1. The Strategy: “The Modernization Leap”
Most migrations fail because they treat the cloud as just “someone else’s computer.” Our strategy focuses on Re-platforming rather than just Re-hosting.
Core pillars:
Semantic Integrity: We move beyond raw tables to business logic. By leveraging Unity Catalog (Databricks) or the Ontology (Palantir), we ensure that data is discovered and understood by business users, not just engineers.
Security by Design: Governance is not a post-migration task. We apply Row-Level Security (RLS) and Attribute-Based Access Control (ABAC) during the ingestion phase.
Data Liquidity: Breaking down silos so that data flows seamlessly between engineering, data science, and business operations.
2. The 5-Phase Action Plan
Phase 1: Discovery & Assessment (Weeks 1-3)
Inventory: Map all on-prem databases, file shares, and legacy ETL pipelines.
Profiling: Identify “Dark Data” (unused data) to avoid migrating trash.
Priority Matrix & Alignment: Categorize workloads by Business Value vs. Technical Complexity while securing executive sponsorship for high-impact use cases.
Phase 2: Foundation & Governance Setup (Weeks 4-6)
Environment Build: Configure the target workspace (VPC, IAM roles, Networking).
Catalog Initialization: Databricks: Set up Unity Catalog metastores. | Palantir: Define the initial Object Types and Links in the Ontology.
Policy Definition: Establish data retention and classification standards (e.g., PII masking).
Phase 3: Pilot Migration (Weeks 7-9)
The “North Star” Use Case: Select one high-impact business question to answer.
Initial Ingestion: Use tools like Databricks Lakehouse Sync or Palantir Hyperauto to automate source connectivity.
Validation: Compare source vs. target results to ensure zero data loss.
Phase 4: Full-Scale Execution (Weeks 10-18)
Wave-Based Migration: Move data in logical “Business Domains” (e.g., Finance, then Marketing).
Pipeline Modernization: Refactor legacy SQL/SSIS into scalable Spark (PySpark) or Palantir Pipelines, replacing rigid code with observable, modular workflows.
Cataloging: Automate metadata extraction so every new table is instantly searchable.
Phase 5: Optimization & Transition (Weeks 19+)
Performance Tuning: Optimize storage formats (Delta Lake / Parquet).
User Training: Upskill teams on using the new catalog for self-service BI.
Decommissioning: Safely shut down on-prem servers to realize TCO savings.
3. Success Metrics
Time-to-Insight: Reduction in the time taken to build a new report.
Governance Coverage: Percentage of data assets tagged and classified in the catalog.
TCO Optimization: Percentage reduction in Total Cost of Ownership (TCO), accounting for hardware maintenance, legacy licensing fees, and manual operational overhead.
