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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    HPE Data Fabric

    Score9.4 out of 10
    N/AHPE 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.N/A

    IBM watsonx.data

    Score8.4 out of 10
    N/AWatsonx.data is presented as an open, hybrid and governed data store that makes it possible for enterprises to scale analytics and AI with a fit-for-purpose data store, built on an open lakehouse architecture, supported by querying, governance and open data formats to access and share data.N/A
    Pricing
    HPE Data FabricIBM watsonx.data
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Offerings
    Pricing Offerings
    HPE Data FabricIBM watsonx.data
    Free Trial
    NoYes
    Free/Freemium Version
    NoNo
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details——
    More Pricing Information
    Community Pulse
    HPE Data FabricIBM watsonx.data
    Considered Both Products
    Hewlett Packard Enterprise (HPE)
    No answer on this topic
    IBM
    No answer on this topic
    Key User Insights
    Would buy again
    No answers on this topic
    94%
    Would buy again
    34 Answers
    Delivers good value for the price
    No answers on this topic
    100%
    Delivers good value for the price
    30 Answers
    Happy with the feature set
    No answers on this topic
    97%
    Happy with the feature set
    35 Answers
    Lived up to sales and marketing promises
    No answers on this topic
    100%
    Lived up to sales and marketing promises
    23 Answers
    Implementation went as expected
    No answers on this topic
    100%
    Implementation went as expected
    29 Answers
    Best Alternatives
    HPE Data FabricIBM watsonx.data
    Small Businesses
    No answers on this topic
    No answers on this topic
    Medium-sized Companies
    HBase
    Score7.3 out of 10
    SAP Business Data Cloud
    Score8.6 out of 10
    Enterprises
    Cassandra
    Score9 out of 10
    SAP Business Data Cloud
    Score8.6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    HPE Data FabricIBM watsonx.data
    Likelihood to Recommend
    7.2
    (4 ratings)
    8.2
    (34 ratings)
    Likelihood to Renew
    -
    (0 ratings)
    7.3
    (4 ratings)
    Usability
    -
    (0 ratings)
    8.0
    (10 ratings)
    Availability
    -
    (0 ratings)
    8.2
    (1 ratings)
    Performance
    -
    (0 ratings)
    8.2
    (1 ratings)
    Support Rating
    -
    (0 ratings)
    9.1
    (4 ratings)
    Online Training
    -
    (0 ratings)
    8.2
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.2
    (1 ratings)
    Configurability
    -
    (0 ratings)
    8.2
    (1 ratings)
    Ease of integration
    -
    (0 ratings)
    7.3
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    7.3
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    8.2
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    8.2
    (1 ratings)
    User Testimonials
    HPE Data FabricIBM watsonx.data
    Likelihood to Recommend
    Hewlett Packard Enterprise (HPE)
    MapR is more well-suited for people who know what they are doing. I consider MapR the Hadoop distribution professionals use.
    Incentivized
    Read full review
    IBM
    Real-time transaction processing (both reads and writes) is where DataStax Enterprise shines. It's very fast with linear scalability should more resources be needed. Additional nodes are added very easily. DataStax Enterprise on its own (without Solr or Spark enabled) isn't well suited for long complicated reports. The data model doesn't support joining multiple tables together which is common in BI reporting.
    Read full review
    Pros
    Hewlett Packard Enterprise (HPE)
    • 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
    Read full review
    IBM
    • Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
    • Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
    • Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
    • Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.
    Incentivized
    Read full review
    Cons
    Hewlett Packard Enterprise (HPE)
    • 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
    Read full review
    IBM
    • Interface do need some improvement to be more intuitive for analysts like me.
    • Configuring multiple data sources can require support, as it isn't easy for new users. So, it can be made easier.
    • Additional ETL features can be added, so users don't have to switch between tools.
    Incentivized
    Read full review
    Likelihood to Renew
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    As an open source technology Cassandra can be readily used with or without any commercial support. DataStax provides value-added services and features, and in the end it is up to individual situations to strike a balance between the desirability of such support/service versus the associated cost.
    Incentivized
    Read full review
    Usability
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    DataStax has a good community built around it and has amazing scalability options. Though the initial setup is a bit costly, in the long run, it makes up for it. It also has powerful monitoring tools and a clean UI.
    Incentivized
    Read full review
    Reliability and Availability
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    good recovery features
    Incentivized
    Read full review
    Performance
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    scalable product
    Incentivized
    Read full review
    Support Rating
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.
    Read full review
    Online Training
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    easy to follow documentation, support is there when needed
    Incentivized
    Read full review
    Implementation Rating
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    use saas service
    Incentivized
    Read full review
    Alternatives Considered
    Hewlett Packard Enterprise (HPE)
    I don't believe there is as much support for MapR yet compared to other more widely known products.
    Incentivized
    Read full review
    IBM
    Claude is better for troubleshooting actual OPS/REXX but that's not an IBM product so I understand why IBM watsonx.data isn't great for that, but if IBM watsonx.data did help with code then it would be the only product that I use.
    Incentivized
    Read full review
    Scalability
    Hewlett Packard Enterprise (HPE)
    No answers on this topic
    IBM
    cognos integration works great
    Incentivized
    Read full review
    Return on Investment
    Hewlett Packard Enterprise (HPE)
    • 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
    Read full review
    IBM
    • for one automation project, we managed to cut cloud storage costs by a third through IBM watsonx.data's lakehouse optimization
    • data integration projects have had a 20 % reduction in turnaround times. Can only imagine how that will improve with the Claude partnership
    Incentivized
    Read full review
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