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Apache NiFi 3Apache NiFi RecordPath Guide Date of Publish: 20180813http://docs.hortonworks.comContents Overview....................................................................................................................
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How to fill out apache nifi record path

01
Open your Apache NiFi application and navigate to the processor configuration.
02
Select the processor that requires the Record Path.
03
In the properties tab, locate the 'Record Reader' or 'Record Writer' properties.
04
Click on the 'Record Path' field to edit it.
05
Use NiFi's Record Path syntax to specify the desired path (e.g., '/data/field').
06
Test the Record Path to ensure it correctly points to the intended data field.
07
Save the configuration and apply changes to the processor.

Who needs apache nifi record path?

01
Data engineers who need to transform and route data efficiently.
02
Analysts who require access to specific fields within complex data structures.
03
Developers working on data integration tasks requiring precise data extraction.
04
Organizations leveraging data pipelines for real-time analytics and processing.
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Apache NiFi Record Path is a query language used to access and manipulate data records within Apache NiFi's data flow. It allows users to select and transform data from records in a structured way.
Typically, developers, data engineers, or system administrators working with Apache NiFi are required to use Record Path to manipulate and process data effectively within their data flows.
To fill out an Apache NiFi Record Path, you need to specify the path to the data fields you want to access using the Record Path syntax. This can involve using dot notation, array indexing, and various functions to navigate through nested data structures.
The purpose of Apache NiFi Record Path is to allow users to read, filter, and transform data records easily. It facilitates data processing and integration tasks by providing a concise way to access and manipulate structured data.
Information to be reported using Apache NiFi Record Path usually includes the data types, field names, and values of various records as specified in the flow. The exact information required can vary based on the application and the design of the data flow.
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