Regex patterns for views to filter in ingestion.Migrate SQL script from AWS Redshift to BigQuery CONVERT_TIMEZONE('AEDT',getdate())::DATE Whether to ignore case sensitivity during pattern matching. List of regex patterns to exclude from ingestion. List of regex patterns to include in ingestion Prevents large amount of soft deletes & the state from committing from accidental changes to the source configuration if the relative change percent in entities compared to the previous state is above the 'fail_safe_threshold'. Soft-deletes the entities of type in the last successful run but missing in the current run with stateful_ingestion enabled. If set to True, ignores the current checkpoint state. If set to True, ignores the previous checkpoint state. Otherwise, the default DatahubClientConfig. Default: The datahub_api config if set at pipeline level. The configuration required for initializing the state provider. ❓ (required if stateful_ingestion.state_provider is set) The ingestion state provider configuration. Default is 16MBĭynamicTypedStateProviderConfig (see below for fields) The maximum size of the checkpoint state in bytes. Stateful_ingestion.max_checkpoint_state_size The type of the ingestion state provider registered with datahub. SQLAlchemyStatefulIngestionConfig (see below for fields) S3LineageProviderConfig (see below for fields) Which table lineage collector mode to use. Whether to capture lineage query parser errors with dataset properties for debuggings Whether lineage should be collected from copy commands Whether table lineage should be ingested. The default schema to use if the sql parser fails to parse the schema with sql_based lineage collector
Takes precedence over other connection parameters. The instance of the platform that all assets produced by this recipe belong toĪlias to apply to database when ingesting. The platform that this source connects to
#DATEADD REDSHIFT FULL#
Default: Last full day in UTC (or hour, depending on bucket_duration) Default: Current time in UTCĮarliest date of usage to consider. Size of the time window to aggregate usage stats. The environment that all assets produced by this connector belong toĪ holder for platform -> platform_instance mappings to generate correct dataset urns Sslrootcert : ~ # needed to unpin the AWS Redshift certificate Sslmode : "prefer" # or "require" or "verify-ca"
#DATEADD REDSHIFT DRIVER#
# This requires you to have already installed the Microsoft ODBC Driver for SQL Server. # Extra options when running Redshift behind a proxy Include_tables : True # whether to include views, defaults to True Include_views : True # whether to include views, defaults to True Table, row, and column statistics via optional SQL profiling.Column types associated with each table.Metadata for databases, schemas, views and tables.Not provided by this module, use redshift-usage for that. We understand this is not ideal and we plan to make this easier in the future. To get all metadata from Redshift you need to use two plugins redshift and redshift-usage. You can find more information from the official documentation here. In addition, these tables only maintain logs for 2-5 days.
Redshift system tables have some latency in getting data from queries.
Less reliable as the query parser can fail on certain queries.Views are connected properly if a table depends on it.The sql_based based collector uses Redshift's stl_insert to discover all the insert queriesĪnd uses sql parsing to discover the dependecies. Instead the table will be connected with the view's dependencies. If a table is depending on a view then the view won't be listed as dependency.Does not work with Spectrum/external tables because those scans do not show up in stl_scan table.The stl_scan based collector uses Redshift's stl_insert and stl_scan system tables to There are multiple lineage collector implementations as Redshift does not support table lineage out of the box. For example, STL_QUERY and STL_QUERYTEXT contain the full text of INSERT, UPDATE, and DELETE statements. Giving a user unrestricted access to system tables gives the user visibility to data generated by other users.
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