![]() You can view the status of the job from the Jobs page in the AWS Glue Console. ![]() Click Run Job and wait for the extract/load to complete. With the script written, we are ready to run the Glue job. RetDatasink4 = glueContext.write_dynamic_om_options(frame = dynamic_dframe, connection_type = "s3", connection_options =, format = "csv", transformation_ctx = "datasink4") #It is possible to write to any Amazon data store (SQL Server, Redshift, etc) by using any previously defined connections. #Write the DynamicFrame as a file in CSV format to a folder in an S3 bucket. #Convert DataFrames to AWS Glue's DynamicFrames Objectĭynamic_dframe = omDF(source_df, glueContext, "dynamic_df") Server=localhost Port=7474 User=my_user Password=my_password ").option("dbtable","ProductCategory").option("driver","4jDriver").load() #Note the populated JDBC URL and driver class name #Use the CData JDBC driver to read Neo4J data from the ProductCategory table into a DataFrame Make any necessary changes to the script to suit your needs and save the job.įrom awsglue.utils import getResolvedOptionsįrom awsglue.dynamicframe import DynamicFrameĪrgs = getResolvedOptions(sys.argv, ) For more information on obtaining this license (or a trial), contact our sales team.īelow is a sample script that uses the CData JDBC driver with the PySpark and AWSGlue modules to extract Neo4J data and write it to an S3 bucket in CSV format. To host the JDBC driver in Amazon S3, you will need a license (full or trial) and a Runtime Key (RTK). Either double-click the JAR file or execute the JAR file from the command-line.įill in the connection properties and copy the connection string to the clipboard.
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