If you are a data platform lead or enterprise architect at an ANZ-based technology consultancy or large-scale data services organization, this playbook was built for you.
Modernizing legacy ETL pipelines into a governed, AI-ready data foundation is no longer optional. You face increasing pressure to demonstrate compliance with data sovereignty requirements, enforce consistent data governance across hybrid environments, and deliver measurable ROI on cloud data investments, all while managing rising client expectations for real-time analytics and machine learning readiness. The shift from monolithic ETL tools like Azure Data Factory to event-driven, code-first architectures on Databricks introduces technical complexity, governance gaps, and audit exposure if not executed with precision. Without a structured approach, migration initiatives risk delays, rework, and failure to meet internal control standards expected by enterprise clients.
Engaging external advisors to design and implement a Databricks Lakehouse migration typically costs between EUR 80,000 and EUR 250,000 depending on scope and team composition. Alternatively, dedicating internal resources requires allocating 2 to 4 full-time engineers for 4 to 6 months to develop assessment criteria, governance controls, automation workflows, and audit documentation from scratch. This playbook delivers the same depth of planning and execution guidance for a one-time cost of $395.
What you get
| Phase | File Type | Description | Count |
| Assessment & Readiness | Domain Assessment | 30-question evaluation covering technical, operational, and governance readiness for migration from legacy ETL to Databricks Lakehouse | 7 |
| Governance Design | Template | RACI matrix template tailored for Databricks workspace roles, Unity Catalog securables, and pipeline ownership | 1 |
| Governance Design | Template | Work Breakdown Structure (WBS) for Lakehouse implementation phases including discovery, migration, testing, and decommissioning | 1 |
| Implementation Planning | Runbook | Step-by-step evidence collection guide for tracking migration progress, control implementation, and stakeholder sign-offs | 1 |
| Audit & Compliance | Playbook | Audit preparation guide with checklist for responding to internal and external review requests related to data lineage, access controls, and change management | 1 |
| Cross-Functional Alignment | Mapping Document | Detailed crosswalk between Databricks Lakehouse components and relevant control frameworks | 1 |
| Architecture & Engineering | Reference Guide | Best practice patterns for Delta Live Tables (DLT) pipeline design, error handling, and monitoring integration | 1 |
| Architecture & Engineering | Reference Guide | Unity Catalog configuration playbook covering metastore setup, data sharing policies, and fine-grained access control implementation | 1 |
| Change Management | Workbook | Stakeholder communication planner with messaging templates for IT, data governance, security, and business units | 1 |
| Operations Readiness | Checklist | Post-migration validation steps including pipeline performance benchmarks, data quality verification, and user access testing | 1 |
| Security & Compliance | Policy Template | Data classification and handling policy aligned with ANZ regulatory expectations and cloud deployment models | 1 |
| Migration Execution | Playbook | Phased migration strategy for decommissioning Azure Data Factory pipelines with minimal business disruption | 1 |
| AI Enablement | Guide | Integration patterns for connecting machine learning workloads to curated Delta tables with versioned features | 1 |
| Monitoring & Observability | Template | Alerting and dashboard specification for pipeline health, data drift, and compute utilization | 1 |
| Training & Enablement | Curriculum Outline | Role-based training plan for data engineers, analysts, and platform administrators on Databricks Lakehouse operations | 1 |
| Total Files Included | 64 |
Domain assessments
Data Architecture Maturity: Evaluates current state of data modeling, pipeline design, and alignment with medallion architecture principles.
Platform Operations: Assesses monitoring, alerting, CI/CD integration, and incident response capabilities for data pipelines.
Security & Access Governance: Reviews identity management, encryption standards, and compliance with least-privilege access models.
Data Quality & Observability: Measures coverage of data validation, anomaly detection, and automated remediation workflows.
Metadata & Lineage Management: Determines completeness of technical and business metadata tracking across source-to-target flows.
Change & Release Management: Examines processes for version control, testing, and deployment of data pipeline code and configuration.
AI/ML Integration Readiness: Gauges preparedness for feature store usage, model training data access, and reproducibility practices.
What this saves you
| Activity | Without This Playbook | With This Playbook |
| Develop migration assessment framework | 40, 60 hours of architect time to define criteria and questions | Download and apply 7 pre-built 30-question assessments (210 total questions) |
| Design governance structure | 20+ hours to draft RACI and WBS from scratch | Use editable templates aligned with Databricks workspace and Unity Catalog roles |
| Prepare for internal audit | Scattergather evidence across teams and systems | Follow runbook for systematic evidence collection and documentation |
| Map controls to compliance requirements | Manual research across multiple frameworks | Leverage included cross-framework mapping document |
| Train team on Lakehouse patterns | Ad hoc knowledge transfer or external training | Distribute role-based curriculum and reference guides |
| Decommission legacy ETL safely | Risk of service interruption due to incomplete validation | Execute phased cutover using migration playbook and validation checklist |
Who this is for
- Enterprise data architects leading cloud data platform modernization initiatives
- Platform engineering managers responsible for Databricks workspace governance
- Consulting delivery leads managing data migration projects for enterprise clients
- Head of Data Office functions overseeing cross-functional data governance programs
- Compliance officers needing to validate data pipeline controls for audit purposes
- Technical program managers coordinating migration timelines and stakeholder alignment
- Cloud data practice leads building repeatable delivery assets for client engagements
Cross-framework mappings
Databricks Lakehouse Architecture
Unity Catalog Security Model
Delta Live Tables (DLT) Pipeline Design
ANZ Prudential Standards (APS 116, CPS 234)
NIST Cybersecurity Framework (CSF)
ISO/IEC 27001:2022
COBIT 2019
ISACA Data Governance Standard
Cloud Security Alliance (CSA) CCM v4.0
Microsoft Azure Security Benchmark
PCI DSS 4.0
GDPR Article 30 Records of Processing
What is NOT in this product
- Custom configuration of your Databricks workspace or Unity Catalog environment
- Onsite consulting, training, or implementation services
- Source code for automated migration tools or connectors
- Access to proprietary software or third-party tools
- Legal advice or regulatory interpretation specific to your organization
- Hosting, storage, or cloud infrastructure
- Real-time support or ticketed assistance
Lifetime access and satisfaction guarantee
This playbook requires no subscription and does not rely on a login portal. Once downloaded, all files are yours to use, modify, and distribute within your organization. You receive lifetime access to the version purchased. If this playbook does not save your team at least 100 hours of manual compliance work, email us for a full refund. No questions, no friction.
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