Generate Notebook

Overview

The Generate Notebook feature is at the heart of how the Osmos AI Data Engineer turns your configuration into powerful, reusable Python code. With one click, it produces a fully functional Spark-based notebook that is ready to run, schedule, version, and integrate into pipelines.

These notebooks do more than ingest and transform data—they represent long-living, production-grade workflows that evolve with your needs, while putting human reviewers in complete control.

What It Is

Generate Notebook triggers the AI Data Engineer to build a ready-to-run Python notebook based on your configuration instructions, source files, and destination schemas. The notebook is:

• Execution-ready: Includes logic for ingestion, transformation, and validation

• Reusable: Can be versioned, re-executed, and adapted for new data

• Pipeline-ready: Built for integration into orchestration systems (e.g., Fabric, Airflow)

• Autonomous but supervised: All actions are user-initiated, ensuring complete control

Think of it as saying: “Hey engineer, write me a Python notebook for this job.” And the AI does it—intelligently, iteratively, and at scale.

What the AI Does Behind the Scenes

When you click Generate Notebook, the AI Data Engineer will:

1. Sample & Analyze Files It inspects your input data (CSV, JSON, XML, Parquet, etc.) to understand schemas, anomalies, and transformations.

2. Write the Code It generates Spark-based Python code that:

   • Ingests your data

   • Transforms it according to your instructions

   • Includes built-in schema checks and validation logic

3. Write Its Tests The notebook includes test cases to catch data issues, logic gaps, or structural inconsistencies.

4. Handle Errors Automatically If tests fail, the AI:

   • Resamples the data

   • Revises the code

   • Re-generates logic until a working solution is found

5. Add Bookkeeping Built-in logic tracks what data has been processed, avoiding duplicates or reprocessing in future runs.

Iteration & Feedback

• After reviewing the generated instructions, you can:

  • Edit them inline

  • Add edge-case handling

  • Strengthen constraints (e.g., "fail if source columns change")

• If the result isn't correct, update your instructions and regenerate

• Use real-time feedback to refine and guide the AI’s behavior

Key Capabilities

Capability	Description
Reusable & Versionable	Notebooks are long-living and can be stored, shared, and reused
Fully Tested	AI includes test scripts and validation checks
Pipeline Integration	Designed to plug into workflows and orchestration platforms
Bookkeeping Logic	Automatically tracks processed files for repeatable, safe operations
Performance Optimized	Passes through an AI profiler for better runtime and scaling
Human-in-the-Loop	All notebook generation and execution are initiated and reviewed by users