Enterprise Data Architecture with Azure Databricks

Duration

2 Days

Skills Gained

By the end of this course, participants will be able to:

• Deploy and configure Azure Databricks workspaces and clusters for optimal cost and performance

• Implement the Medallion Architecture to transform raw data into high-quality assets using Delta Live Tables

• Secure and govern data assets across the organization using Unity Catalog and lineage tracking

• Automate complex data engineering workflows and schedule reliable production jobs

• Build interactive AI/BI dashboards and integrate insights directly with Power BI

• Accelerate code development and troubleshooting using the Azure Databricks Assistant

Who Can Benefit

This course is designed for:

• Enterprise & Solution Architects

• Senior Data Engineers

• Platform & Technical Leads

Prerequisites

Participants should enter this course with:

• Working knowledge of Microsoft Azure

Organizational Objectives

This course assists organizations to:

• Build in-house architectural capability for health insurance data platforms in two days

• Reduce reliance on external consultants for Lakehouse and Medallion Architecture design

• Strengthen PHI- and HIPAA-appropriate data governance through consistent Unity Catalog adoption

• Lower pipeline failure risk through applied design-pattern and anti-pattern review

Software

• A computer with an internet connection is required

• A remote lab VM with an Azure account will be provided to the participants

Module 1 - Azure Databricks Overview

By the end of this module, you will be able to describe Azure Databricks' architecture, compare it to alternative platforms, and trace a sample health insurance data workflow through the platform.

• Introduction to Azure Databricks

• Azure Databricks Architecture

• Usage Comparisons

• Workflow and Sample Flows

• Hands-on Lab: Trace a sample health insurance claims workflow through the Azure Databricks architecture, from ingestion to reporting.

Module 2 - Azure Databricks Interface and Environment

By the end of this module, you will be able to navigate the Azure Databricks workspace, configure compute clusters for cost and performance, and run queries using Databricks SQL.

• Main Page and Sections

• Compute Options: Clusters and Costs

• Workspaces and Notebooks

• Databricks SQL

• Hands-on Lab: Configure a compute cluster sized for a claims-processing workload, then run a Databricks SQL query against a sample member dataset.

Module 3 - Data Lakehouses and Unity Catalog

By the end of this module, you will be able to explain the Data Lakehouse architecture and apply Unity Catalog to govern access, quality, and lineage across sensitive health insurance data.

• Introduction to Data Lakehouses

• Unity Catalog and Governance

• Data Access and Quality

• Tracking and Auditing

• Hands-on Lab: Register a sample claims dataset in Unity Catalog, configure PHI-appropriate access controls, and trace its lineage.

Module 4 - Reference Architecture & Deployment Patterns

By the end of this module, you will be able to apply the Medallion Architecture, integrate Azure Data Factory and Delta Live Tables, and evaluate cloud vs. hybrid deployment patterns for a health insurance data platform.

• Medallion Architecture in Databricks

• Azure Data Factory and Delta Live Tables

• Cloud and Hybrid Integration Patterns

• Health Insurance Use Cases

• Hands-on Lab: Design a Medallion Architecture (bronze/silver/gold) for a health insurance claims pipeline and implement the bronze-to-silver transformation with Delta Live Tables.

Module 5 - EDA and AI/BI Dashboards

By the end of this module, you will be able to conduct exploratory data analysis, build AI/BI dashboards, and integrate insights with Power BI for health insurance reporting.

• Data Visualization and EDA

• Creating Visualizations and Dashboards

• AI/BI Dashboards in Databricks

• Integrations with Power BI

• Hands-on Lab: Build an AI/BI dashboard summarizing claims trends and member metrics, then connect it to Power BI.

Module 6 - Data Workflows, Jobs & Pipelines

By the end of this module, you will be able to automate data workflows, handle errors and scheduling, apply slowly changing dimension (SCD) patterns, and run production jobs and pipelines.

• Databricks Workflows and Automation

• Handling Errors and Scheduling Jobs

• SCD Types and DataDisplay Notebooks

• Creating and Running Jobs and Pipelines

• Hands-on Lab: Build and schedule a job that automates a claims-intake pipeline, including error handling and an SCD Type 2 update.

Module 7 - Azure Databricks Assistant

By the end of this module, you will be able to use the Databricks Assistant to accelerate code generation, troubleshooting, and dashboard development.

• Features and Usage

• Code Generation and Genie Space

• Integration with Dashboards

• Hands-on Lab: Use the Databricks Assistant and Genie Space to accelerate a claims-analytics notebook and connect the result to a dashboard.

Module 8 - End-to-End Scenario

By the end of this module, you will be able to design and troubleshoot a comprehensive Databricks solution end-to-end, recognizing common design patterns and anti-patterns.

• Comprehensive Scenario Implementation

• Design Patterns and Anti-Patterns

• Pipeline Issues and Troubleshooting

• End-to-End Health Insurance Pipeline

• Hands-on Lab: Implement and troubleshoot an end-to-end health insurance data pipeline, from ingestion through governed, dashboard-ready data.