Databricks and DBUtils: Cloud Data Access for MLOps

Managing data ingestion and storage access across cloud environments is a foundational skill for machine learning operations. This course guides you through interacting with distributed file systems using Databricks utilities (DBUtils) across AWS, Azure, and GCP storage layers. You will build a solid understanding of how cloud file systems operate within Databricks workspaces and how to structure secure, scalable data access patterns for your MLOps pipelines. What you'll learn: - Understand core Databricks concepts, workspace architecture, and file system basics - Navigate and manipulate files and directories using DBUtils file system commands - Configure secure connections to object storage buckets and data lakes across cloud platforms - Apply data ingestion workflows to load and prepare datasets for machine learning models - Implement best practices for managing credentials, secrets, and data governance - Integrate automated file handling routines into reproducible MLOps data pipelines The course starts with essential terminology and storage concepts before stepping through practical text-based guides and code examples for everyday data management tasks. This guide is designed for beginners in data engineering and machine learning who want to build practical cloud storage access skills without complex prerequisites. Begin reading today to streamline your cloud data access and strengthen your MLOps foundation.