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You can configure the Map Reduce executor to write the Parquet files to the parent generated directory and to delete the Avro files after processing them.
You can also delete the temporary directories after the files are processed, as needed. Impala requires using the Invalidate Metadata command to refresh the Impala metadata cache each time changes occur in the Hive metastore.
Connect the data stream to the Hadoop FS or Map R FS destination to write data to the destination system using record header attributes.
The Hive Metadata processor passes the metadata record through the second output stream - the metadata output stream.
If your data contains nested fields, you would add a Field Flattener to flatten records as follows: pipeline that writes Avro log data to Kafka.
Now to process the metadata records - and to automatically create and update tables in Hive - you need the Hive Metastore destination.The basic Parquet implementation looks like this: As with Avro data, the Hive Metadata processor passes records through the first output stream - the data stream.Each time the destination closes an output file, it creates a file-closure event that triggers the Map Reduce executor to start an Avro to Parquet Map Reduce job.If your data contains nested fields, you would add a Field Flattener to flatten records as follows: .You configure the data-processing destination to generate events, and use a Map Reduce executor to convert the closed Avro files to Parquet.