Databricks is a unified analytics platform that can be deployed on all three major cloud providers:

• Databricks on AWS: This is the original and most mature deployment option, offering deep integration with AWS services like S3, Redshift, and EMR.
• Databricks on Azure: This option provides native integration with Azure services like Azure Data Lake Storage, Azure Synapse Analytics, and Azure Machine Learning.
• Databricks on Google Cloud: This is the newest deployment option, offering seamless integration with Google Cloud Storage, BigQuery, and the Google Cloud AI Platform.

Which Cloud to Choose?

The best cloud for Databricks depends on your specific needs and existing infrastructure:

• Existing Cloud Investments: If you already have a significant investment in a particular cloud provider, choosing Databricks on that platform makes sense for easier integration and potential cost savings.
• Specific Service Requirements: Each cloud provider offers unique services and strengths. For example, if you heavily rely on Google Cloud’s AI capabilities, Databricks on Google Cloud might be the better choice.
• Cost Considerations: Compare the pricing of Databricks and related services on each cloud to see which option is most cost-effective for your workload.

Key Features of Databricks on All Clouds:

Regardless of the cloud provider you choose, Databricks offers the following key features:

• Unified Platform: A single platform for data engineering, data science, machine learning, and analytics.
• Collaborative Notebooks: Interactive notebooks for code development, data exploration, and visualization.
• Scalable Clusters: Easily scale compute resources up or down to match your workload demands.
• Managed Services: Databricks handles infrastructure management, allowing you to focus on your data and analytics tasks.
• Open Source Integration: Support for popular open-source tools and libraries like Apache Spark, Delta Lake, and MLflow.