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10 problems with the Data Fabric architecture

A data fabric is a distributed data architecture with shared data assets, efficient data management and integration processes that are aggregated under one roof.

In a nutshell, data fabric aims to link any sort of data to everyone in an organization without having to physically provision servers or create large time-consuming IT integration projects instead of one data warehouse to rule them all. You have a virtualized view of your backend data sources using a data virtualization process accessible under one interface.

There is a lot of hype surrounding Data fabric and data mesh architectures, but it's not without its share of problems.


    1. High upfront costs

    2. High ongoing maintenance costs

    3. Vendor Lock In

    4. Underlying data sources must have pristine data quality and a robust master data managment (MDM) process

    5. Dependant on data source, system solid uptime

    6. Hire specialized SMEs needed to build write data virtualization coding layer

    7. Security/Access concerns

    • Unauthorized access
    • Having data access from one place creates a significant risk.

    8. Complexity in managing data fabric architecture

    9. Gaps in data could be rendered in correctly

    Say you have a customer in database one but is mislabeled or missing database two could give erroneous results upfront

    10. Single point of failure needs to have a robust contingency plan

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