forPath(spark, delta_table_path) # Separate CDC data into inserts, updates, and deletes inserts_updates_df = cdc_df.filter(col("op_flag").isin("I", "U")) deletes_df = cdc_df.filter(col("op_flag") == "D") # UPSER
commonConfig = {'className':'org.apache.hudi','hoodie.datasource.hive_sync.use_jdbc':'false','hoodie.datasource.write.precombine.field':'MTime','hoodie.datasource.write.recordkey.field':'id','hoodie.table.name':'ny_yellow_trip_data','hoodie.consistency.check.enabled':'true','hoodie.dat...
and you want to move it into an S3 data lake on a continuous basis, so that your downstream applications or consumers can use it for analytics. After your initial data movement to Amazon S3, you’re supposed to receive incremental updates from the source database as ...
This statement creates an external table namedinsurance_policiesthat points to a Delta Lake dataset stored in the specified S3 location. Thetable_typeproperty is set toDELTAto indicate that this is a Delta Lake table. Once created, you can query this table using standard SQL syntax in ...
forPath(spark, delta_table_path) # Separate CDC data into inserts, updates, and deletes inserts_updates_df = cdc_df.filter(col("op_flag").isin("I", "U")) deletes_df = cdc_df.filter(col("op_flag") == "D") # UPSERT process delta_table.alias("prev_df").merge(...
forPath(spark, delta_table_path) # Separate CDC data into inserts, updates, and deletes inserts_updates_df = cdc_df.filter(col("op_flag").isin("I", "U")) deletes_df = cdc_df.filter(col("op_flag") == "D") # UPSERT process delta_table.alias("prev_df").merge(...
forPath(spark, delta_table_path) # Separate CDC data into inserts, updates, and deletes inserts_updates_df = cdc_df.filter(col("op_flag").isin("I", "U")) deletes_df = cdc_df.filter(col("op_flag") == "D") # UPSERT process delta_table.alias("prev_df").merge( source...