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Comprehensive Technical Tutorial for AEP
  • Comprehensive Technical Tutorial for Adobe Experience Platform
    • Architecture
    • Video Overview
  • 0 - Getting started
    • 0.0 Which environment do I use?
    • (Deprecated) Install the Chrome extension for the Experience League documentation
    • 0.1 Use Demo System Next to setup your Adobe Experience Platform Data Collection client property
    • 0.2 Create your Datastream
    • 0.3 Set up the website
    • 0.4 Set up the mobile app
    • 0.5 Ingest Data to AEP through the Website
    • 0.6 Ingest Data to AEP through the Mobile App
    • 0.7 Visualize your own Real-time Customer Profile - UI
    • 0.8 See your Real-time Customer Profile in action in the Call Center
    • 0.9 Set up and use the AEP API to visualize your Real-Time Customer Profile
    • 0.10 Install the Experience Platform Debugger Extension
    • 0.11 What if I want to demonstrate basic AEP concepts directly on a live website?
  • 1 - Adobe Experience Platform Data Collection and the Web SDK extension
    • 1.1 Understanding Adobe Experience Platform Data Collection
    • 1.2 Edge Network, Datastreams and Server Side Data Collection
    • 1.3 Introduction to Adobe Experience Platform Data Collection
    • 1.4 Client-side Web Data Collection
    • 1.5 Implement Adobe Analytics and Adobe Audience Manager
    • 1.6 Implement Adobe Target
    • 1.7 XDM Schema requirements in Adobe Experience Platform
    • Summary and Benefits
  • 2 - Data Ingestion
    • 2.1 Explore the Website
    • 2.2 Configure Schemas and Set Identifiers
    • 2.3 Configure Datasets
    • 2.4 Data Ingestion from Offline Sources
    • 2.5 Data Landing Zone
    • Summary and Benefits
  • 3 - Real-time Customer Profile
    • 3.1 Visit the website
    • 3.2 Visualize your own real-time customer profile - UI
    • 3.3 Visualize your own real-time customer profile - API
    • 3.4 Create a segment - UI
    • 3.5 Create a segment - API
    • 3.6 See your Real-time Customer Profile in action in the Call Center
    • Summary and benefits
  • 4 - Query Service
    • 4.0 Prerequisites
    • 4.1 Getting Started
    • 4.2 Using the Query Service
    • 4.3 Queries, queries, queries... and churn analysis
    • 4.4 Generate a dataset from a query
    • 4.5 Query Service and Power BI
    • 4.6 Query Service and Tableau
    • 4.7 Query Service API
    • Summary and benefits
  • 5 - Intelligent Services
    • 5.1 Customer AI - Data Preparation (Ingest)
    • 5.2 Customer AI - Create a New Instance (Configure)
    • 5.3 Customer AI - Scoring Dashboard and Segmentation (Predict & Take Action)
  • 6 - Real-time CDP - Build a segment and take action
    • 6.1 Create a segment
    • 6.2 Review how to configure DV360 Destination using Destinations
    • 6.3 Take Action: send your segment to DV360
    • 6.4 Take Action: send your segment to an S3-destination
    • 6.5 Take Action: send your segment to Adobe Target
    • 6.6 External Audiences
    • 6.7 Destinations SDK
    • Summary and benefits
  • 7 - Adobe Journey Optimizer: Orchestration
    • 7.1 Create your event
    • 7.2 Create your journey and email message
    • 7.3 Update your Data Collection property and test your journey
    • Summary and benefits
  • 8 - Adobe Journey Optimizer: External data sources and custom actions
    • 8.1 Define an event
    • 8.2 Define an external data source
    • 8.3 Define a custom action
    • 8.4 Create your journey and messages
    • 8.5 Trigger your journey
    • Summary and benefits
  • 9 - Adobe Journey Optimizer: Offer Decisioning
    • 9.1 Offer Decisioning 101
    • 9.2 Configure your offers and decision
    • 9.3 Prepare your Data Collection Client property and Web SDK setup for Offer Decisioning
    • 9.4 Combine Adobe Target and Offer Decisioning
    • 9.5 Use your decision in an email
    • 9.6 Test your decision using the API
    • Summary and benefits
  • 10 - Adobe Journey Optimizer: Event-based Journeys
    • 10.1 Configure an event-based journey - Order Confirmation
    • 10.2 Configure a batch-based newsletter journey
    • 10.3 Apply personalization in an email message
    • 10.4 Setup and use push notifications
    • 10.5 Create a business event journey
    • Summary and benefits
  • 11 - Customer Journey Analytics - Build a dashboard using Analysis Workspace on top of Adobe Experie
    • 11.1 Customer Journey Analytics 101
    • 11.2 Connect Adobe Experience Platform Data Sets in Customer Journey Analytics
    • 11.3 Create a Data View
    • 11.4 Data Preparation in Customer Journey Analytics
    • 11.5 Visualization using Customer Journey Analytics
    • Summary and benefits
  • 12 - Ingest & Analyze Google Analytics data in Adobe Experience Platform with the BigQuery Source Co
    • 12.1 Create your Google Cloud Platform Account
    • 12.2 Create your first query in BigQuery
    • 12.3 Connect GCP & BigQuery to Adobe Experience Platform
    • 12.4 Load data from BigQuery into Adobe Experience Platform
    • 12.5 Analyze Google Analytics Data using Customer Journey Analytics
    • Summary and benefits
  • 13 - Real-Time CDP: Segment Activation to Microsoft Azure Event Hub
    • 13.1 Configure your Microsoft Azure EventHub environment
    • 13.2 Configure your Azure Event Hub Destination in Adobe Experience Platform
    • 13.3 Create a segment
    • 13.4 Activate segment
    • 13.5 Create your Microsoft Azure Project
    • 13.6 End-to-end scenario
    • Summary and benefits
  • 14 - Real-Time CDP Connections: Event Forwarding
    • 14.1 Create a Data Collection Event Forwarding property
    • 14.2 Update your Datastream to make data available to your Data Collection Event Forwarding property
    • 14.3 Create and configure a custom webhook
    • 14.4 Create and configure a Google Cloud Function
    • 14.5 Forward events towards the AWS ecosystem
    • Summary and benefits
  • 15 - Stream data from Apache Kafka into Adobe Experience Platform
    • 15.1 Introduction to Apache Kafka
    • 15.2 Install and configure your Kafka cluster
    • 15.3 Configure HTTP API Streaming endpoint in Adobe Experience Platform
    • 15.4 Install and configure Kafka Connect and the Adobe Experience Platform Sink Connector
    • Summary and benefits
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On this page
  • Objectives
  • Before you start
  • 12.4.1 BigQuery Table Selection
  • 12.4.2 XDM mapping
  • 12.4.3 Connection and the data ingestion scheduling
  • 12.4.4 Review and launch connection
  1. 12 - Ingest & Analyze Google Analytics data in Adobe Experience Platform with the BigQuery Source Co

12.4 Load data from BigQuery into Adobe Experience Platform

Ingest & Analyze Google Analytics data in Adobe Experience Platform with the BigQuery Source Connector - Load data from BigQuery into Adobe Experience Platform

Previous12.3 Connect GCP & BigQuery to Adobe Experience PlatformNext12.5 Analyze Google Analytics Data using Customer Journey Analytics

Last updated 2 years ago

Objectives

  • Map BigQuery data to an XDM schema

  • Load BigQuery data into Adobe Experience Platform

  • Become familiar with the BigQuery Source Connector UI

Before you start

After exercise 12.3, you should have this page open in Adobe Experience Platform:

If you have it open, continue with exercise 12.4.1.

If you don't have it open, go to Adobe Experience Platform.

In the left menu, go to Sources. You'll then see the Sources homepage. In the Sources menu, click on Databases.

Select the Google BigQuery Source Connector and click on + Configure.

You'll then see the Google BigQuery Account selection screen.

Select your account and click Next.

You'll then see the Add data view.

12.4.1 BigQuery Table Selection

In the Add data view, select your BigQuery dataset.

You can now see a sample data preview of the Google Analytics data in BigQuery.

Click Next.

12.4.2 XDM mapping

You'll now see this:

You now have to either create a new dataset or select an existing dataset to load the Google Analytics data into. For this exercise, a dataset and schema have already been created. You do not need to create a new schema or dataset.

Select Existing dataset. Open the dropdown menu to select a dataset. Search for the dataset named Demo System - Event Dataset for BigQuery (Global v1.1) and select it. Click Next.

Scroll down. You now need to map every Source Field from Google Analytics/BigQuery to an XDM Target Field, field by field.

Use the below mapping table for this exercise.

Source Field
Target Field

_id

_id

_id

channel._id

timeStamp

timestamp

GA_ID

--aepTenantId--.identification.core.gaid

customerID

--aepTenantId--.identification.core.loyaltyId

Page

web.webPageDetails.name

Device

device.type

Browser

environment.browserDetails.vendor

MarketingChannel

marketing.trackingCode

TrafficSource

channel.typeAtSource

TrafficMedium

channel.mediaType

TransactionID

commerce.order.payments.transactionID

Ecommerce_Action_Type

eventType

Pageviews

web.webPageDetails.pageViews.value

Unique_Purchases

commerce.purchases.value

Product_Detail_Views

commerce.productViews.value

Adds_To_Cart

commerce.productListAdds.value

Product_Removes_From_Cart

commerce.productListRemovals.value

Product_Checkouts

commerce.checkouts.value

After copying and pasting the above mapping into the Adobe Experience Platform UI, please verify if you don't see any errors due to typos or leading/trailing spaces.

You now have a Mapping like this one:

The source fields GA_ID and customerID are mapped to an Identifier in this XDM Schema. This will allow you to enrich Google Analytics data (web/app behavior data) with other datasets such as Loyalty or Call Center-data.

Click Next.

12.4.3 Connection and the data ingestion scheduling

You'll now see the Scheduling tab:

In the Scheduling tab, you are able to define a frequency for the data ingestion process for this Mapping and data.

As you're using demo data in Google BigQuery that won't be refreshed, there's no real need for setting a schedule in this exercise. You do have to select something, and to avoid too many useless data ingestion processes, you need to set the frequency like this:

  • Frequency: Week

  • Interval: 200

Important: be sure you activate the Backfill switch.

Last but not least, you must define a delta field.

The delta field is used to schedule the connection and upload only new rows that come into your BigQuery dataset. A delta field is typically always a timestamp column. So for future scheduled data ingestions, only the rows with a new, more recent timestamp will be ingested.

Select timeStamp as the delta field.

You now have this.

Click Next.

12.4.4 Review and launch connection

In the Dataset flow detail view. you need to name your connection, which will help you to find it later.

Please use this naming convention:

Field
Naming
Example

Dataset flow name

DataFlow - ldap - BigQuery Website Interaction

DataFlow - vangeluw - BigQuery Website Interaction

Description

DataFlow - ldap - BigQuery Website Interaction

DataFlow - vangeluw - BigQuery Website Interaction

Click Next.

You now see a detailed overview of your connection. Make sure everything is correct before you continue, as some settings can't be changed anymore afterwards, like for instance the XDM mapping.

Click Finish.

Setting up the connection may take some time, so don't worry if you see this:

Once the connection has been created, you'll see this:

You're now ready to continue with the next exercise, in which you'll use Customer Journey Analytics to build powerful visualizations on top of Google Analytics data.

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