Databricks Workshop: Data & Analytics

For BI Organization - Operational Reporting & Analytics

This workshop is specifically designed for the BI Organization - analysts who support operational reporting to run day-to-day business operations. It provides a hands-on introduction to Databricks' Medallion Architecture (Bronze/Silver/Gold layers), with a strong focus on operational reporting, compliance analytics, contact center metrics, and data quality validation.

🚀 Overview

This interactive workshop guides you through:

• 📞 Contact Center Analytics: Call volumes, wait times, service levels, and agent performance
• ✅ Claims Processing Compliance: Processing times, regulatory compliance, and SLA tracking
• 👥 Member Services & Enrollment: Open enrollment tracking, retention analysis, and engagement metrics
• 📊 Operational Dashboards: Daily KPIs, trend analysis, and period-over-period comparisons
• 🔍 Data Quality Audits: Comprehensive validation checks for compliance reporting
• 💻 Databricks SQL: Hands-on SQL examples for operational reporting

📂 Medallion Architecture

• Bronze Layer (Raw Data): Raw data ingestion from CSV files using COPY INTO - preserves original data for audit trails
• Silver Layer (Cleaned Data): Data cleansing, deduplication, type corrections, and standardization
• Gold Layer (Operational Analytics 🎉): Contact center metrics, claims compliance, enrollment tracking, operational dashboards, and data quality audits

This modular pattern ensures data lineage, scalability, ACID compliance, and is the industry-standard approach for organizing data across all domains (retail, finance, manufacturing, healthcare, etc.).

🏗️ Features

🎯 Five SQL Examples for Operational Reporting

1. Contact Center Performance Metrics: Monitor call volumes, wait times, and service levels
2. Claims Processing Compliance Report: Track processing times and regulatory compliance
3. Member Services Enrollment Tracking: Analyze open enrollment and member retention
4. Operational Daily Dashboard: Daily KPIs with trend analysis and period comparisons
5. Data Quality Audit for Compliance: Validate data completeness for regulatory reporting

🎯 Hands-on Exercises

• Exercise #1: Calculate the total claims by specialty in SQL
• Exercise #2: Provider Performance by State - State-level claims aggregation and metrics
• Exercise #3: Weekly Claims Trend Analysis - Week-over-week trends with rolling averages

💻 Databricks SQL Features

• CTEs (WITH clause): Modular query structure for complex logic
• Window Functions: LAG(), SUM() OVER(), ROW_NUMBER() for trend analysis
• Advanced Aggregations: PERCENTILE(), MODE() for statistical analysis
• Date Functions: DATE_TRUNC(), DATEDIFF() for time-based analysis
• UNION ALL: Combining multiple data quality checks
• NULL Handling: NULLIF() to prevent divide-by-zero errors

🛠️ Technical Features

• Unity Catalog: Unified governance, row/column-level security
• Delta Lake: ACID transactions, time travel, schema evolution
• Predictive Optimization: Automatic table maintenance and optimization
• AI/BI & Genie: Natural language queries and self-service analytics
• Production-ready patterns: Checkpointing, caching, deterministic execution

📋 Example Dataset (Healthcare Payer)

Note: This workshop uses a healthcare payer dataset that simulates upstream data patterns currently in SQL Server. All concepts apply to your operational reporting needs.

Datasets (Bronze → Silver → Gold):

• Members: Member demographics and enrollment information
• Claims: Transaction records for claims processing operations
• Providers: Provider network information
• Diagnoses: Diagnosis codes for classification and reporting
• Procedures: Procedure details and associated costs

🛠️ Getting Started

Prerequisites

• Databricks workspace (Community Edition or higher)
• Basic SQL knowledge (SQL Server experience is helpful)
• No prior Spark/PySpark experience needed
• Familiarity with operational reporting and business intelligence

Quick Start (5 minutes)

In Databricks:

1. Open the notebook DBX Workshop_DnA_01122026.ipynb in your workspace
2. Run the setup cells to configure catalog, schemas, and load example data
3. Follow along with examples sequentially:
   • Setup and data loading
   • Bronze/Silver layer data preparation
   • Five Gold layer operational reporting examples
   • Two hands-on exercises with solutions
4. Practice with the exercises and adapt queries for your use cases!

📑 Project Structure

├── DBX Workshop_DnA_01122026.ipynb              ⭐ Main training notebook
├── [Reference] Best Practices.ipynb             📚 Best practices guide
├── README.md                                    📖 This file
├── LICENSE.md                                   📄 License
└── data/
    ├── claims.csv                               💰 Transaction/claims records
    ├── diagnoses.csv                            🏥 Classification codes
    ├── procedures.csv                           🔬 Service/procedure details
    ├── providers.csv                            👨‍⚕️ Service providers/vendors
    ├── member.csv                               👥 Member/customer data
    └── Payor_Archive.zip                        📦 Source data archive

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© 2026 | Databricks Workshop: Data & Analytics

Target Audience: BI Organization analysts supporting operational reporting
Difficulty Level: Beginner to intermediate
Focus Areas: Operational reporting, compliance analytics, contact center metrics, data quality

Last updated: January 12, 2026