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data-analytics/bda-ai-customer-challenges.html
customer, challenge, data lake, repository, syncing, moving data
This page discusses the challenges that a customer might face when trying to access data from big-data analytics for AI operations.

Customer challenges

Customers might face the following challenges when trying to access data from big-data analytics for AI operations:

  • Customer data is in a data lake repository. The data lake can contain different types of data such as structured, unstructured, semi-structured, logs, and machine-to-machine data. All these data types must be processed in AI systems.

  • AI is not compatible with Hadoop file systems. A typical AI architecture is not able to directly access HDFS and HCFS data, which must be moved to an AI-understandable file system (NFS).

  • Moving data lake data to AI typically requires specialized processes. The amount of data in the data lake can be very large. A customer must have an efficient, high-throughput, and cost-effective way to move data into AI systems.

  • Syncing data. If a customer wants to sync data between the big-data platform and AI, sometimes the data processed through AI can be used with big data for analytical processing.