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How to Manage a Data Warehouse Migration Timeline

Plan a data warehouse migration with a Gantt chart. Track ETL pipelines, schema migration, validation, and cutover in one visual timeline. Free tool, no sign-up.

How to Manage a Data Warehouse Migration Timeline

The Problem: Data Warehouse Migrations Fail When Validation Is Rushed

Moving a data warehouse from one platform to another—from on-premise Teradata to Snowflake, from Redshift to BigQuery, from a legacy Oracle DW to a modern lakehouse—is one of the highest-risk data engineering projects a company can undertake. The warehouse is the source of truth for business intelligence. When migration errors go undetected, executive dashboards silently show wrong numbers. Strategic decisions are made on bad data. The damage can persist for months before anyone notices.

The failure pattern is nearly always the same: the engineering team underestimates ETL rewrite complexity, the validation phase gets compressed when the schedule slips, and the cutover happens before the data has been fully validated. The result is a technically successful migration to a platform that delivers untrustworthy data.

A data warehouse migration timeline on a Gantt chart forces the validation work to be explicitly scheduled—not compressed into a "we'll do it during cutover" afterthought. gantt-chart.io gives data engineering teams a free, shareable project plan that shows every phase from schema design through post-cutover monitoring.

Prerequisites

Before building your data warehouse migration Gantt chart:

  • Source and target platforms: What you're migrating from and to
  • Data volume: Terabytes of data, number of tables, number of ETL jobs
  • ETL inventory: Count of pipelines, their languages (SQL, Python, Spark), and their schedules
  • BI and reporting inventory: Number of dashboards, reports, and downstream consumers of the warehouse
  • Blackout periods: Fiscal year-end, earnings dates, board reporting windows when the warehouse must be reliable
  • Target platform contract: When the new platform license starts (don't pay for two platforms longer than necessary)
  • Team capacity: Data engineers, analytics engineers, and DBA availability
  • Rollback threshold: At what point you would revert to the source platform rather than proceeding

Step-by-Step Instructions

Step 1: Set Up the Migration Timeline

  1. Open gantt-chart.io
  2. Title it "Data Warehouse Migration — [Source] to [Target] — Cutover [Date]"
  3. Set the view to cover from project start through 60 days post-cutover
  4. Use Week view—migration work is tracked at the weekly level
  5. Create task groups for: Assessment, Schema Migration, ETL Migration, Validation, BI Migration, Cutover, and Monitoring

Step 2: Build the Assessment Phase

  1. Create a task group: Assessment & Planning
  2. Add:
  • Source warehouse inventory — catalog all schemas, tables, views, stored procedures
  • ETL pipeline inventory — document all jobs, schedules, dependencies
  • BI report inventory — list all dashboards and their source tables
  • Data quality baseline — document known data quality issues in source
  • Migration complexity scoring — tier tables by complexity (straightforward, needs refactoring, needs redesign)
  • Migration plan approved — milestone
  1. This phase typically takes 2-4 weeks and is consistently underinvested—protect it

Step 3: Add the Schema Migration Track

  1. Create a task group: Schema Migration
  2. Add:
  • DDL conversion scripts — convert CREATE TABLE statements to target syntax
  • Schema compatibility review — data types, constraints, partitioning strategy
  • Target schema deployed to dev — milestone
  • Schema review and approval — milestone
  • Target schema deployed to production — milestone (prerequisite for ETL migration)

Step 4: Build the ETL Migration Track

  1. Create a task group: ETL Migration
  2. Organize by tier:
  • Tier 1 — high complexity ETL rewrite — these take longest, start first
  • Tier 2 — medium complexity ETL migration
  • Tier 3 — simple ETL migration (often automated)
  • ETL unit testing — parallel with migration, each job tested as converted
  • ETL integration testing — test full pipeline chains, not individual jobs
  • ETL schedule validation — verify all jobs run in correct dependency order
  1. Add a Code review task after each tier—ETL migrations are notorious for silent logic errors

Step 5: Build the Validation Track

  1. Create a task group: Data Validation
  2. Add:
  • Row count reconciliation — source vs. target record counts per table
  • Aggregate validation — SUM, AVG, MAX by key dimensions match between systems
  • Null and distribution checks — key fields have expected null rates and distributions
  • Historical data spot checks — manually verify specific records in both systems
  • Business logic validation — finance reconciles revenue numbers, ops reconciles order counts
  • Validation sign-off by business teams — milestone (Finance, Sales, Operations each sign off)
  1. This track should run for at least 3-4 weeks in parallel with BI migration

Step 6: Add the BI Migration Track

  1. Create a task group: BI and Reporting Migration
  2. Add:
  • Dashboard inventory and prioritization — which dashboards are business-critical
  • BI tool connection update — point Tableau, Looker, or Power BI to new warehouse
  • Critical dashboard validation — business users verify dashboards match prior outputs
  • Secondary dashboard migration
  • BI sign-off by analytics stakeholders — milestone

Step 7: Build the Cutover Plan

  1. Create a task group: Cutover
  2. Add:
  • Cutover runbook documented — 4 weeks before cutover
  • Cutover rehearsal — 2 weeks before cutover (dry run in staging)
  • Go/no-go decision — milestone (48 hours before cutover)
  • Source warehouse set to read-only — cutover milestone
  • DNS / connection string update — all applications point to new warehouse
  • Final validation run — first 24 hours post-cutover
  • Cutover complete — milestone
  • Source warehouse decommission — 30-60 days post-cutover (hold for rollback)

Common Mistakes to Avoid

Mistake 1: Compressing Validation When ETL Migration Runs Late

What happens: ETL migration takes 4 weeks longer than planned. The validation phase is compressed from 4 weeks to 10 days to preserve the cutover date. A miscalculated revenue aggregation in a critical ETL job goes undetected. The CFO's revenue dashboard shows incorrect figures for 6 weeks post-cutover.

How to avoid it: In gantt-chart.io, mark validation as the gate before cutover—not a parallel activity that can be compressed. If ETL migration runs late, delay cutover. The cost of a wrong number on the CFO's dashboard is higher than any schedule pressure.

Mistake 2: Not Getting Business Sign-Off on Validation

What happens: The data engineering team validates row counts and aggregates. Numbers match. Cutover proceeds. Finance opens the revenue report on Monday and finds numbers that "don't look right." It turns out a currency conversion logic changed during ETL rewrite. Engineering didn't know about it; Finance did.

How to avoid it: Add explicit Business validation sign-off milestones for Finance, Sales, and Operations. These teams know their numbers. Their sign-off is the final check before cutover, not an optional formality.

Mistake 3: Decommissioning the Source Too Quickly

What happens: Source warehouse is decommissioned 1 week after cutover to save licensing costs. Three weeks later, a reporting issue is discovered that requires comparing current numbers to pre-migration data. There's no source to compare to.

How to avoid it: Keep the source warehouse in read-only mode for 60 days post-cutover. The licensing cost for 60 days is negligible compared to the cost of a rollback without a source to roll back to.

Mistake 4: Migrating All ETL Jobs Simultaneously

What happens: The team migrates all 200 ETL jobs over a 3-week sprint. Testing is cursory because there's too much to test. Integration issues between jobs surface only when the full pipeline runs for the first time—at cutover.

How to avoid it: Migrate in tiers. Test each tier's full pipeline end-to-end before starting the next tier. The Gantt chart tier structure makes this explicit and prevents the "big bang ETL" anti-pattern.

Frequently Asked Questions

Q: How long does a typical data warehouse migration take?

A: A warehouse with 200-500 tables and 50-100 ETL jobs typically takes 4-6 months from assessment to cutover. A larger warehouse (1,000+ tables) can take 9-18 months. The biggest variable is ETL complexity and business availability for validation.

Q: Should we migrate all tables or only active ones?

A: Audit first. Many warehouses have tables that haven't been queried in 18+ months. Migrate only tables with active consumers. Archive the rest in cold storage with documentation. Migrating dead tables wastes engineering time and increases validation burden.

Q: How do we handle incremental loads during the migration period?

A: Run dual-write: write to both the source and target warehouse during the validation period. This keeps the target current so cutover is a DNS switch, not a bulk reload. The Gantt chart should show the dual-write start date as a milestone—it requires additional ETL capacity.

Q: What's the right validation sample size?

A: 100% row count reconciliation for all tables. Aggregate validation (SUM, COUNT, AVG) on all fact tables. Spot-check at least 50 individual records per business-critical table. For revenue-adjacent tables, finance should independently validate the totals, not just rely on engineering's row counts.

Q: When should we consider a phased cutover?

A: When the warehouse serves multiple business units with different data domains. Cut over the supply chain data first (lower risk), validate for 2 weeks, then cut over finance data (higher risk). The Gantt chart shows each domain's cutover as a separate milestone.

Summary: Data Warehouse Migration Timeline That Delivers Trustworthy Data

A structured data warehouse migration Gantt chart ensures validation is treated as the critical path, not an afterthought. Here's the structure:

  1. Assessment that inventories every table, ETL job, and BI report before work begins
  2. Schema migration with target schema approved before ETL work starts
  3. ETL migration in complexity tiers with unit and integration testing per tier
  4. Data validation with business sign-off, not just engineering sign-off
  5. BI migration with critical dashboard validation before cutover
  6. Cutover with documented runbook, rehearsal, and source warehouse held 60 days

gantt-chart.io is free and requires no sign-up. Build your data warehouse migration timeline today and protect the integrity of your business intelligence.