Reading from Okta entities - AWS Glue

Reading from Okta entities

Prerequisites

  • A Okta Object you would like to read from. Refer the supported entities table below to check the available entities.

Supported entities

Entity Can be Filtered Supports Limit Supports Order By Supports Select * Supports Partitioning
Applications Yes Yes No Yes No
Devices Yes Yes No Yes Yes
Groups Yes Yes Yes Yes Yes
Users Yes Yes Yes Yes Yes
User Types No No No Yes No

Example 

okta_read = glueContext.create_dynamic_frame.from_options(
    connection_type="Okta",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "applications",
        "API_VERSION": "v1"
    }

Okta entity and field details

Entities list:

Partitioning queries

Additional spark options PARTITION_FIELD, LOWER_BOUND, UPPER_BOUND, NUM_PARTITIONS can be provided if you want to utilize concurrency in Spark. With these parameters, the original query would be split into NUM_PARTITIONS number of sub-queries that can be executed by spark tasks concurrently.

  • PARTITION_FIELD: the name of the field to be used to partition query.

  • LOWER_BOUND: an inclusive lower bound value of the chosen partition field.

    For date, we accept the Spark date format used in Spark SQL queries. Example of valid values: "2024-02-06".

  • UPPER_BOUND: an exclusive upper bound value of the chosen partition field.

  • NUM_PARTITIONS: number of partitions.

Example 

okta_read = glueContext.create_dynamic_frame.from_options(
    connection_type="okta",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "lastUpdated",
        "API_VERSION": "v1",
        "PARTITION_FIELD": "lastMembershipUpdated"
        "LOWER_BOUND": "2022-08-10T10:28:46.000Z"
        "UPPER_BOUND": "2024-08-10T10:28:46.000Z"
        "NUM_PARTITIONS": "10"
    }