AWS Glue to Redshift: Is it possible to replace, update or delete data?

Job bookmarks are the key. Just edit the job and enable "Job bookmarks" and it won't process already processed data. Note that the job has to rerun once before it will detect it does not have to reprocess the old data again.

For more info see: http://docs.aws.amazon.com/glue/latest/dg/monitor-continuations.html

The name "bookmark" is a bit far fetched in my opinion. I would have never looked at it if I did not coincidentally stumble upon it during my search.

Please check this answer. There is explanation and code sample how to upsert data into Redshift using staging table. The same approach can be used to run any SQL queries before or after Glue writes data using preactions and postactions options:

// Write data to staging table in Redshift
glueContext.getJDBCSink(
  catalogConnection = "redshift-glue-connections-test",
  options = JsonOptions(Map(
    "database" -> "conndb",
    "dbtable" -> staging,
    "overwrite" -> "true",
    "preactions" -> "<another SQL queries>",
    "postactions" -> "<some SQL queries>"
  )),
  redshiftTmpDir = tempDir,
  transformationContext = "redshift-output"
).writeDynamicFrame(datasetDf)

This was the solution I got from AWS Glue Support:

As you may know, although you can create primary keys, Redshift doesn't enforce uniqueness. Therefore, if you are rerunning Glue jobs then duplicate rows can get inserted. Some of the ways to maintain uniqueness are:

1. Use a staging table to insert all rows and then perform a upsert/merge [1] into the main table, this has to be done outside of glue.

2. Add another column in your redshift table [1], like an insert timestamp, to allow duplicate but to know which one came first or last and then delete the duplicate afterwards if you need to.

3. Load the previously inserted data into dataframe and then compare the data to be insert to avoid inserting duplicates[3]

[1] - http://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-upsert.html and http://www.silota.com/blog/amazon-redshift-upsert-support-staging-table-replace-rows/

[2] - https://github.com/databricks/spark-redshift/issues/238

[3] - https://kb.databricks.com/data/join-two-dataframes-duplicated-columns.html