Complete Aws to Databricks Migration Checklist

Published on: November 11, 2025 03:21

Complete AWS DataStage to Databricks Migration Checklist

Authored By: A Principal Data Engineer & Platform Architect
Version: 1.0
Purpose: This document is the single source of truth for planning, executing, and finalizing the migration of data pipelines from AWS DataStage to the Databricks Data Intelligence Platform. It is intended for the core migration team to drive weekly progress and ensure all critical activities are tracked to completion.

1. Pre-Migration Readiness Checklist

This phase is about establishing the "why" and "who" before a single job is touched. Skipping this is the #1 cause of budget overruns and stakeholder friction.

  • [ ] Business Drivers & Success Criteria:

    • [ ] Document and gain executive sign-off on the primary business drivers (e.g., reduce TCO, improve performance, enable ML/AI, decommission legacy tech).
    • [ ] Define and quantify success criteria. (e.g., "Reduce average pipeline execution time by 30%," "Lower data platform TCO by 20% within 12 months," "Achieve 99.9% data reconciliation for critical finance reports").
    • [ ] Identify and confirm the business owners for key data domains who will provide final sign-off.

  • [ ] Stakeholder Alignment & Ownership:

    • [ ] Create and publish a RACI (Responsible, Accountable, Consulted, Informed) matrix for all key roles (Program Manager, Tech Lead, Business Owner, Security, Infrastructure).
    • [ ] Establish a recurring steering committee meeting with executive sponsors and key stakeholders.
    • [ ] Confirm the "Accountable" owner for the migration budget and timeline.

  • [ ] Budget, Timeline, and Risk Assumptions:

    • [ ] Secure approved budget covering tools, platform costs (Databricks, AWS), and human resources (internal & external).
    • [ ] Develop a high-level, phased migration timeline with major milestones. (High-Risk Item: Be realistic. A typical enterprise migration is 12-24 months, not 6).
    • [ ] Document all major assumptions (e.g., "Less than 10% of jobs require complete architectural redesign," "Key personnel will be 100% allocated").
    • [ ] Create an initial risk register (e.g., skill gaps, unforeseen job complexity, data validation challenges).

  • [ ] Skills and Team Readiness:

    • [ ] Conduct a skills assessment of the core team against Spark, Python/Scala, SQL, and Databricks.
    • [ ] Finalize the team structure (e.g., migration pods, a central platform team).
    • [ ] Initiate a training plan or hire/contract for missing skills. Do not assume DataStage developers can be productive in Spark overnight.
    • [ ] Onboard team to the Databricks environment and provide foundational training.

2. Discovery & Assessment Checklist

This phase is about defining the "what" and "how hard." Under-investing here leads to massive scope creep and inaccurate estimates.

  • [ ] AWS Job Inventory:

    • [ ] Extract a complete inventory of all AWS jobs and sequences from the AWS repository.
    • [ ] Augment inventory with metadata from schedulers (e.g., Control-M, Autosys) to capture actual execution frequency and SLAs.

  • [ ] Metadata Extraction:

    • [ ] Automate the extraction of critical metadata for each job: source/target tables, SQL overrides, transformation logic, parameters, and annotations.
    • [ ] Store extracted metadata in a queryable format (e.g., a database or a Delta table) to facilitate analysis.

  • [ ] Dependency and Lineage Analysis:

    • [ ] Parse job metadata to build a dependency graph (job A -> table B -> job C).
    • [ ] Visualize the lineage to identify critical paths and logical groupings for migration waves.
    • [ ] High-Risk Item: Manually verify lineage for the most critical pipelines. Automated parsing often misses dependencies triggered by external scripts or obscure database triggers.

  • [ ] Identification of Unused or Redundant Jobs:

    • [ ] Analyze job logs and target table access patterns to identify jobs that are obsolete, redundant, or failing.
    • [ ] Secure business owner agreement to decommission (not migrate) these jobs. This is your primary lever for scope reduction.

  • [ ] Complexity and Migration Effort Classification:

    • [ ] Define migration complexity buckets (e.g., Simple: SQL-based, Medium: Basic transformations, Complex: Procedural logic/loops, Very Complex: Custom plugins, undocumented functions).
    • [ ] Classify every job in the inventory.
    • [ ] Use this classification to create a bottom-up effort estimate and prioritize migration waves.

3. Architecture & Design Checklist

This is the blueprint for the target state. Architectural mistakes made here are the most expensive to fix later.

  • [ ] Target Databricks Architecture:

    • [ ] Finalize and document the target data architecture (e.g., Medallion Architecture: Bronze/Silver/Gold layers).
    • [ ] Decide on the Unity Catalog strategy. (Opinion: Use Unity Catalog from day one. Retrofitting it is painful).
    • [ ] Define the integration patterns for ingress (e.g., Auto Loader) and egress (e.g., JDBC, Power BI Connector).

  • [ ] Storage, File formats, and Partitioning Strategy:

    • [ ] Confirm target storage layer (e.g., AWS S3).
    • [ ] Standardize on Delta Lake as the default format for all managed tables.
    • [ ] Define a standard for S3 bucket and path naming conventions (e.g., s3://<bucket>/<bronze|silver|gold>/<domain>/<table>/).
    • [ ] High-Risk Item: Define and review partitioning strategies for large tables (>100GB) with data architects. Poor partitioning kills performance.

  • [ ] Security, Governance, and Access Model:

    • [ ] Design the Unity Catalog access control model. Map existing user roles (e.g., from Active Directory) to Databricks groups and privileges (e.g., SELECT on Gold tables for BI users).
    • [ ] Define the network architecture (e.g., VNet injection, Private Link).
    • [ ] Plan for PII/sensitive data handling (e.g., masking at the Silver/Gold layer).

  • [ ] Environment Strategy (dev, test, prod):

    • [ ] Define the Databricks workspace strategy (e.g., separate workspaces for dev, test, prod).
    • [ ] Document the promotion path for code and artifacts (e.g., Git -> CI/CD -> Dev -> Test -> Prod).
    • [ ] Establish cluster policies to enforce tagging, sizing, and cost controls in all environments.

4. Mapping & Refactoring Checklist

The technical heart of the migration. Focus on patterns, not just line-for-line conversion.

  • [ ] AWS Stages → Databricks/Spark Patterns:

    • [ ] Create a "cookbook" mapping common AWS stages to PySpark/SQL functions (e.g., Lookup -> Broadcast Join, Aggregator -> groupBy().agg(), Transformer -> withColumn()).
    • [ ] Identify AWS-specific functions (e.g., DSParallel, specific database functions) and design standard Spark equivalents.

  • [ ] SQL and Stored Procedure Handling:

    • [ ] Strategy for migrating stored procedure logic:
      • Option A: Rewrite as PySpark DataFrames (most scalable).
      • Option B: Encapsulate in SQL UDFs.
      • Option C: Run via Databricks SQL for ELT patterns.
    • [ ] Convert proprietary SQL dialects to ANSI SQL/Spark SQL.

  • [ ] Handling Complex Transformations:

    • [ ] High-Risk Item: Isolate jobs with heavy procedural logic (loops, stateful variables). These require a full redesign into a functional, set-based paradigm for Spark, not a simple lift-and-shift.
    • [ ] Develop patterns for handling slowly changing dimensions (SCDs) using Delta Lake's MERGE capability.

  • [ ] Refactoring Parallelism and Sequencing Logic:

    • [ ] Analyze how AWS achieved parallelism (e.g., multiple invokations, partitioning) and map it to Spark's distributed nature.
    • [ ] Convert internal job control logic (e.g., JobActivity) to task dependencies within a Databricks Workflow.

5. Orchestration & Scheduling Checklist

A job that doesn't run reliably is a useless job.

  • [ ] AWS Sequences Mapping:

    • [ ] Map each AWS Sequence to a corresponding Databricks Workflow.
    • [ ] Document the entry point for each workflow (e.g., schedule, file arrival trigger, API call).

  • [ ] Databricks Workflows and Job Dependencies:

    • [ ] Re-implement intra-job dependencies as tasks within a Databricks Workflow DAG.
    • [ ] Implement inter-job dependencies (across Workflows) using methods like the Databricks API or sensors in an external orchestrator (e.g., Airflow).

  • [ ] Error Handling and Restart Strategy:

    • [ ] Standardize error handling and notification patterns (e.g., on-failure email/Slack/PagerDuty notifications).
    • [ ] Define retry logic for transient failures.
    • [ ] High-Risk Item: For critical jobs, design for idempotency to allow safe restarts from the point of failure.

  • [ ] SLA and Scheduling Alignment:

    • [ ] Port all cron schedules from the source scheduler to Databricks Workflows.
    • [ ] Verify that the new schedule and expected runtime will meet existing business SLAs.

6. Development & Conversion Checklist

Establish the "factory" for converting jobs efficiently and consistently.

  • [ ] Code Standards and Structure:

    • [ ] Define and enforce code standards (e.g., PEP8 for Python, style guides for SQL).
    • [ ] Mandate a standard project/repository structure for all migrated jobs.
    • [ ] Create standard code templates for common job types (e.g., Bronze ingestion, Silver transformation).

  • [ ] Parameterization and Configuration Handling:

    • [ ] Mandate that all environment-specific values (paths, database names, etc.) are parameterized. No hardcoded values in notebooks or scripts.
    • [ ] Use Databricks Widgets or configuration files (e.g., YAML/JSON) passed into jobs.

  • [ ] Environment-Specific Deployment Practices:

    • [ ] Set up and mandate the use of a CI/CD pipeline (e.g., GitHub Actions, Azure DevOps) for deploying code and job definitions (as Databricks Asset Bundles or JSON).
    • [ ] Prohibit manual code promotion or "cowboy coding" in test/prod environments.

  • [ ] Version Control and Collaboration Setup:

    • [ ] All code must reside in a Git repository (e.g., GitHub, GitLab).
    • [ ] Enforce a branching strategy (e.g., GitFlow) and require pull requests with reviews for all changes.

7. Data Validation & Testing Checklist

Trust, but verify. This is how you earn business sign-off.

  • [ ] Row Count and Reconciliation Strategy:

    • [ ] Automate row count comparisons between source AWS tables and target Databricks tables for every job.
    • [ ] For financial or critical data, implement automated checksum/hash validation on key columns.

  • [ ] Data Quality and Transformation Validation:

    • [ ] Develop a test harness to compare the output of a specific AWS transformation against the new Spark logic using a sample dataset.
    • [ ] Implement data quality checks (e.g., NOT NULL, value ranges) in the new pipelines using expectations or assertions.

  • [ ] Performance Benchmarking:

    • [ ] For each migrated job/workflow, record execution times in Databricks.
    • [ ] Compare against AWS baseline runtimes to ensure SLAs are met. Flag any regressions for optimization.

  • [ ] Parallel Run Planning:

    • [ ] For critical workflows, plan and execute a parallel run period (e.g., 1-2 weeks) where both AWS and Databricks pipelines run simultaneously.
    • [ ] Build automated reconciliation reports to compare the outputs of the parallel runs daily, proving consistency before cutover.

8. Performance & Cost Optimization Checklist

A migrated platform that is slow and expensive is a failure. This is a continuous process, not a one-time task.

  • [ ] Cluster Sizing and Runtime Selection:

    • [ ] Right-size job clusters based on actual workload data. Avoid over-provisioning.
    • [ ] Leverage autoscaling for all job clusters.
    • [ ] Use the latest Databricks Runtime (DBR) and enable Photon where it provides a clear performance/cost benefit.

  • [ ] Partitioning and File Optimization:

    • [ ] Run OPTIMIZE and Z-ORDER on large, frequently queried Delta tables as part of the ETL workflow.
    • [ ] Review and tune Spark shuffle partitions (spark.sql.shuffle.partitions).

  • [ ] Caching and Persistence Review:

    • [ ] Use Spark caching (.cache()) strategically for dataframes that are reused multiple times within a single job. (High-Risk Item: Over-caching causes memory issues. Use it judiciously).

  • [ ] Cost Monitoring and Guardrails:

    • [ ] Set up cost monitoring dashboards using system tables or cloud monitoring tools.
    • [ ] Implement cluster policies to enforce cost-related tags and limit cluster sizes.
    • [ ] Set up budget alerts to notify the platform owner of potential overruns.

9. Security, Governance & Compliance Checklist

Integrate security from the start. Don't wait for the CISO to block your go-live.

  • [ ] Data Access Controls:

    • [ ] Implement and test all table, row, and column-level access controls in Unity Catalog as per the design.
    • [ ] Conduct a review with data owners to confirm permissions are correct.

  • [ ] Audit and Logging:

    • [ ] Enable and configure Unity Catalog audit logs.
    • [ ] Ensure logs are streamed to a central SIEM (Security Information and Event Management) system.

  • [ ] Regulatory and Compliance Validation:

    • [ ] Engage the compliance team to validate that the new platform meets all regulatory requirements (GDPR, CCPA, etc.).
    • [ ] Confirm PII detection and masking/anonymization processes are working as designed.

  • [ ] Secrets and Credential Management:

    • [ ] Eradicate all hardcoded secrets.
    • [ ] All secrets must be stored and accessed via Databricks Secrets, backed by a service like AWS Secrets Manager or Azure Key Vault.

10. Cutover & Go-Live Checklist

Meticulous planning prevents a chaotic go-live weekend.

  • [ ] Cutover Planning and Rollback Strategy:

    • [ ] Create a detailed, step-by-step cutover plan for each migration wave (e.g., "1. Disable AWS sequence," "2. Run final reconciliation," "3. Enable Databricks workflow").
    • [ ] High-Risk Item: Document and test the rollback plan. What are the exact steps to re-enable AWS if a critical failure occurs post-cutover?
    • [ ] Define the Go/No-Go criteria for the cutover event.

  • [ ] Business Sign-Off:

    • [ ] Obtain formal, written sign-off from the business data owners on the data validation and parallel run results.

  • [ ] Monitoring and Alerting Setup:

    • [ ] Confirm all production monitoring dashboards and critical alerts are active and tested before the cutover window.
    • [ ] Establish a "war room" (physical or virtual) and on-call rotation for the go-live period.

  • [ ] Support Readiness:

    • [ ] Train the L1/L2 support teams on the new platform.
    • [ ] Provide them with a runbook for common issues and an escalation path.

11. Post-Migration Checklist

The migration isn't complete until the old platform is turned off.

  • [ ] Decommissioning AWS Assets:

    • [ ] After a stabilization period (e.g., 30 days), begin the decommissioning process.
    • [ ] Step 1: Disable the old AWS jobs and sequences.
    • [ ] Step 2: Archive the old job code.
    • [ ] Step 3 (The most important for ROI): Schedule and execute the shutdown of the AWS servers/environment.

  • [ ] Cost and Performance Review:

    • [ ] After 1-3 months, conduct a formal review of Databricks costs vs. the original AWS costs.
    • [ ] Compare production pipeline performance against the initial success criteria.

  • [ ] Knowledge Transfer and Documentation:

    • [ ] Consolidate all design documents, runbooks, and code documentation into a central knowledge base (e.g., Confluence).
    • [ ] Conduct final knowledge transfer sessions with the permanent operations and development teams.

  • [ ] Lessons Learned and Optimization Backlog:

    • [ ] Hold a retrospective with the entire migration team and key stakeholders.
    • [ ] Document what went well and what could be improved for future migrations.
    • [ ] Create a backlog of future optimization tasks (e.g., refactoring complex jobs, exploring new Databricks features).