Databricks notebooks are interactive, multi-language development environments within the Databricks Data Intelligence Platform, designed for collaborative data engineering, analytics, and machine learning workflows. They combine executable code cells with visualizations, markdown documentation, and real-time collaboration features. Unlike traditional Jupyter notebooks, Databricks notebooks provide native integration with Apache Spark, automatic versioning, built-in Git support, and serverless compute options—making them production-ready tools for both exploratory analysis and automated data pipelines. Understanding magic commands, dbutils, and the notebook execution model is essential for maximizing productivity.

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