comparison_table.md

Feature	Jupyter Notebooks	Databricks Notebooks
Platform	Open-source, runs locally or on cloud platforms	Exclusive to the Databricks platform
Collaboration and Sharing	Limited collaboration features, manual sharing	Built-in collaboration, real-time concurrent editing
Execution	Relies on local or external servers	Execution on Databricks clusters
Integration with Big Data	Can be integrated with Spark, requires additional configurations	Native integration with Apache Spark, optimized for big data
Built-in Features	External tools/extensions for version control, collaboration, and visualization	Integrated with Databricks-specific features like Delta Lake, built-in support for collaboration and analytics tools
Cost and Scaling	Local installations are often free, cloud-based solutions may have costs	Paid service, costs depend on usage, scales seamlessly with Databricks clusters
Ease of Use	Familiar and widely used in the data science community	Tailored for big data analytics, may have a steeper learning curve for Databricks-specific features
Data Visualization	Limited built-in support for data visualization	Built-in support for data visualization within the notebook environment
Cluster Management	Users need to manage Spark sessions and dependencies manually	Databricks platform handles cluster management and scaling automatically
Use Cases	Versatile for various data science tasks	Specialized for collaborative big data analytics within the Databricks platform