Data Warehouse Migration Project Timeline
The Problem: Data Warehouse Migrations Break Analytics Silently
Data warehouse migrations have a unique failure mode: they look successful until they don't. The data loads, the dashboards render, and then—two weeks after cutover—someone notices that a key metric has been off since the migration. A field mapping was wrong. A data type was truncated. A historical backfill was incomplete.
A data warehouse migration project timeline forces the validation work to be scheduled, not assumed. Every schema change gets a data quality check. Every dashboard gets a post-migration reconciliation. The Gantt chart makes the validation work as explicit and tracked as the migration work itself. gantt-chart.io is free and requires no account.
Prerequisites
- Source warehouse: Legacy system (Teradata, Oracle, on-prem Redshift, etc.)
- Target warehouse: Snowflake, BigQuery, Databricks, AWS Redshift, Azure Synapse?
- Data volume: TB/PB range—determines migration tool and timeline
- ETL/ELT tools: Fivetran, dbt, Airbyte, custom pipelines?
- BI tools: Which dashboards and tools connect to the warehouse? (Tableau, Looker, Power BI)
- Data owners: Who owns each data domain? They must sign off on their data.
Step-by-Step Instructions
Step 1: Set Up the Timeline
- Open gantt-chart.io
- Title the chart:
Data Warehouse Migration - [Source] to [Target] - Plan 16–20 weeks for a mid-sized migration
- Add a
Data Freezemilestone before final cutover - Use Week view
Step 2: Define the Seven Phases
- Discovery & Inventory — catalog all tables, pipelines, reports, and consumers
- Schema Design — design target schema, map data types, handle quirks
- Infrastructure Setup — provision target warehouse, access, networking
- ETL/Pipeline Migration — rebuild or migrate data pipelines
- Historical Data Load — backfill historical data to target
- Validation — reconcile source vs. target, fix discrepancies
- Cutover — redirect consumers, decommission source
Step 3: Discovery & Inventory (Week 1-3)
Table inventory and size assessment— Week 1Column-level data profiling (nulls, types, cardinality)— Week 1-2Pipeline and job dependency mapping— Week 2BI report and dashboard inventory— Week 2Data consumer interviews— Week 2-3Migration complexity scoring per table— Week 3Discovery report complete— Week 3 (milestone)
Step 4: Schema Design (Week 3-6)
Target schema draft— Week 3-4Data type mapping documented— Week 4Handling of legacy quirks (nullability, encoding, precision)— Week 4-5Schema review with data owners— Week 5Schema approved— Week 6 (milestone)
Step 5: Infrastructure Setup (Week 4-6)
Target warehouse provisioned— Week 4Access controls and IAM configured— Week 4-5Network connectivity to source— Week 5Monitoring and query logging enabled— Week 6Cost management controls set— Week 6
Step 6: ETL / Pipeline Migration (Week 6-12)
Migrate pipelines in priority order (most critical data domains first):
Core dimension tables (customers, products, etc.)— Week 6-8High-volume transaction fact tables— Week 7-10Historical aggregates— Week 9-11Specialized domain pipelines— Week 10-12Real-time / streaming pipelines— Week 11-12
For each pipeline, add subtasks:
- Rebuild/migrate pipeline logic
- Test with sample data
- Full load to staging area
Step 7: Historical Data Load (Week 10-14)
Incremental load strategy defined— Week 10Historical backfill for 2+ years of data— Week 10-13Data load progress monitoring— continuousBackfill completeness verified— Week 14
Step 8: Validation (Week 13-16)
Row count reconciliation: source vs. target— Week 13Sum validation on key metrics— Week 13-14Null/missing data comparison— Week 14BI report reconciliation— Week 14-15Data owner sign-off by domain— Week 15-16Validation complete— Week 16 (milestone)
Step 9: Cutover (Week 17-18)
Final delta load— Week 17Source system data freeze— Week 17 (milestone)BI tools reconnected to target warehouse— Week 17Pipeline source switches to target— Week 1724-hour post-cutover monitoring— Week 17-18Source warehouse decommissioned— Week 18Migration complete— Week 18 (milestone)
Validation Is the Real Work
Most teams underestimate validation. A warehouse migration that loads all the data but gets the numbers wrong is worse than no migration—it erodes trust in the entire data platform. Budget as much time for validation as for the migration itself.
Key validation tests:
- Row counts: Every table in source matches target
- Metric reconciliation: KPIs in dashboards match between old and new system for the same date range
- Null profiles: Null rates match source (unexpected nulls mean a mapping error)
- Data freshness: Incremental loads running on schedule with no lag
Common Mistakes
No data freeze before cutover. If source data keeps changing while you're validating, you'll never achieve reconciliation. Define a data freeze point and stick to it.
Migrating BI tools and warehouse simultaneously. Change one variable at a time. Migrate the warehouse first, keeping BI tools pointing at source, then reconnect BI to the new warehouse after validation is complete.
Build your data warehouse migration timeline at gantt-chart.io—free, no account required.