Complete DataStage to Databricks Migration Checklist
Published on: January 08, 2026 04:16 AM
Complete DataStage to Databricks Migration Checklist
Author: Principal Data Engineer & Platform Architect
Version: 2.1
Purpose: This document is an execution playbook for migrating enterprise DataStage workloads to the Databricks Lakehouse Platform. It is intended for the core migration team and senior stakeholders to track readiness, identify risks, and ensure a successful transition.
1. Pre-Migration Readiness Checklist
(Phase Goal: Establish a rock-solid foundation. Misalignment here is the #1 cause of project failure.)
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[ ]Business Drivers & Success Criteria:[ ]Primary business drivers formally documented and signed off (e.g., Reduce TCO, increase data processing speed, enable ML capabilities, retire technical debt).[ ]Success metrics are defined and measurable (e.g., "Reduce ETL processing window by 40%", "Decommission 3 DataStage servers by Q4", "Reduce licensing/infra cost by $X").[ ]Key stakeholders (Business, IT, Finance, Security) have reviewed and agreed upon the drivers and metrics. This is non-negotiable.
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[ ]Stakeholder Alignment & Ownership:[ ]Executive Sponsor identified and actively engaged.[ ]Clear RACI chart defined for all key roles (Migration Lead, Architect, Dev Lead, Business Owner, PM).[ ]Business data owners identified for each subject area to be migrated; they are responsible for UAT sign-off.[ ]Scheduled a recurring steering committee meeting with all key stakeholders.
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[ ]Budget, Timeline, and Risk Assumptions:[ ]Budget approved, with a specific contingency allocation (recommend 15-20%).[ ]High-level timeline established with phased rollouts. Avoid a "big bang" migration at all costs.[ ]Initial risk register created, including top risks like skill gaps, data validation complexity, and dependency creep.[ ]Assumptions are explicitly listed (e.g., "Assume <10% of jobs require complete re-architecture," "Assume cloud environment will be provisioned by date X").
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[ ]Skills and Team Readiness:[ ]Core team skills assessment completed for PySpark/Scala, SQL, Databricks platform administration, and cloud infrastructure (Azure/AWS/GCP).[ ]Identified skill gaps and created a mitigation plan (e.g., targeted training, hiring specialists, engaging a partner). Do not underestimate the learning curve from visual ETL to code-based ETL.[ ]A "migration champion" or lead developer from the existing DataStage team is embedded in the core migration team. Their domain knowledge is invaluable.
2. Discovery & Assessment Checklist
(Phase Goal: Create a detailed, data-driven inventory of the work. No guesswork.)
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[ ]DataStage Job Inventory:[ ]Extracted a complete list of all DataStage projects, jobs, and sequences from the XMETA repository or via command-line tools.[ ]Cross-referenced the job list with the scheduler (e.g., TWS, Control-M, Autosys) to identify which jobs actually run.[ ]Used repository queries to pull job metadata: last run time, run duration, and success/failure rates.
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[ ]Metadata Extraction & Analysis:[ ]Parsed.dsxexports or used an automated discovery tool to extract source/target systems, stage types, transformation logic, and parameters for every job.[ ]Created a searchable catalog of all DataStage assets and their metadata.[ ]Identified all unique DataStage stage types in use (e.g., Transformer, Aggregator, Join, Lookup, Funnel). Pay special attention to non-standard or custom stages.
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[ ]Dependency and Lineage Analysis:[ ]Mapped all DataStage Sequence dependencies to build an end-to-end lineage graph. Focus on sequences, not just individual jobs.[ ]Identified all external dependencies: upstream file producers, downstream consumers, database triggers, API calls.[ ]Documented dependencies on shared containers, parameter sets, and environment variables.
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[ ]Complexity and Migration Effort Classification:[ ]Classified all jobs into migration patterns and complexity buckets:- Low (Automated Conversion/Simple Refactor): Simple source->target loads, basic filters, and joins.
- Medium (Guided Refactor): Complex Transformer logic, aggregations, lookups. Requires thoughtful rewrite into Spark patterns.
- High (Re-Architect): Jobs with Pivot stages, server routines, complex looping logic in sequences, or session-parameter dependencies. These cannot be "lifted and shifted."
- Blocker (Investigate/Replace): Jobs using custom C++ stages, unsupported database connectors, or obsolete business logic.
[ ]Identified and tagged unused/redundant jobs for decommissioning, not migration. Do not migrate your legacy baggage.
3. Architecture & Design Checklist
(Phase Goal: Define the target state. Rushing this leads to a poorly performing and unmanageable platform.)
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[ ]Target Databricks Architecture:[ ]Defined target data architecture (e.g., Medallion Architecture: Bronze/Silver/Gold layers).[ ]Mapped existing data layers (e.g., Staging, ODS, Marts) to the new Medallion structure.[ ]Designed the landing/raw zone for data ingestion from source systems.[ ]Selected the primary compute model: Databricks SQL for BI vs. All-Purpose/Jobs clusters for ETL/ML.
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[ ]Storage, File Formats, and Partitioning Strategy:[ ]Standard file format chosen (Recommendation: Delta Lake for almost everything). Justify any use of Parquet, Avro, etc.[ ]Defined a global partitioning strategy for large tables (e.g., partition by date).[ ]Defined file optimization strategy (OPTIMIZE,Z-ORDER) for key Silver/Gold tables.
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[ ]Security, Governance, and Access Model:[ ]Chosen governance model (Recommendation: Unity Catalog for new projects).[ ]Defined the role-based access control (RBAC) model. How will existing AD/LDAP groups map to Databricks roles and privileges?[ ]Defined data access patterns (e.g., service principals for production jobs, user groups for ad-hoc query access).
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[ ]Environment Strategy:[ ]Defined the environment promotion path (e.g., DEV → TEST → PROD).[ ]Determined the implementation: separate Databricks workspaces for full isolation vs. separate catalogs/schemas within a single workspace. Separate workspaces are strongly recommended for Prod.[ ]Cloud networking strategy defined (VNet/VPC injection, private endpoints, firewall rules).
4. Mapping & Refactoring Checklist
(Phase Goal: Translate DataStage concepts into efficient Spark patterns. This is the core technical work.)
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[ ]DataStage Stages → Databricks/Spark Patterns:[ ]Created a "cookbook" mapping common DataStage stages to PySpark/Spark SQL functions (Transformer→withColumn,Aggregator→groupBy().agg(),Lookup→ broadcast join, etc.).[ ]Defined the strategy for handling DataStage's partitioned datasets. Do not try to replicate DataStage's partitioning logic; leverage Spark's native partitioning and shuffle mechanisms.
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[ ]SQL and Stored Procedure Handling:[ ]Strategy for migrating stored procedures: Keep in DB (if performant), migrate to Spark SQL, or rewrite as a PySpark DataFrame transformation.[ ]Reviewed all "SQL before/after" job stages and "Stored Procedure" stages for refactoring opportunities.
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[ ]Handling Complex Transformations:[ ]Specific patterns developed for high-complexity items identified in discovery (e.g., Pivot, vertical-to-horizontal transforms).[ ]Plan for replacing proprietary DataStage functions (e.g.,Ereplace,Iconv/Oconv) with Spark equivalents or UDFs. Minimize UDFs for performance reasons.[ ]Strategy for handling job logic that relies on sequential processing or record-order dependencies. This is an anti-pattern in Spark and requires re-architecture.
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[ ]Refactoring Parallelism and Sequencing Logic:[ ]Confirmed developers understand the shift from DataStage's pipeline parallelism to Spark's distributed data parallelism.[ ]Job-level sequencing (e.g., Job A runs before Job B) mapped to Databricks Workflows.[ ]Intra-job flow constraints (e.g., waiting for one data flow to finish before starting another) re-evaluated. Often, these can be combined into a single Spark job.
5. Orchestration & Scheduling Checklist
(Phase Goal: Ensure the new jobs run reliably, on schedule, and with proper dependency management.)
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[ ]DataStage Sequences Mapping:[ ]Analyzed all Sequence logic (loops, conditional branches, exception handlers).[ ]Designed target Databricks Workflows to replicate the business logic. This is a redesign, not a 1:1 mapping.[ ]Identified sequences that can be simplified or consolidated.
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[ ]Databricks Workflows and Job Dependencies:[ ]Defined standard workflow patterns for ingestion, transformation, and load tasks.[ ]Mapped external file/event dependencies to Databricks triggers (e.g., File Arrival Triggers,Jobs.runNowAPI calls).[ ]Validated multi-workflow dependencies (e.g., the "Daily Load" workflow depends on the "Master Data" workflow).
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[ ]Error Handling and Restart Strategy:[ ]Defined a standard error handling and notification pattern for all jobs.[ ]Defined restart/retry logic for tasks within a workflow.[ ]Documented the procedure for manual job reruns, including how to handle partially loaded data (Delta Lake's ACID properties are key here).
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[ ]SLA and Scheduling Alignment:[ ]Migrated production schedules from the external scheduler to Databricks Workflows or updated the external scheduler to call the Databricks Jobs API.[ ]Validated that the new workflow schedules and runtimes will meet or exceed existing business SLAs.
6. Development & Conversion Checklist
(Phase Goal: Establish a disciplined and repeatable development process.)
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[ ]Code Standards and Structure:[ ]Programming language standardized (PySpark is the common choice).[ ]Code style guide and linting tools established (e.g., Black, Flake8 for Python).[ ]Project structure for Databricks Repos defined (e.g., folders for notebooks, libraries, tests, config).[ ]Strategy for reusable code (e.g., shared Python libraries packaged as wheels).
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[ ]Parameterization and Configuration Handling:[ ]Standardized method for parameterizing jobs (e.g., Databricks Job Parameters). Avoid hardcoding in notebooks.[ ]Configuration management strategy in place for environment-specific values (e.g., DB connections, file paths).
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[ ]Environment-Specific Deployment Practices:[ ]CI/CD pipeline established for automated testing and deployment of code (notebooks, libraries) and job definitions (Terraform/Databricks CLI).[ ]Promotion process from DEV to TEST to PROD is automated and requires approvals. Manual notebook promotion to Prod is a high-risk anti-pattern.
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[ ]Version Control and Collaboration Setup:[ ]All code and job configurations are stored in a Git repository (e.g., GitHub, Azure Repos). This is non-negotiable.[ ]Branching strategy defined (e.g., GitFlow).[ ]Pull Request (PR) process with mandatory code reviews is enforced.
7. Data Validation & Testing Checklist
(Phase Goal: Prove, with data, that the new system is correct.)
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[ ]Row Count and Reconciliation Strategy:[ ]Automated framework built to compare source/target row counts between DataStage and Databricks runs.[ ]Validation performed at every layer (Bronze, Silver, Gold).
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[ ]Data Quality and Transformation Validation:[ ]Field-level data validation strategy defined (e.g., comparing checksums or hashes of critical columns).[ ]Numeric measure validation (SUM, AVG, MIN, MAX) for key fact tables.[ ]A "diff" tool or process is in place to identify specific records that do not match between old and new outputs.
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[ ]Performance Benchmarking:[ ]Baseline performance metrics captured from existing DataStage jobs (runtime, CPU/memory usage).[ ]Performance tests executed for migrated Databricks jobs to ensure they meet SLA.
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[ ]Parallel Run Planning:[ ]Plan for running DataStage and Databricks pipelines in parallel for a defined period (e.g., one business cycle).[ ]Downstream consumption temporarily duplicated or managed to handle dual data sources during the parallel run. This is complex but essential for building confidence.
8. Performance & Cost Optimization Checklist
(Phase Goal: Ensure the solution is not just correct, but also efficient and cost-effective.)
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[ ]Cluster Sizing and Runtime Selection:[ ]Defined standard T-shirt sizes for job clusters (Small, Medium, Large).[ ]Using latest stable Databricks Runtime (DBR) and Photon where applicable.[ ]Using Job clusters over All-Purpose clusters for all automated workloads to reduce cost.
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[ ]Partitioning and File Optimization:[ ]Post-migration review of table partitioning schemes based on actual query patterns.[ ]Scheduled jobs to runOPTIMIZEandZ-ORDERon critical Delta tables.[ ]File compaction strategies are in place to solve the "small files problem."
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[ ]Caching and Persistence Review:[ ]Code reviewed for appropriate use of Spark caching (.cache(),.persist()). Over-caching is a common and costly mistake.[ ]Verified that intermediate dataframes are not being cached unnecessarily.
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[ ]Cost Monitoring and Guardrails:[ ]All clusters and jobs are tagged with owner, project, and cost center.[ ]Budgets and alerts configured in the cloud provider's cost management tool.[ ]Regular cost review meetings scheduled to identify and address runaway jobs or inefficient clusters.
9. Security, Governance & Compliance Checklist
(Phase Goal: Ensure the new platform is secure and meets all regulatory requirements.)
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[ ]Data Access Controls:[ ]All tables in the production catalog have defined owners and ACLs applied via Unity Catalog.[ ]Validated that users and service principals have the minimum required privileges.[ ]Row-level security and column-masking implemented where required.
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[ ]Audit and Logging:[ ]Databricks audit logs are enabled and configured to stream to a central monitoring system (e.g., Splunk, Azure Log Analytics).[ ]Key events are being monitored: permission changes, job failures, cluster creation.
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[ ]Regulatory and Compliance Validation:[ ]Confirmed that PII/sensitive data handling logic (masking, tokenization) was correctly migrated and validated.[ ]Engaged with the corporate compliance/GRC team to get formal sign-off on the new platform's adherence to GDPR, CCPA, HIPAA, etc.
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[ ]Secrets and Credential Management:[ ]Verified that no secrets (passwords, API keys) are hardcoded in notebooks or configuration.[ ]All secrets are stored and accessed via Databricks Secrets, backed by a secure vault (e.g., Azure Key Vault, AWS Secrets Manager).
10. Cutover & Go-Live Checklist
(Phase Goal: Execute a seamless transition from the old system to the new.)
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[ ]Cutover Planning and Rollback Strategy:[ ]A detailed, hour-by-hour cutover plan (runbook) is created and reviewed by all teams.[ ]"Go/No-Go" decision points and criteria are clearly defined.[ ]A rollback plan is documented and tested. If you can't roll back, you're not ready to go live.
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[ ]Business Sign-Off:[ ]Formal User Acceptance Testing (UAT) completed.[ ]Written sign-off received from the business data owners for each migrated workflow.
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[ ]Monitoring and Alerting Setup:[ ]Production monitoring configured for all new Databricks jobs.[ ]Alerts are routed to the correct L1/L2 support teams.[ ]On-call rotation and escalation paths are confirmed.
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[ ]Support Readiness:[ ]Operations/Support team has been trained on the new platform, runbooks, and troubleshooting procedures.[ ]A "hypercare" period defined (e.g., 2-4 weeks) with heightened monitoring and dedicated support from the migration team.
11. Post-Migration Checklist
(Phase Goal: Realize the full value of the migration and set the stage for future work.)
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[ ]Decommissioning DataStage Assets:[ ]After the hypercare period, old DataStage jobs are disabled in the scheduler.[ ]After a stability period (e.g., one month/quarter-end), DataStage servers and supporting infrastructure are powered down and eventually decommissioned. This is critical for achieving TCO reduction.[ ]Old DataStage projects are archived.
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[ ]Cost and Performance Review:[ ]Post-migration cost and performance metrics are compared against the initial business case and success criteria.[ ]The results are communicated to the executive sponsor and stakeholders.
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[ ]Knowledge Transfer and Documentation:[ ]All architecture, design, and operational documentation is finalized and handed over to the permanent platform owners.[ ]Final training sessions conducted for developers and operations staff.
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[ ]Lessons Learned and Optimization Backlog:[ ]Held a project retrospective to document what went well and what could be improved.[ ]Created a backlog of future optimization opportunities (e.g., further job consolidation, new performance tuning techniques). This feeds into the continuous improvement of the new platform.