I rate HPE Ezmeral Data Fabric seven out of 10. I haven't seen much innovation from the company in the last couple of years. Only in the last four or five months have I seen some dynamism in them. Something has changed, and now they're looking futuristic. There wasn't much on the roadmap in the past two or three years. Maybe they had some ideas they weren't talking about, but I've seen a shift in the last few months. 

My advice to potential users is to understand your use case. You wouldn't use this product in certain scenarios, but Ezmeral is a good option if you need to meet multiple requirements with a single product. Other products might work better when you have a specialized requirement for one specific domain. You go for Ezmeral if you're looking for a one-stop shop. 

I recommend the product to those who plan to use it.

I rate the overall product between seven and a half to eight out of ten.

I would rate the product a nine out of ten. The tool is a fabulous solution to compare with the others. Though there is a difficulty in installing the product the first time, after that it is easy. 

I would recommend this solution to potential users.

On a scale from one to ten, I would give HPE Ezmeral Data Fabric a seven.

The biggest drawback is that it has vendor locking. It's proprietary in MapR distribution and extract cost. The positive side is that its storage is inherently within the MapR-FS and there is no region server overhead. Consider these aspects before making your decision.

I used to work with the Map Plus form, for two years, in 2016/2017, and I went back to the product to see what kind of new improvements they made to the platform. I'm not currently using it at the moment. I'm planning to get back to it soon.

We were, at that time, creating another product to be sold on the market and we wanted something that could educate different sources of data. Their connector list wasn't long enough. We stopped after one year, maybe less, because we started in September 2016, working with that platform, and then we switched in 2017. I think less than a year.

Today, I would recommend the solution. I had exactly the same question from someone who was interviewing me for a job, and he wanted to know on which platform I worked before, and I listed Cloudar, Hortonworks, and MapR, and he wanted my opinion on the three, and my first choice. My first choice is MapR, as it is more adaptable to different contexts, and it could be customized in some way to fit the different needs, and this is my first choice and my first advice to people who ask me about this particular platform.

I would rate this solution 8 out of 10.

I rate it an eight because, from my point of view, such a platform will not be used as a standalone solution, it has to be integrated into an information system, with operational systems together on the back end and also to write positions that could be back integrated into the operational system recommendations, positions, and so on. Then I think it creates API architecture, a micro-services orientation of the platform.

In terms of Hadoop growing pains, there was some pain, compared to the M7 license which I found largely painless. However, it was nowhere near the amount of pain I had playing vanilla Hadoop.

MapR is a great distribution, although I have limited experience with other distributors. I know that I have never come across the name node issue, and MapR even translates some posix calls to HDFS and abstracts away a lot of the complexity. It's enterprise ready, and comes with a host of features that really simplify some scenarios.

For example, MapR NFS provides great flexibility when it comes to connecting to the cluster and ingesting data. It also has true multi-tenancy, which allows clients to trust our platform with their data, knowing that there are several layers of security, including encryption authentication and volume access control.

I would recommend using the free MapR training resources and posting on MapR answers. The sandbox is a great place to start, and also there is pretty extensive documentation on their site.

Make sure the third-party applications you are trying to implement with it are certified with this product. If not, please start discussing with the MapR team that they can start a partnership with the third-party application vendor.

Please make sure following answers are clearly known:

• Define the Objectives, and what you are not currently able to achieve without Big Data Tools.
• Why is the data big - because of velocity of data pouring in. th number of years of data, or a sudden increase in business operations
• Define a sample use case / example of the expectations from Big Data analytics
• Carry out a POC & review the objectives.

I highly recommend MapR.

I don't have any advice because the installation documentation is superb.