Adobe Data Distiller Guide
  • Adobe Data Distiller Guide
  • Foreword
  • Unit 1: Prerequisites
    • PRE 100: Why was Data Distiller Built?
    • PRE 101: Data Distiller Use Case & Capability Matrix Guide
    • PRE 102: Adobe Experience Platform & Data Distiller Primers
    • PRE 103: Leveraging Data Loops for Real-Time Personalization
    • PRE 104: Key Topics Overview: Architecture, MDM, Personas
    • PRE 105: DBVisualizer SQL Editor Setup
  • PRE 106: Ingesting CSV Data into Adobe Experience Platform
  • PRE 107: Ingesting JSON Test Data into Adobe Experience Platform
  • PRE 200: Generative SQL with Microsoft GitHub Copilot, Visual Studio Code and Data Distiller
  • Unit 2: DATA DISTILLER EXPLORATION
    • EXPLORE 100: Data Lake Overview
    • EXPLORE 101: Exploring Ingested Batches in a Dataset
    • EXPLORE 200: Exploring Behavioral Data - Case Study with Adobe Analytics Data
    • EXPLORE 201: Web Analytics - Case Study with Adobe Analytics Data
    • EXPLORE 202: Product Analytics - Analyzing Adobe Analytics Data
    • EXPLORE 300: Exploring Adobe Journey Optimizer System Datasets with Data Distiller
    • EXPLORE 400: Exploring Offer Decisioning Datasets with Data Distiller
  • Unit 3: DATA DISTILLER DERIVED ATTRIBUTES
    • ENRICH 100: Real-Time Customer Profile Overview
    • ENRICH 101: Behavior-Based Personalization: A Movie Genre Case Study
    • ENRICH 200: Decile-Based Audiences
    • ENRICH 300: Recency, Frequency, Monetary (RFM) Modeling for Real-Time Personalization
  • Unit 4: DATA DISTILLER IDENTITY STITCHING
    • ID 100: Identity Graph Overview
    • ID 200: Channel Identity Lookup Table from Profile Attribute Snapshot Data
  • Unit 5: DATA DISTILLER AUDIENCES
    • DDA 100: Audiences Overview
    • DDA 300: Audience Overlaps using Profile Attribute Snapshot Datasets
  • Unit 6: Data Distiller Insights
    • DASH 100: Data Distiller Dashboards: A Complete Feature Overview
    • DASH 200: Create Your First Data Model in the Accelerated Store For Dashboarding
    • DASH 300: Dashboard Authoring with Data Distiller Query Pro Mode
  • Unit 7: DATA DISTILLER STATISTICS
    • STATS 100: Python & JupyterLab Setup
    • STATS 101: Learn Basic Python Online
    • STATS 200: Unlock Dataset Metadata Insights via Adobe Experience Platform APIs and Python
    • STATS 300: AI & Machine Learning Concepts for Data Distiller Users
  • UNIT 8: DATA DISTILLER ORCHESTRATION
    • ORCH 200: Chaining of Data Distiller Jobs
    • ORCH 300: Incremental Processing Using Checkpoint Tables
  • UNIT 9: DATA DISTILLER FUNCTIONS & EXTENSIONS
    • FUNC 300: Privacy Functions
    • FUNC 400: Lambda Functions: Exploring Similarity Joins
  • UNIT 10: DATA DISTILLER ACTIVATION
    • ACT 100: Dataset Activation with Data Distiller
    • ACT 200: Dataset Activation: Anonymization, Masking & Differential Privacy Techniques
    • ACT 300: Functions and Techniques for Handling Sensitive Data
    • ACT 400: AES Data Encryption & Decryption with Data Distiller
  • About the Author
Powered by GitBook