![]() ![]() If you're reading information from a streaming source, you provide your job with source information Information about your source data location, see Connection types and options for ETL in For an explanation of how to provide your script Provide your script information about your source data format, see Data format options for inputs and outputs inĪWS Glue. Refer to the supplemental documentation about format_options andĬonnection_parameters to identify your required parameters. You will need to provide more detailed parameters describing your data than when using Of your source, see the create_dynamic_frame_from_options If you need to directly provide your job with configuration that describes the structure and location This code is a portion of the generated sample script.Ĭreate_dynamic_frame_from_catalog is used to connect to tables in the AWS Glue Data Catalog. In this procedure, you write the following code. YouĬan learn about job bookmarks in the following section, Optional - Enable job One of these features is job bookmarks, which you can optionally configure in this tutorial. Your script can run without a Job object, but theīest practice is to initialize it so that you don't encounter confusion if those features are later The Job object sets configuration and tracks the state More information about resolving job parameters, see Accessing You won't need to use them directly withinĬall getResolvedOptions to prepare your job arguments for use within the script. The Spark engine available inside the AWS Glue job. Initialize a SparkContext and SparkSession. GlueContext class, see GlueContext class. This exposes standard methods for defining source and targetĭatasets, which is the starting point for any ETL script. Import and initialize a GlueContext object. S3bucket_node3 = glueContext.write_dynamic_om_options(Ĭonnection_options= , ("time_of_infraction", "decimal", "time_of_infraction", "decimal"), ![]() ("set_fine_amount", "decimal", "set_fine_amount", "float"), ("infraction_description", "string", "infraction_description", "string"), ("infraction_code", "decimal", "infraction_code", "decimal"), ("officer", "decimal", "officer_name", "decimal"), ![]() ("ticket_number", "decimal", "ticket_number", "float"), ("ticket_date", "string", "ticket_date", "string"), ("date_of_infraction", "string", "date_of_infraction", "string"), ("tag_number_masked", "string", "tag_number_masked", "string"), S3bucket_node1 = glueContext.create_dynamic_om_catalog(ĭatabase="yyz-tickets", table_name="tickets", transformation_ctx="S3bucket_node1" This tutorial has the following prerequisites:įrom awsglue.utils import getResolvedOptionsĪrgs = getResolvedOptions(sys.argv, ) To perform the Scala AWS Glue ETL script writing process. Going through this tutorial, you should be able to generate and inspect a sample Scala script to understand how Similar functionality is available in Scala. You use the Python language and libraries in this tutorial. This prepares you to use additional functionalities that aren't yet available in visual By running this script in a job, you can compare it to visual jobs and see how AWS Glue ETL Work is identical in form and function to the introductory tutorial for the AWS Glue Studio visual editor. In this tutorial, you extract, transform, and load a dataset of parking tickets. Workflows from within an AWS Glue script. YouĬan access native Spark APIs, as well as AWS Glue libraries that facilitate extract, transform, and load (ETL) They give you access to the expanded set of tools available to work with Apache Spark programs. The AWS Glue Studio visual editor offers a graphical, no-code interface for building AWS Glue jobs. ![]() For more information about interactive sessions, see Overview of AWS Glue interactive sessions. For more information about jobs, see Adding jobs in AWS Glue. Jobs, or interactively with interactive sessions. This tutorial introduces you to the process of writing AWS Glue scripts. ![]()
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