# Adobe Data Distiller Guide

## Adobe Data Distiller Guide

- [Adobe Data Distiller Guide](https://data-distilller.gitbook.io/adobe-data-distiller-guide/adobe-data-distiller-guide.md)
- [What is Data Distiller?](https://data-distilller.gitbook.io/adobe-data-distiller-guide/what-is-data-distiller.md)
- [PREP 100: Why was Data Distiller Built?](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-100-why-was-data-distiller-built.md)
- [PREP 200: Data Distiller Use Case & Capability Matrix Guide](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-200-data-distiller-use-case-and-capability-matrix-guide.md): Navigate your data journey with precision—empower every decision with the Data Distiller Use Case & Capability Matrix
- [PREP 300: Adobe Experience Platform & Data Distiller Primers](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-300-adobe-experience-platform-and-data-distiller-primers.md)
- [PREP 301: Leveraging Data Loops for Real-Time Personalization](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-301-leveraging-data-loops-for-real-time-personalization.md): Real-time personalization isn't just about having the best tools—it's about creating efficient data loops that allow you to respond instantly to customer needs and provide exceptional service.
- [PREP 302:  Key Topics Overview: Architecture, MDM, Personas](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-302-key-topics-overview-architecture-mdm-personas.md)
- [PREP 303: What is Data Distiller Business Intelligence?](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-303-what-is-data-distiller-business-intelligence.md): Unleash the Power of BI with Speed, Flexibility, and Precision
- [PREP 304: The Human Element in Customer Experience Management](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-304-the-human-element-in-customer-experience-management.md): Where data meets humanity: elevating customer experience with insight and empathy
- [PREP 305: Driving Transformation in Customer Experience: Leadership Lessons Inspired by Lee Iacocca](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-305-driving-transformation-in-customer-experience-leadership-lessons-inspired-by-lee-iacocca.md)
- [PREP 400: DBVisualizer SQL Editor Setup for Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-400-dbvisualizer-sql-editor-setup-for-data-distiller.md)
- [PREP 500: Foundation Data Modeling with Standard Objects in Adobe Experience Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-500-foundation-data-modeling-with-standard-objects-in-adobe-experience-platform.md)
- [PREP 501: Custom Fields Creation in Adobe Experience Platform (AEP) Data Modeling](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-1-getting-started/prep-501-custom-fields-creation-in-adobe-experience-platform-aep-data-modeling.md)
- [PREP 502: Download and Install Azure Storage Explorer](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-502-download-and-install-azure-storage-explorer.md)
- [PREP 503: Batch Data Ingestion Basics: Ingesting Record Data](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-503-batch-data-ingestion-basics-ingesting-record-data.md)
- [Prep 504: Batch Data Ingestion Basics: Ingesting Event Data](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-504-batch-data-ingestion-basics-ingesting-event-data.md)
- [PREP 505: Streaming Data Ingestion Basics: Ingest Record Data](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-505-streaming-data-ingestion-basics-ingest-record-data.md)
- [PREP 500: Ingesting CSV Data into Adobe Experience Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-500-ingesting-csv-data-into-adobe-experience-platform.md)
- [PREP 501: Ingesting JSON Test Data into Adobe Experience Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-501-ingesting-json-test-data-into-adobe-experience-platform.md)
- [PREP 600: Rules vs. AI with Data Distiller: When to Apply, When to Rely, Let ROI Decide](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-600-rules-vs.-ai-with-data-distiller-when-to-apply-when-to-rely-let-roi-decide.md)
- [Prep 601: Breaking Down B2B Data Silos: Transform Marketing, Sales & Customer Success into a Revenue](https://data-distilller.gitbook.io/adobe-data-distiller-guide/prep-601-breaking-down-b2b-data-silos-transform-marketing-sales-and-customer-success-into-a-revenue.md): Don't break down silos, just unify data, and turn every customer interaction into a growth opportunity.
- [EXPLORE 100: Data Lake Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-100-data-lake-overview.md): The data lake in Adobe Experience Platform centralizes and manages diverse data types, enabling organizations to harness their data's full potential for personalized customer experiences.
- [EXPLORE 101: Exploring Ingested Batches in a Dataset with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-101-exploring-ingested-batches-in-a-dataset-with-data-distiller.md): It is important for you to understand how the data ingestion process works and why interrogating the records ingested in a batch may be an important tool in your arsenal to address downstream issues.
- [EXPLORE 200: Exploring Behavioral Data with Data Distiller - A Case Study with Adobe Analytics Data](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-200-exploring-behavioral-data-with-data-distiller-a-case-study-with-adobe-analytics-data.md)
- [EXPLORE 201: Exploring Web Analytics Data with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-201-exploring-web-analytics-data-with-data-distiller.md): Web analytics refers to the measurement, collection, analysis, and reporting of data related to website or web application usage.
- [EXPLORE 202: Exploring Product Analytics with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-202-exploring-product-analytics-with-data-distiller.md): Product analytics is the process of collecting, analyzing, and interpreting data related to a product's usage and performance.
- [EXPLORE 300: Exploring Adobe Journey Optimizer System Datasets with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-300-exploring-adobe-journey-optimizer-system-datasets-with-data-distiller.md): Unleashing Insights from Adobe Journey Optimizer Datasets with Data Distiller
- [EXPLORE 400: Exploring Offer Decisioning Datasets with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-400-exploring-offer-decisioning-datasets-with-data-distiller.md): Unleashing Insights from Offer Decisioning Datasets with Data Distiller
- [EXPLORE 500: Incremental Data Extraction with Data Distiller Cursors](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-2-data-distiller-data-exploration/explore-500-incremental-data-extraction-with-data-distiller-cursors.md): Learn to Navigate Data Efficiently with Incremental Extraction Using Data Distiller Cursors
- [ETL 200: Chaining of Data Distiller Jobs](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-3-data-distiller-etl-extract-transform-load/etl-200-chaining-of-data-distiller-jobs.md): Unleash the power of seamless insights with Data Distiller’s chained queries—connect your data, step by step, to drive better decisions
- [ETL 300: Incremental Processing Using Checkpoint Tables in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-3-data-distiller-etl-extract-transform-load/etl-300-incremental-processing-using-checkpoint-tables-in-data-distiller.md): Turn every data update into actionable intelligence through incremental processing
- [\[DRAFT\]ETL 400: Attribute-Level Change Detection in Profile Snapshot Data](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-3-data-distiller-etl-extract-transform-load/draft-etl-400-attribute-level-change-detection-in-profile-snapshot-data.md)
- [ENRICH 100: Real-Time Customer Profile Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-100-real-time-customer-profile-overview.md): Learn how Data Distiller can power the Real-time Customer Profile that offers a comprehensive, real-time view of individual customers.
- [ENRICH 101: Behavior-Based Personalization with Data Distiller: A Movie Genre Case Study](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-101-behavior-based-personalization-with-data-distiller-a-movie-genre-case-study.md): Here's a basic tutorial that displays the essential components of filtering, shaping, and data manipulation with Data Distiller.
- [ENRICH 200: Decile-Based Audiences with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-200-decile-based-audiences-with-data-distiller.md): Bucketing is a technique used by marketers to split their audience along a dimension and use that to fine-tune the targeting.
- [ENRICH 300: Recency, Frequency, Monetary (RFM) Modeling for Personalization with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-300-recency-frequency-monetary-rfm-modeling-for-personalization-with-data-distiller.md): Learn how to leverage RFM modeling to enhance real-time customer personalization and drive targeted marketing strategies.
- [ENRICH 400: Net Promoter Scores (NPS) for Enhanced Customer Satisfaction with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-400-net-promoter-scores-nps-for-enhanced-customer-satisfaction-with-data-distiller.md): Unlock the power of NPS to measure and improve customer loyalty and satisfaction
- [ENRICH 500: Implementing Personalized Audiences with Data Distiller for B2B Manufacturing](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-4-data-distiller-data-enrichment/enrich-500-implementing-personalized-audiences-with-data-distiller-for-b2b-manufacturing.md)
- [IDR 100: Identity Graph Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-100-identity-graph-overview.md): In Adobe's Real-Time Customer Profile, an identity graph is a core component that maps various identifiers associated with individual customers across multiple devices, touchpoints, and interactions.
- [IDR 200: Extracting Identity Graph from Profile Attribute Snapshot Data with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-200-extracting-identity-graph-from-profile-attribute-snapshot-data-with-data-distiller.md): An identity lookup table is a database table used to store identities associated with various identity namespaces in the Real-Time Customer Profile.
- [IDR 300: Understanding and Mitigating Profile Collapse in Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-300-understanding-and-mitigating-profile-collapse-in-identity-resolution-with-data-distiller.md): Mastering profile cleanup transforms data chaos into clarity, enabling accurate, unified real-time customer profiles with 15+ algorithms.
- [IDR 301: Using Levenshtein Distance for Fuzzy Matching in Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-301-using-levenshtein-distance-for-fuzzy-matching-in-identity-resolution-with-data-distiller.md): Learn how to apply fuzzy matching with Data Distiller to improve accuracy in identity resolution and profile management.
- [IDR 302: Algorithmic Approaches to B2B Contacts - Unifying and Standardizing Across Sales Orgs](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-302-algorithmic-approaches-to-b2b-contacts-unifying-and-standardizing-across-sales-orgs.md): Learn algorithmic techniques for merging, deduplicating, and enriching B2B contact data to create unified, accurate profiles using Data Distiller
- [IDR 302: K-Means Clustering for Identity Resolution with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/idr-302-k-means-clustering-for-identity-resolution-with-data-distiller.md)
- [\[DRAFT\]IDR 300: Probabilistic Identity Resolution Using Fuzzy Matching and Blocking Techniques](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-5-data-distiller-identity-resolution/draft-idr-300-probabilistic-identity-resolution-using-fuzzy-matching-and-blocking-techniques.md)
- [DDA 100: Audiences Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/dda-100-audiences-overview.md): Segmentation matters because it enables businesses to understand and cater to the diverse needs and preferences of their customer base, leading to more effective marketing and product strategies.
- [DDA 200: Build Data Distiller Audiences on Data Lake Using SQL](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/dda-200-build-data-distiller-audiences-on-data-lake-using-sql.md): Unleash the full potential of your data with Data Distiller—where advanced audience creation meets real-time insights, scalability, and unmatched personalization.
- [\[DRAFT\]DDA 202: Data Distiller Audience Orchestration](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/draft-dda-202-data-distiller-audience-orchestration.md)
- [\[DRAFT\]DDA 203: Data Distiller Audience Activation](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/draft-dda-203-data-distiller-audience-activation.md)
- [DDA 300: Audience Overlaps with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/dda-300-audience-overlaps-with-data-distiller.md): Learn how to leverage snapshot of profile attributes, identities and segment memberships to build exotic queries such as 3 or 4 segment overlaps
- [DDA 301: Audience Health and Lifecycle Audit in Adobe Experience Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-6-data-distiller-audiences/dda-301-audience-health-and-lifecycle-audit-in-adobe-experience-platform.md)
- [BI 100: Data Distiller Business Intelligence: A Complete Feature Overview](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-100-data-distiller-business-intelligence-a-complete-feature-overview.md): Unlock insights with Data Distiller dashboards featuring advanced queries, customizable filters, drillthroughs, built-in SQL, and accelerated querying, all integrated seamlessly with BI tools.
- [\[DRAFT\]BI 101: What is a Data Distiller Data Model?](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/draft-bi-101-what-is-a-data-distiller-data-model.md)
- [\[DRAFT\] BI 103: AJO B2B Analysis](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/draft-bi-103-ajo-b2b-analysis.md)
- [BI 200: Create Your First Data Model in the Data Distiller Warehouse for Dashboarding](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-200-create-your-first-data-model-in-the-data-distiller-warehouse-for-dashboarding.md): Creating your first table in the Accelerated Store involves defining and setting up a star schema containing tables to store and manage data.
- [BI 300: Dashboard Authoring with Data Distiller Query Pro Mode](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-300-dashboard-authoring-with-data-distiller-query-pro-mode.md): This tutorial goes through the steps of building a dashboard using SQL Chart Authoring, Drillthroughs and Global Filters.
- [BI 400: Subscription Analytics for Growth-Focused Products using Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-400-subscription-analytics-for-growth-focused-products-using-data-distiller.md): Unlocking Key Subscription Metrics to Drive Growth and Retention with Powerful Visualizations
- [BI 401 Mastering Subscriber Engagement: Retention, Re-Engagement, and Churn Prevention](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-401-mastering-subscriber-engagement-retention-re-engagement-and-churn-prevention.md)
- [BI 500: Optimizing Omnichannel Marketing Spend Using Marginal Return Analysis](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-500-optimizing-omnichannel-marketing-spend-using-marginal-return-analysis.md): Analyzing marketing effectiveness across various channels using
- [BI 600: Trade Promotion Optimization with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-600-trade-promotion-optimization-with-data-distiller.md)
- [BI 700: Perceptual Mapping and Conjoint Analysis with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/bi-700-perceptual-mapping-and-conjoint-analysis-with-data-distiller.md)
- [Offer Resolution in Customer Care with Amazon Connect and Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-7-data-distiller-business-intelligence/offer-resolution-in-customer-care-with-amazon-connect-and-data-distiller.md)
- [STATSML 100: Python & JupyterLab Setup for Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-100-python-and-jupyterlab-setup-for-data-distiller.md): Learn how to setup Python and JupyterLab to connect to Data Distiller.
- [STATSML 101: Learn Basic Python Online](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-101-learn-basic-python-online.md): The goal of this module is to teach you basic Python so that you can understand any code that you come across.
- [STATSML 200: Unlock Dataset Metadata Insights via Adobe Experience Platform APIs and Python](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-200-unlock-dataset-metadata-insights-via-adobe-experience-platform-apis-and-python.md): This chapter covers the essential steps for installing necessary libraries, generating access tokens, and making authenticated API requests.
- [STATSML 201: Securing Data Distiller Access with Robust IP Whitelisting](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-201-securing-data-distiller-access-with-robust-ip-whitelisting.md): Secure Access, Simplified: Protect Data Distiller with IP Whitelisting
- [\[DRAFT\]STATSML 201: Unlocking Dataset Insights with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/draft-statsml-201-unlocking-dataset-insights-with-data-distiller.md)
- [STATSML 300: AI & Machine Learning: Basic Concepts for Data Distiller Users](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-300-ai-and-machine-learning-basic-concepts-for-data-distiller-users.md): Unlock the power of AI and machine learning in this course—equipping you with the basic concepts  to make a real-world impact
- [STATSML 301: A Concept Course on Language Models](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-301-a-concept-course-on-language-models.md): Learn the key ideas behind language models
- [STATSML 302: A Concept Course on Feature Engineering Techniques for Machine Learning](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-302-a-concept-course-on-feature-engineering-techniques-for-machine-learning.md): Transform raw data into predictive power with essential feature engineering techniques.
- [STATSML 400: Data Distiller Basic Statistics Functions](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-400-data-distiller-basic-statistics-functions.md): Unlock the Power of Data: Master Every Key Statistical Function in Data Distiller
- [STATSML 500: Generative SQL with Microsoft GitHub Copilot, Visual Studio Code and Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-500-generative-sql-with-microsoft-github-copilot-visual-studio-code-and-data-distiller.md): Streamline your development workflow with Visual Studio Code and Github Copilot—fast, lightweight, and customizable for all your coding needs, from generating SQL queries to managing projects.
- [STATSML 600: Data Distiller Advanced Statistics & Machine Learning Models](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-600-data-distiller-advanced-statistics-and-machine-learning-models.md): Discover advanced statistics and machine learning functions to build predictive models
- [STATSML 601: Building a Period-to-Period Customer Retention Model Using Logistics Regression](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-601-building-a-period-to-period-customer-retention-model-using-logistics-regression.md): Unlocking Future Engagement: Data-Driven Retention Predictions for Smarter Personalization Strategies
- [STATSML 602: Techniques for Bot Detection in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-602-techniques-for-bot-detection-in-data-distiller.md): Turn clicks into insights: Discover how SQL can reveal bot behavior
- [STATSML 603: Predicting Customer Conversion Scores Using Random Forest in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-603-predicting-customer-conversion-scores-using-random-forest-in-data-distiller.md): Transform Data Into Action: Predict, Personalize, Prosper!
- [STATSML 604: Data Exploration for Customer AI in Real-Time Customer Data Platform](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-data-exploration-for-customer-ai-in-real-time-customer-data-platform.md)
- [STATSML 604: Predicting Customer Journey Insights for Airlines](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-predicting-customer-journey-insights-for-airlines.md)
- [STATSML 604: Car Loan Propensity Prediction using Logistic Regression](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-604-car-loan-propensity-prediction-using-logistic-regression.md)
- [STATSML 700: Sentiment-Aware Product Review Search with Retrieval Augmented Generation (RAG)](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-700-sentiment-aware-product-review-search-with-retrieval-augmented-generation-rag.md): This tutorial demonstrates how to implement a Retrieval-Augmented Generation (RAG) architecture using Python, LangChain and Hugging Face Transformers.
- [STATSML 800: Turbocharging Insights with Data Distiller: A Hypercube Approach to Big Data Analytics](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-8-data-distiller-statistics-and-machine-learning/statsml-800-turbocharging-insights-with-data-distiller-a-hypercube-approach-to-big-data-analytics.md): Turning Big Data into Big Insights with Speed, Precision, and Scalability
- [ACT 100: Dataset Activation with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-activation-and-data-export/act-100-dataset-activation-with-data-distiller.md): Shipping your datasets to distant destinations for maximizing enterprise ROI
- [ACT 200: Dataset Activation: Anonymization, Masking & Differential Privacy Techniques](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-activation-and-data-export/act-200-dataset-activation-anonymization-masking-and-differential-privacy-techniques.md): Explore advanced differential privacy techniques to securely activate data while balancing valuable insights and individual privacy protection."
- [ACT 300: Functions and Techniques for Handling Sensitive Data with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-activation-and-data-export/act-300-functions-and-techniques-for-handling-sensitive-data-with-data-distiller.md): Powering Enterprise Use Cases While Keeping Sensitive Data in Safe Mode
- [ACT 400: AES Data Encryption & Decryption with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-activation-and-data-export/act-400-aes-data-encryption-and-decryption-with-data-distiller.md): Secure your sensitive data with AES encryption - a robust, industry-standard way to protect customer information, while easily decrypting it when needed.
- [\[DRAFT\]FUNC 100: Date and Time Functions](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/draft-func-100-date-and-time-functions.md)
- [FUNC 200: Create XDM Schemas with Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/func-200-create-xdm-schemas-with-data-distiller.md)
- [FUNC 300: Privacy Functions in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/func-300-privacy-functions-in-data-distiller.md): Tutorials from other sections that cover this topic in detail
- [FUNC 400: Statistics Functions in Data Distiller](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/func-400-statistics-functions-in-data-distiller.md)
- [FUNC 500: Lambda Functions in Data Distiller: Exploring Similarity Joins](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/func-500-lambda-functions-in-data-distiller-exploring-similarity-joins.md): The goal of similarity join is to identify and retrieve similar or related records from one or more datasets based on a similarity metric.
- [FUNC 600: Advanced Statistics & Machine Learning Functions](https://data-distilller.gitbook.io/adobe-data-distiller-guide/unit-9-data-distiller-functions-and-extensions/func-600-advanced-statistics-and-machine-learning-functions.md)
- [About the Authors](https://data-distilller.gitbook.io/adobe-data-distiller-guide/about-the-authors.md)