A focused course, tailored for you
The Snowflake and dbt Data Migration Playbook
A migration playbook for moving an enterprise data estate to Snowflake with dbt, written for the data engineer who will be on the hook when the post-migration retro happens.
Enterprise data migrations to Snowflake with dbt arrive with a 12-month plan and a 30% chance of a rewrite year. The course delivers the playbook that retires the rewrite-year risk before the first model is materialised.
$199 one-time
Tailored to your situation. Access within 24 hours. 30-day money-back.
Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.
Why this course
An enterprise data migration to Snowflake with dbt looks tidy on the kickoff slide and gets messy by month four. The legacy stack (Teradata, Oracle Exadata, on-premise Hadoop, SQL Server, or a mix) carries the customer's actual business logic and the business logic has been edited by twelve different teams over fifteen years. The dbt-based rewrite restates the logic in a clean model layer, and the first wave of stakeholders signs off on the prototype. The second wave finds the gaps. The post-migration retro finds the rewrite year.
The course works through the migration playbook that retires the rewrite-year risk before the first model is materialised. The discovery pattern that surfaces the actual business logic before the rewrite begins. The dbt project structure that survives multi-team contribution at scale. The data-quality framework that prevents silent-drift between legacy stack and Snowflake stack during the migration window. The cutover pattern that holds the line on parallel-run cost. The stakeholder-management pattern across the second-wave sign-off. Twelve modules with deliverables. Plus a hand-built playbook for your specific migration shape.
The 12 modules
Module 1. The 2026 enterprise migration landscape
Walkthrough of the 2026 enterprise migration landscape. The legacy stacks driving migration demand. The Snowflake migration partner landscape. The dbt market position. The competitive landscape against Databricks migrations. The strategic decisions a customer's data leader faces when planning a 12 to 24-month Snowflake migration with dbt as the transformation layer. The financial and political dynamics that drive scope.
Module 2. Discovery: surfacing actual business logic
Build the discovery pattern. The interview script for the long-tenured analyst who owns the legacy business logic. The query-log mining pattern that finds the actually-used transformations. The lineage reconstruction pattern. The stakeholder map that surfaces the second-wave sign-off contributors. Plus the discovery output that the dbt project structure depends on. The discovery cadence over a six-week pre-migration window.
Module 3. dbt project structure for multi-team scale
Build the dbt project structure. The folder layout. The model naming convention. The materialisation strategy. The package and dependency strategy. The CI/CD integration. The data contracts pattern. The integration with the customer's existing data catalog. Plus the worked example for a single-team starter project and a ten-team production project structure.
Module 4. Staging layer pattern
Build the staging layer pattern. The 1-to-1 mapping from source to staging model. The data-type normalisation. The column-rename framework that handles legacy short-name conventions. The cleansing pattern. The freshness-test pattern. The integration with the source-system metadata. Plus the worked example for a customer with twenty source systems and the staging-layer build sequence over the first eight migration weeks.
Module 5. Intermediate and mart layer
Build the intermediate and mart layer. The business-logic translation pattern. The dimensional-modelling pattern where appropriate. The wide-table pattern where appropriate. The semantic-layer integration. The integration with the customer's existing BI tools (Tableau, Power BI, Looker). Plus the worked example for the customer's first three mart deliveries and the validation pattern for each.
Module 6. Data quality and silent-drift prevention
Build the data quality framework. The dbt-test pattern. The Great Expectations integration. The Elementary integration. The reconciliation pattern between legacy stack output and Snowflake stack output during the parallel-run window. The silent-drift detection pattern that catches the slow divergence before it reaches a stakeholder. Plus the data-quality dashboard the customer sponsor reads weekly.
Module 7. Performance and cost
Build the performance and cost framework. The warehouse-sizing pattern. The query-profile analysis pattern. The materialisation-strategy decisions for high-cost models. The clustering-key strategy. The result-cache strategy. The cost-attribution pattern across teams and projects. Plus the worked example for the customer's typical query mix and the cost target over the first three months post-cutover.
Module 8. Cutover pattern
Build the cutover pattern. The parallel-run window design. The reconciliation cadence. The user-cohort cutover pattern. The rollback pattern. The communication cadence with stakeholders during cutover. The integration with the customer's existing release-management cadence. Plus the worked example for a phased cutover over four months and a big-bang cutover over one weekend.
Module 9. Stakeholder management for second-wave sign-off
Build the stakeholder-management pattern. The first-wave kickoff stakeholder. The second-wave verification stakeholder. The post-cutover support stakeholder. The escalation framework for stakeholder disputes. The renegotiation framework for scope. The integration with the customer's existing data-governance committee. Plus the talking points for the inevitable second-wave gap conversation that lands in month four.
Module 10. Documentation and handover
Build the documentation and handover framework. The model-documentation pattern in dbt. The data-catalog integration. The runbook pattern for the customer's data engineering team. The on-call rotation pattern. The training material for the customer's existing analyst team. Plus the worked example for the handover package that the customer's data leader signs at engagement close.
Module 11. Post-cutover sustainment
Build the post-cutover sustainment framework. The first-month support pattern. The first-quarter optimisation pattern. The customer's transition from external support to internal sustainment. The continuous-improvement pattern. The integration with the customer's existing platform team. Plus the worked example for a sustainment-engagement structure that converts the migration into an ongoing programme.
Module 12. Your 10-week build plan
Week by week. Weeks 1-2: landscape and discovery pattern. Weeks 3-4: dbt project structure and staging layer. Weeks 5-6: intermediate and mart layer, data quality framework. Weeks 7-8: performance and cost, cutover pattern. Weeks 9-10: stakeholder management, documentation, post-cutover sustainment. Deliverable: a Snowflake-and-dbt migration playbook ready for the next customer engagement.
How this addresses your situation
Specific modules that map to what you said you are dealing with.
You need to find the real business logic → Module 2.
Multi-team project structure → Module 3.
Source-to-staging design → Module 4.
Marts → Module 5.
Silent drift risk → Module 6.
Cost too high → Module 7.
Cutover → Module 8.
Stakeholder disputes → Module 9.
Handover → Module 10.
Who it is for
For Snowflake data engineers leading migrations, dbt practice leads, principal consultants at SI firms running Snowflake migrations, and senior data engineers at the customer side of an active Snowflake migration.
Who this is NOT for. Pure non-Snowflake practitioners. Practitioners with no migration experience. Pure non-data roles.
How it arrives
Text-based course via LMS, plus downloadable templates and worked examples and the hand-built playbook.
Time investment. Roughly 18 hours of reading and 60 to 120 hours of build effort across the 10-week plan.
Why $199 is the right number
External migration consultants charge from 200,000 to 1,500,000 USD for an enterprise Snowflake-and-dbt programme build. 199 USD buys the focused playbook and the implementation document for your migration shape.
FAQ
Will this work for a Teradata migration?
Yes. Modules 2 and 4 cover Teradata-specific patterns.
What if the customer is Oracle Exadata?
Modules 2 and 4 cover Exadata-specific patterns.
Does this cover Coalesce as an alternative to dbt?
The framework adapts. The dbt-specific modules can be reframed for Coalesce on request.
What is in the implementation playbook for me specifically?
Discovery pattern tuned to your customer's legacy stack, project structure matched to your team scale, cutover pattern shaped for your customer's risk appetite.
30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.
