Design Framework for Data Lake house solution in Azure.

Hi Relay
Thanks for your detailed and thoughtful question. Designing a reusable, scalable Data Lakehouse framework in Azure that supports both batch and streaming workloads - and is enterprise-ready - is a great goal. Here's a high-level approach to guide your implementation:

Landing Zone Design (Governed, Scalable, and Secure)

Use Azure Landing Zones as the foundation for your framework:

• Deploy via Azure Landing Zone Accelerator: https://v4.hkg1.meaqua.org/en-us/azure/cloud-adoption-framework/landing-zones/
• Configure Azure Policies, RBAC, and Management Groups to enforce compliance.
• Use Azure Blueprints or Terraform for repeatable deployments across environments and regions.

Data Lakehouse Core Components

Structure your lakehouse with:

• Azure Data Lake Storage Gen2 – unified storage for batch & streaming.
• Azure Databricks (Delta Lake) or Synapse Analytics – for processing.
• Delta Live Tables (DLT) – for declarative ETL pipelines.
• Azure Event Hubs / Kafka – for streaming ingestion.
• Azure Data Factory / Synapse Pipelines – for orchestration.

Framework Building Blocks

Make the framework modular and reusable:

• Metadata-driven pipeline orchestration (e.g., store pipeline configs in control tables).
• Parameterized ADF/Synapse pipelines for flexibility across environments.
• Reusable Databricks notebooks for common ETL logic.

Multi-Environment & Multi-Region

• Use Dev/Test/Prod resource groups per environment with CI/CD support.
• Azure DevOps / GitHub Actions for deployment automation.
• Use Azure Key Vault + Managed Identity for secure credential and key management.

Observability, Cost & Compliance

Integrate with Azure Monitor, Log Analytics, and Azure Purview for:

• Data lineage & classification
• Governance & compliance

Use Azure Cost Management for budget enforcement.

Performance Optimization & Scalability

• Design using Delta Lake best practices (Z-Ordering, OPTIMIZE, Auto Compaction).
• Use Databricks autoscaling and streaming checkpointing for resiliency.
• Partition data logically for faster query performance.

Testing Strategy

• Incorporate unit tests for notebooks using pytest and assertions.
• Use ADF/Synapse debug mode with test parameters.
• Consider tools like Great Expectations or Deequ for data quality validation.

Reference: Microsoft’s Well-Architected Framework for Analytics

Hope this helps you get started on building a robust and future-ready data framework.

If this helps, kindly click "Accept Answer" and feel free to follow up with any further questions.