#Json to redshift drivers#

This is built on top of the destination-jdbc code base and is configured to rely on JDBC 4.2 standard drivers provided by Amazon via Mulesoft here as described in Redshift documentation here. INSERT: Replicates data via SQL INSERT queries.This Redshift destination connector has two replication strategies: The Airbyte Redshift destination allows you to sync data to Redshift. If the datastore is streamed in a time series, then we can leverage the use of Glue Job Bookmarks to only read part of the datastore that has not been computed in the previous job runs.This page guides you through the process of setting up the Redshift destination connector. The resultant DynamicFrames are then written to Redshift as separate tables using the `fromjdbc_conf` method. This transformation helps in renaming the fields before they are written to the destination. The three DynamicFrames are extracted from the DynamicFrameCollection 'relationalized json' and then each are tranformed using the ApplyMapping transformation.

The data source is then transformed using the relationalize transformation. This ETL script reads the data store via the Glue Catalog. Root_properties_df = relationalized_lect('root_properties')Īpplymapping1 = ApplyMapping.apply(frame = root_df, mappings =, transformation_ctx = 'applymapping1')Īpplymapping2 = ApplyMapping.apply(frame = root_notes_df, mappings =, transformation_ctx = 'applymapping2')Īpplymapping3 = ApplyMapping.apply(frame = root_properties_df, mappings =, transformation_ctx = 'applymapping3')ĭatasink1 = glueContext.write_dynamic_om_jdbc_conf(frame = applymapping1, catalog_connection = 'connection-rel', connection_options =, redshift_tmp_dir = args, transformation_ctx = 'datasink3') Root_notes_df = relationalized_lect('root_notes') Relationalized_json = datasource0.relationalize(root_table_name = 'root', staging_path = args)

Create a Glue ETL job that runs 'A new script to be authored by you' and specify the connection created in step 3.įrom awsglue.utils import getResolvedOptionsĪrgs = getResolvedOptions(sys.argv, )ĭatasource0 = glueContext.create_dynamic_om_catalog(database = 'default', table_name = 'rel', transformation_ctx = 'datasource0').

Add a Glue connection with connection type as Amazon Redshift, preferably in the same region as the datastore, and then set up access to your data source.

Run a crawler to create an external table in Glue Data Catalog.

Add a Crawler with 'S3' data store and specify the S3 prefix in the include path.In the example, we take a sample JSON source file, relationalize it and then store it in a Redshift cluster for further analytics.

#Json to redshift series#

When applied on a DynamicFrame, it flattens nested schema and pivots out array columns from the flattened frame.ĭue to its ability to primitively convert a NoSQL style nested schema into a combination of multiple Relationalized schemas, it is a great tool for tranforming a nested JSON into a series of tables linked to each other which can then be operated like Relational Databases, with operations including by not limited to JOINS, Aggregations etc. Amongst these transformation is the Relationalize transformation. AWS Glue is a fully managed ETL service provided by Amazon that makes it easy to extract and migrate data from one source to another whilst performing a transformation on the source data.