Azure Databricks: A Practical Overview
Whether you’re a data engineer, analyst, or ML practitioner, Azure Databricks gives you a collaborative, managed Spark platform on Azure. In this guide we’ll cover the essentials—from standing up a workspace to navigating the UI, understanding the default catalog (hive_metastore), and the big-picture architecture.
Table of Contents
- Introduction to Azure Databricks
- Creating an Azure Databricks Service
- Databricks User Interface Overview
- Databricks Workspace Settings — Default Catalog hive_metastore
- Azure Databricks Architecture Overview
- Quick FAQs & Next Steps
1) Introduction to Azure Databricks
Azure Databricks is a first-party Azure service that combines the power of Apache Spark with Databricks’ collaborative environment, auto-scaling clusters, SQL Warehouses, and production-grade workflow orchestration...
2) Creating an Azure Databricks Service
Step-by-step instructions on how to create a Databricks workspace on Azure...
3) Databricks User Interface Overview
Overview of workspace, repos, data, compute, workflows, SQL, ML, and settings...
4) Databricks Workspace Settings — Default Catalog hive_metastore
Details on hive_metastore vs Unity Catalog, governance, and best practices...
5) Azure Databricks Architecture Overview
Explanation of control plane, data plane, storage, networking, security, governance, and performance...
6) Quick FAQs & Next Steps
Frequently asked questions and next steps for getting started with Databricks...
