Typical big data analytics management issues:

  • Unscheduled system downtime
  • Tedious management of existing system
  • Constantly increasing temporary storage space and/or memory


  • Scaling is hard
    • Designing and setting up new hardware takes too long
    • Configuring new hardware to work with older generations is error-prone or impossible
    • Common tasks for installing new nodes are manual: repartitioning, re-indexing, or re-creating constraints
  • System is not designed for fault-tolerance
    • Integrating high availability and recovering from failures is hard
    • Restoring the replicas/backups when hardware components fail is complex and time-consuming
    • Detecting and recovering from transient software failures is difficult or impossible
    • System management is difficult
    • No single-system view of the data and analytics workloads
    • Queries cannot be monitored
    • Run-away queries cannot be cancelled
    • Adding/removing hardware to the cluster is time consuming and error-prone
    • Constantly tuning the system is time consuming and difficult


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