above the ones we have used.

Incentivized

Hortonworks and Cloudera are both sort of hacky. We have to do a lot of extra steps to automate those two. MapR has far fewer issues and doesn't force you into a once size fits all deployment scenario. There are multiple ways to deploy and some are more amenable to automation, MapR just has that in spades

Incentivized

• Overall cost.
• Financial/organizational viability.
• Strong services expertise.

Incentivized

• Less manual intervention for maintaining a cluster.

Incentivized