Reading from Google Analytics 4 entities - Amazon Glue
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Reading from Google Analytics 4 entities

Prerequisites

  • A Google Analytics 4 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
Real-Time Report Yes Yes Yes Yes No
Core Report Yes Yes Yes Yes Yes

Example 

googleAnalytics4_read = glueContext.create_dynamic_frame.from_options(
    connection_type="GoogleAnalytics4",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "entityName",
        "API_VERSION": "v1beta"
    }

Google Analytics 4 entity and field details

Entity Field Data Type Supported Operators
Core Report Dynamic Fields
Core Report Dimension Fields String LIKE, =
Core Report Dimension Fields Date LIKE, =
Core Report Metric Fields String >, <, >=, <=, = BETWEEN
Core Report Custom Dimension and Custom Metric Fields String NA
Real-Time Report appVersion String LIKE, =
Real-Time Report audienceId String LIKE, =
Real-Time Report audienceName String LIKE, =
Real-Time Report city String LIKE, =
Real-Time Report cityId String LIKE, =
Real-Time Report country String LIKE, =
Real-Time Report countryId String LIKE, =
Real-Time Report deviceCategory String LIKE, =
Real-Time Report eventName String LIKE, =
Real-Time Report minutesAgo String LIKE, =
Real-Time Report platform String LIKE, =
Real-Time Report streamId String LIKE, =
Real-Time Report streamName String LIKE, =
Real-Time Report unifiedScreenName String LIKE, =
Real-Time Report activeUsers String >, <, >=, <=, = BETWEEN
Real-Time Report conversions String >, <, >=, <=, = BETWEEN
Real-Time Report eventCount String >, <, >=, <=, = BETWEEN
Real-Time Report screenPageViews String >, <, >=, <=, = BETWEEN

Partitioning queries

  1. Filter-based partition

    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 

    googleAnalytics4_read = glueContext.create_dynamic_frame.from_options(
    connection_type="GoogleAnalytics4",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "entityName",
        "API_VERSION": "v1beta",
        "PARTITION_FIELD": "date"
        "LOWER_BOUND": "2022-01-01"
        "UPPER_BOUND": "2024-01-02"
        "NUM_PARTITIONS": "10"
    }

  2. Record-based partition

    Additional spark options 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.

    • NUM_PARTITIONS: number of partitions.

    Example 

    googleAnalytics4_read = glueContext.create_dynamic_frame.from_options(
    connection_type="GoogleAnalytics4",
    connection_options={
        "connectionName": "connectionName",
        "ENTITY_NAME": "entityName",
        "API_VERSION": "v1beta",
        "NUM_PARTITIONS": "10"
    }