Databricks notebooks are interactive environments where you can write and run code (Python, SQL, Scala, R), visualize data, and collaborate with others. They are fundamental to Databricks and used for diverse tasks like data analysis, machine learning, and data engineering.

Examples of Databricks notebooks can include:

• Data exploration: Notebooks can load, clean, transform, and visualize data from various sources (like CSV files, databases, or cloud storage) to gain insights and understanding.
• Machine learning: Notebooks can train and deploy machine learning models, including tasks like feature engineering, model selection, and evaluation.
• Data pipelines: Notebooks can build and orchestrate complex data pipelines, including data ingestion, processing, and storage.
• Stream processing: Notebooks can process real-time data streams, such as those from sensors or social media.

Where to Find Databricks Notebook Examples:

1. Official Databricks Documentation:
   • Feature Engineering Notebooks: Demonstrate how to create feature tables and training models and use the Feature Store.
   • Get Started Notebook: Walks you through querying and visualizing sample data.
   • Introduction to Notebooks: Overview of notebooks, features, and usage.
2. GitHub Repository:
   • dennyglee/databricks: Contains various sample notebooks on data exploration, machine learning, and streaming.
3. Microsoft Learn:
   • Introduction to Databricks Notebooks: Explains notebooks in the context of Azure Databricks and how to import and run examples.

These examples are just a starting point. You can find numerous other examples online or create your custom notebooks tailored to your specific needs.