Oracle AI Data Platform Workbench Samples
This repository contains a curated collection of sample notebooks demonstrating how to build data pipelines, run machine learning workloads, and integrate AI capabilities using Oracle AI Data Platform (AIDP) Workbench — a unified, governed workspace for data engineering, ML, and AI development powered by Apache Spark.
What is Oracle AI Data Platform Workbench?
Oracle AI Data Platform Workbench is a unified, governed workspace for building, managing, and deploying AI and data-driven solutions. It brings together notebooks, agent development, orchestration, and catalog management in a single collaborative platform — empowering teams to explore data, fine-tune models, and operationalize AI with trust and speed.
Learn more about AIDP Workbench →
Repository Structure
oracle-aidp-samples/
├── getting-started/ # Foundational notebooks for new users
│ ├── Delta_Lake/ # Delta Lake feature walkthroughs
│ └── migration/ # Migrating workloads to AIDP
├── data-engineering/
│ ├── ingestion/ # Connectors and data loading patterns
│ └── transformation/ # Pipeline architectures and table formats
│ ├── liquid-clustering/
│ ├── medallion-lake/
│ ├── scd/
│ └── streaming/
├── ai/
│ ├── agent-flows/ # Agent orchestration and scheduling
│ └── ml-datascience/ # ML, LLM, and AI service integrations
└── shared-utils/ # Reusable utilities and data generators
Sample Catalog
Getting Started
Foundational examples to help you get up and running on AIDP Workbench.
| Notebook | Description |
|---|
| Access ALH Data | Write and query data in Oracle Autonomous AI Lakehouse (ALH) using PySpark insertInto and SQL INSERT statements with external catalogs. |
| Access Object Storage Data | Read and write data from OCI Object Storage using direct access, external volumes, and external tables. |
| Analyse Data Using PySpark | PySpark fundamentals: catalog and schema setup, table creation, data insertion, schema exploration, and matplotlib visualizations. |
| Analyse Data Using SQL | Core SQL operations on AIDP including DataFrame creation, transformations, aggregations, and simple visualizations. |
| ALH External Catalog MERGE | End-to-end MERGE workflow into an ALH table via an AIDP external catalog: insert/update/delete with merge keys and OOS-staging skip optimization. |
Delta Lake
| Notebook | Description |
|---|
| Use Delta Lake Table | Comprehensive guide covering Delta table operations: updates, merges, time travel, liquid clustering, and vacuuming. |
| Delta Change Data Feed | Capture row-level changes (inserts, updates, deletes) from Delta tables for CDC, incremental processing, and streaming pipelines. |
| Handle Schema Evolution | Add and evolve columns in Delta tables without rewriting existing data, leveraging automatic schema evolution. |
| Delta UniForm Tables | Create Delta UniForm tables that automatically synchronize Iceberg metadata for cross-format interoperability. |
Migration
| Notebook | Description |
|---|
| Migrate Files from Databricks to AIDP | Recursively export notebooks and files from a Databricks workspace to AIDP using the databricks-sdk library. |
| Download from Git to AIDP | Download notebooks and files from a Git repository as a ZIP archive and extract them directly into an AIDP workspace volume. |
Data Engineering — Ingestion
Patterns for connecting to and loading data from a wide range of sources.
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