HPE Ezmeral Data Fabric (MapR) vs. IBM watsonx.data

MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.

Incentivized

IBM watsonx.data is well suited for use cases were you have to combine various data sources to build a lakehouse. It provides a secure framework to gather data and provide access to it to build ML/AI models. It allows users to focus on prompts and business logic than spend time on data engineering.

Incentivized

• 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.

Incentivized

• It doesn't just store data but unlocks potential. I am able to analyse a vast amount of information, identify trends, and predict future outcomes.
• It not only gives me high quality but accessible data as well. It handles missing values, outliers and feature engineering with case.

Incentivized

• 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.

Incentivized

• The UI can be slow to respond at times (just like many cloud services thought)
• Semantic automation is still in beta and has no Japanese support yet
• Loading data to the platform can be slow at times

I can give it 10/10 due to its impact in data analysis management. This is the right software for driving business insights and enhancing effective decision making. The infrastructure has the formal tools for preparing data before using it to make critical decisions. The NLP has enhanced standard analysis of unstructured data from social media websites.

Incentivized

I don't believe there is as much support for MapR yet compared to other more widely known products.

Incentivized

Oracle really cost effective solution, where it has the support of community, with rich integration of all wide range of oracle products. Amazon SageMaker is another cost effective solution, where is tightly coupled with AWS platform, in terms of performance it copes up really well for the large data sets. So that we can train AI data sets models well.

Incentivized

• 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

Incentivized

• Incorporates cognitive computing, natural language processing, machine learning and data analysis.
• Includes structured, semi-structured and unstructured data.
• Data visualization.
• Reports on time.

Incentivized