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Why might a "back pressure" mechanism on NiFi connections (limiting how many FlowFiles/how much data can queue up) be important for a production data flow?
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Why might a "back pressure" mechanism on NiFi connections (limiting how many FlowFiles/how much data can queue up) be important for a production data flow?
What does NiFi's "data provenance" feature provide?
Why might NiFi be a reasonable choice for a "data ingestion" use case -- pulling data from many disparate external sources into a central system?
Why might a "RouteOnAttribute" (or similar conditional routing) processor be valuable in a NiFi flow handling data from multiple sources with different downstream requirements?
What does it mean for NiFi to support "guaranteed delivery" for data moving through a flow?
What is a general reason a data engineering team might choose a tool like NiFi over writing and scheduling custom ETL scripts for moving/transforming data between systems?
What is a "FlowFile" in NiFi's data model?
Why might NiFi's ability to modify a flow's configuration while it's actively running (without stopping the whole system) be valuable operationally?
What is the purpose of NiFi's "controller services," a concept distinct from individual processors?
Why might a data engineering team combine NiFi with other specialized tools (like a message queue such as Kafka, or a stream processing framework) rather than relying on NiFi alone for an entire data pipeline?
What is a general trade-off consideration when adopting a visual flow-based tool like NiFi versus writing equivalent logic in code, from a version-control and code-review perspective?
What is a "processor" in NiFi's visual flow-building model?