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.

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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.

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Step-by-Step Instructions

Step 1: Set Up the Timeline

1. Open gantt-chart.io
2. Title the chart: Data Warehouse Migration - [Source] to [Target]
3. Plan 16–20 weeks for a mid-sized migration
4. Add a Data Freeze milestone before final cutover
5. Use Week view

Step 2: Define the Seven Phases

1. Discovery & Inventory — catalog all tables, pipelines, reports, and consumers
2. Schema Design — design target schema, map data types, handle quirks
3. Infrastructure Setup — provision target warehouse, access, networking
4. ETL/Pipeline Migration — rebuild or migrate data pipelines
5. Historical Data Load — backfill historical data to target
6. Validation — reconcile source vs. target, fix discrepancies
7. Cutover — redirect consumers, decommission source

Step 3: Discovery & Inventory (Week 1-3)

1. Table inventory and size assessment — Week 1
2. Column-level data profiling (nulls, types, cardinality) — Week 1-2
3. Pipeline and job dependency mapping — Week 2
4. BI report and dashboard inventory — Week 2
5. Data consumer interviews — Week 2-3
6. Migration complexity scoring per table — Week 3
7. Discovery report complete — Week 3 (milestone)

Step 4: Schema Design (Week 3-6)

1. Target schema draft — Week 3-4
2. Data type mapping documented — Week 4
3. Handling of legacy quirks (nullability, encoding, precision) — Week 4-5
4. Schema review with data owners — Week 5
5. Schema approved — Week 6 (milestone)

Step 5: Infrastructure Setup (Week 4-6)

1. Target warehouse provisioned — Week 4
2. Access controls and IAM configured — Week 4-5
3. Network connectivity to source — Week 5
4. Monitoring and query logging enabled — Week 6
5. Cost management controls set — Week 6

Step 6: ETL / Pipeline Migration (Week 6-12)

Migrate pipelines in priority order (most critical data domains first):

1. Core dimension tables (customers, products, etc.) — Week 6-8
2. High-volume transaction fact tables — Week 7-10
3. Historical aggregates — Week 9-11
4. Specialized domain pipelines — Week 10-12
5. Real-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)

1. Incremental load strategy defined — Week 10
2. Historical backfill for 2+ years of data — Week 10-13
3. Data load progress monitoring — continuous
4. Backfill completeness verified — Week 14

Step 8: Validation (Week 13-16)

1. Row count reconciliation: source vs. target — Week 13
2. Sum validation on key metrics — Week 13-14
3. Null/missing data comparison — Week 14
4. BI report reconciliation — Week 14-15
5. Data owner sign-off by domain — Week 15-16
6. Validation complete — Week 16 (milestone)

Step 9: Cutover (Week 17-18)

1. Final delta load — Week 17
2. Source system data freeze — Week 17 (milestone)
3. BI tools reconnected to target warehouse — Week 17
4. Pipeline source switches to target — Week 17
5. 24-hour post-cutover monitoring — Week 17-18
6. Source warehouse decommissioned — Week 18
7. Migration complete — Week 18 (milestone)

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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

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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.

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Build your data warehouse migration timeline at gantt-chart.io—free, no account required.