Drift file not updating
The destination, upon receiving the metadata record from the Hive Metadata processor, creates or updates Hive tables as needed.
You connect the Hive Metadata processor data output stream to a Hadoop FS destination and configure it to use the information in record headers.
enables creating and updating Hive tables based on record requirements and writing data to HDFS or Map R FS based on record header attributes.
You can use the full functionality of the solution or individual pieces, as needed. When processing Parquet data, the solution generates temporary Avro files and uses the Map Reduce executor to convert the Avro files to Parquet.
Using the tag header attribute and other user-defined expressions, a Hive Metadata processor can determine the database, table, and partition to use for the target directory and write that information along with the Avro schema to the record header, including file roll indicator when necessary.
The File Tail origin in the pipeline processes data from several different web services, tagging each record with a "tag" header attribute that identifies the service that generated the data.
The Map R FS destination then writes the data to the updated table.
When writing data without the new fields to the updated table, the destination inserts null values for the missing fields.
It generates metadata records that describe the necessary changes and passes it to the Hive Metastore destination.
The Hive Metadata processor also adds information to the header of each record and passes the records to the Hadoop FS destination or the Map R FS destination.Now you want a new pipeline to pass the data to HDFS where it can be stored and reviewed, and you'd like the data written to tables based on the web service that generated the data.