Client

gluedatabrew

R Documentation

AWS Glue DataBrew¶

Description¶

Glue DataBrew is a visual, cloud-scale data-preparation service. DataBrew simplifies data preparation tasks, targeting data issues that are hard to spot and time-consuming to fix. DataBrew empowers users of all technical levels to visualize the data and perform one-click data transformations, with no coding required.

Usage¶

gluedatabrew(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments¶

config

Optional configuration of credentials, endpoint, and/or region.

credentials:
- creds:
  - access_key_id: AWS access key ID
  - secret_access_key: AWS secret access key
  - session_token: AWS temporary session token
- profile: The name of a profile to use. If not given, then the default profile is used.
- anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

creds:
- access_key_id: AWS access key ID
- secret_access_key: AWS secret access key
- session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value¶

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax¶

svc <- gluedatabrew(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations¶

batch_delete_recipe_version: Deletes one or more versions of a recipe at a time
create_dataset: Creates a new DataBrew dataset
create_profile_job: Creates a new job to analyze a dataset and create its data profile
create_project: Creates a new DataBrew project
create_recipe: Creates a new DataBrew recipe
create_recipe_job: Creates a new job to transform input data, using steps defined in an existing Glue DataBrew recipe
create_ruleset: Creates a new ruleset that can be used in a profile job to validate the data quality of a dataset
create_schedule: Creates a new schedule for one or more DataBrew jobs
delete_dataset: Deletes a dataset from DataBrew
delete_job: Deletes the specified DataBrew job
delete_project: Deletes an existing DataBrew project
delete_recipe_version: Deletes a single version of a DataBrew recipe
delete_ruleset: Deletes a ruleset
delete_schedule: Deletes the specified DataBrew schedule
describe_dataset: Returns the definition of a specific DataBrew dataset
describe_job: Returns the definition of a specific DataBrew job
describe_job_run: Represents one run of a DataBrew job
describe_project: Returns the definition of a specific DataBrew project
describe_recipe: Returns the definition of a specific DataBrew recipe corresponding to a particular version
describe_ruleset: Retrieves detailed information about the ruleset
describe_schedule: Returns the definition of a specific DataBrew schedule
list_datasets: Lists all of the DataBrew datasets
list_job_runs: Lists all of the previous runs of a particular DataBrew job
list_jobs: Lists all of the DataBrew jobs that are defined
list_projects: Lists all of the DataBrew projects that are defined
list_recipes: Lists all of the DataBrew recipes that are defined
list_recipe_versions: Lists the versions of a particular DataBrew recipe, except for LATEST_WORKING
list_rulesets: List all rulesets available in the current account or rulesets associated with a specific resource (dataset)
list_schedules: Lists the DataBrew schedules that are defined
list_tags_for_resource: Lists all the tags for a DataBrew resource
publish_recipe: Publishes a new version of a DataBrew recipe
send_project_session_action: Performs a recipe step within an interactive DataBrew session that's currently open
start_job_run: Runs a DataBrew job
start_project_session: Creates an interactive session, enabling you to manipulate data in a DataBrew project
stop_job_run: Stops a particular run of a job
tag_resource: Adds metadata tags to a DataBrew resource, such as a dataset, project, recipe, job, or schedule
untag_resource: Removes metadata tags from a DataBrew resource
update_dataset: Modifies the definition of an existing DataBrew dataset
update_profile_job: Modifies the definition of an existing profile job
update_project: Modifies the definition of an existing DataBrew project
update_recipe: Modifies the definition of the LATEST_WORKING version of a DataBrew recipe
update_recipe_job: Modifies the definition of an existing DataBrew recipe job
update_ruleset: Updates specified ruleset
update_schedule: Modifies the definition of an existing DataBrew schedule

Examples¶

## Not run: 
svc <- gluedatabrew()
svc$batch_delete_recipe_version(
  Foo = 123
)

## End(Not run)