Great! Since your first topic is Data Lakehouse Architecture, the next step should build smoothly toward using Databricks practically—with cloud context (AWS or Azure).
Here’s a suggested progression roadmap and what cloud-specific highlights to include at each step:
🔁 Follow-Up Sequence (Beginner → Advanced)
✅ 1. Lakehouse Basics (You’ve Done)
- Cover distinctions: Data Lake vs Warehouse vs Lakehouse
- Introduce Delta Lake / Iceberg / Hudi (mention their role in Lakehouse)
✅ 2. Cloud Foundation (Azure + AWS for Databricks)
👇 Choose Azure or AWS depending on your target audience; cover both if needed.
🌩️ Cloud Concepts to Highlight:
| Topic | Azure | AWS |
|---|---|---|
| Cloud Storage | Azure Data Lake Storage Gen2 (ADLS) | Amazon S3 |
| Compute (VMs / Clusters) | Azure VMs (under Databricks) | EC2 / EKS (managed by Databricks) |
| Identity + Security | Azure Active Directory, RBAC | IAM roles, policies, Lake Formation |
| Metadata Catalog | Unity Catalog / Hive Metastore | Unity Catalog / AWS Glue |
| Key Databricks Resource | Azure Databricks Workspace | Databricks on AWS |
| Networking (basic) | VNet injection, Private Link | VPC, PrivateLink, S3 buckets access policy |
✅ 3. Getting Started on Databricks
Create your first notebook and cluster.
- Walkthrough: Creating a workspace (Azure/AWS UI)
- Launching cluster (standard vs serverless)
- Creating notebooks (Python, SQL)
- Mounting storage (S3 / ADLS) with configs
- Uploading sample files
📝 Hands-on: Load CSV/JSON from cloud storage and do basic DataFrame operations.
✅ 4. Delta Lake Deep Dive
- Delta Table = Parquet + Transaction Log
- Versioning, Time Travel
- Upsert (MERGE INTO), Delete, Update
- Optimization: ZORDER, OPTIMIZE, VACUUM
- Streaming Support
🧪 Demo: Convert CSV → Delta → update records → rollback
✅ 5. SQL Warehousing + BI
- Databricks SQL workspace (visual layer)
- Connect to Power BI / Tableau / Looker
- Build a simple dashboard
✅ 6. Advanced Topics
- Medallion Architecture (Bronze, Silver, Gold)
- Data Quality with Expectations (e.g., Delta Live Tables)
- Job Scheduling with Workflows
- CI/CD with GitHub/Bitbucket
- Streaming with Auto Loader or Kafka
☁️ Where to Add AWS or Azure Cloud Highlights?
| Tutorial Topic | AWS Context | Azure Context |
|---|---|---|
| Storage Layer | S3 (boto3, access key, IAM role) | ADLS Gen2 (OAuth, SAS token, Storage Key) |
| Mounting Buckets to DBFS | S3 mount script | ADLS mount via OAuth2 configs |
| Security & Roles | IAM Role vs Instance Profile | RBAC, Service Principal |
| Unity Catalog + External Tables | Catalogs via AWS Glue + S3 | Unity with ADLS & Azure Purview |
| Orchestration | Airflow on AWS, Step Functions | Azure Data Factory or Logic Apps |
🔁 Example Post Sequence:
- What is a Data Lakehouse?
- Cloud Setup for Databricks (Azure & AWS)
- Creating Your First Databricks Notebook
- Delta Lake Essentials (Time Travel, Upsert)
- Data Ingestion from ADLS/S3
- SQL & BI Dashboarding in Databricks
- Deploying Medallion Architecture
- Databricks Jobs & Workflows (Scheduled Pipelines)
- Streaming Data with Auto Loader
- Git CI/CD for Databricks Projects