@Alvin Jin To answer your question about which processors to use: it depends on what you want to do with the whole CSV file. Your question only mentions splitting and ignoring the header, the CSVReader takes care of that. The record-aware processors in NiFi 1.3.0 include: ConsumeKafkaRecord_0_10: Gets messages from a Kafka topic, bundles into a single flow file instead of one per message ConvertRecord: Converts records from one data format to another (Avro to JSON, e.g.) LookupRecord: Uses fields from a record to lookup a value, which can be added back to the record PartitionRecord: Groups "like" records (based on user-provided criteria) into individual flow files PublishKafkaRecord_0_10: Posts messages to a Kafka topic PutDatabaseRecord: Executes a specified operation (INSERT, UPDATE, DELETE, e.g.) on a database for each record in a flow file PutElasticsearchHttpRecord: Executes a specified operation ("index", e.g.) on an Elasticsearch cluster for each record in a flow file QueryRecord: execute SQL queries on fields from the records. This can be used to filter, aggregate, etc. SplitRecord: Splits records into smaller flow files. Usually only used when downstream processors are not record-aware UpdateRecord: Updates field(s) in each record of a flow file Also I wanted to mention, if for some reason all your CSV columns are strings, you can set "Schema Access Strategy to "Use String Fields From Header", and then you don't need a schema or schema registry. Otherwise if you want to provide a schema, you're not required to use a schema registry, you can just paste your schema into the Schema Text property. and set "Schema Access Strategy" to "Use Schema Text Property".
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