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Module 2: Vector data and embeddings fundamentals
10 Lessons-
StartModule introduction
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StartWhat is an AI model?
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StartWhat embeddings are and why they exist
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StartHow vector similarity differs from relational comparison
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StartTypical AI scenarios that involve vectors
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StartWhy vectors work well for these scenarios
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StartWhen vectors are inappropriate
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StartWhere embeddings models are hosted
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StartCommon embeddings models
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StartQuiz 2
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Module 3: Vector data types in PostgreSQL
10 Lessons-
StartModule introduction
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StartVector data type basics
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Startpgvector extension basics
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StartDeclaring vector columns and variables
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StartHow vector values are represented
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StartPractical table pattern for embeddings
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StartDimensionality is enforced
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StartDimensionality changes are migration events
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StartStorage characteristics and limitations of pgvector
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StartQuiz 3
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Module 4: Querying vector data
8 Lessons-
StartModule introduction
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StartWhat similarity search means in PostgreSQL
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StartWriting similarity queries in SQL
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StartVector distance comparison operators
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StartExact similarity (KNN) vs approximate search (ANN)
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StartCombining vector similarity with relational predicates
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StartCommon query mistakes and inefficiencies
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StartQuiz 4
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Module 5: Vector indexing and performance
9 Lessons-
StartModule introduction
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StartWhy vector indexing exists
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StartTraditional PostgreSQL vector indexes
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StartIntroduction to DiskANN in HorizonDB
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StartCreating a DiskANN index
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StartAccuracy versus performance trade-offs
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StartCPU and memory impact of vector queries
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StartMonitoring and diagnosing vector query performance
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StartQuiz 5
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Module 6: Integrating External AI Services
7 Lessons -
Module 7: Calling AI services from HorizonDB
7 Lessons -
Module 8: Durable AI pipelines
8 Lessons-
StartModule introduction
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StartChallenges with external ETL for AI workflows
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Startpg_durable execution engine
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StartDefining a pipeline with sources, steps, and sinks
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StartHandling automatic chunking and embedding in SQL
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StartManaging pipeline state and recovery
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StartMonitoring and troubleshooting persistent workflows
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StartQuiz 8
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Module 11: When NOT to use AI features in HorizonDB
7 Lessons
Using AI Features in Azure HorizonDB (In development - coming soon)
Store, retrieve, and integrate data for AI systems using Azure HorizonDB
Course Summary
Do you want to start working with AI-related features in Azure HorizonDB?
As these features are relatively new, the focus of this course is on design and architecture decisions that remain valid, even as individual features, syntax, and external AI services evolve. But we will show you how to achieve great outcomes right now. It does not aim to turn Azure HorizonDB into an AI platform, nor does it attempt to teach model training, data science, or vendor-specific AI tooling.
This course is about using Azure HorizonDB to store, retrieve, and integrate data for AI systems, in a way that is reliable, understandable, and operationally sound. We assume you already understand PostgreSQL or HorizonDB fundamentals and are comfortable working with PostgreSQL and database design.
— Are you responsible for deciding where AI-related logic should live, and where it should not?
— Do you need to support vector search, retrieval, or AI integration without turning the database into an experimental system?
— Have you been asked whether Azure HorizonDB should be used for embeddings, RAG patterns, or AI orchestration, and wanted a clear answer that you can explain?
If that matches the kind of responsibility you have, you’re in the right place. You'll get clear explanations, hands-on exercises, and short quizzes to make sure you not only understand the concepts but can confidently apply them in real Azure HorizonDB environments. And while the practical exercises are optional, we strongly encourage completing them. They are designed to be straightforward and focused on realistic scenarios, rather than tooling complexity. Setup is easy. The main requirement is access to an Azure subscription where you can create an Azure HorizonDB resource.
