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Step 07 of 9 1-3 quarters· advanced

Step 7: Continue Expanding Domains

Build out remaining domains, refining patterns. Common slicing: by business domain, by source system, by criticality, or by region.

What you're doing in this step

Domain by domain — Sales, then Inventory, then HR, then Finance, etc. Each gets faster than the last as the team builds expertise. Track % of source data on platform (Bronze coverage), % of business domains modeled (Silver/Gold coverage), % of legacy reports migrated, # of pipelines in production, DQ pass rate trends.

Recommended prompts

Use one of these to do the work in your IDE

Open the template to read it in full. Click Copy prompt to grab it (with your stack values pre-filled where they apply) — then paste into Claude Code, Cursor, or wherever you build.

Primary recommendation reference document; 1 day for initial setup

PySpark Transformation Standards

Standards for PySpark transformations in Bronze/Silver/Gold pipelines: idempotency, partitioning, Delta Lake patterns, and code organization.

azurepysparkfabricdatabricks
View template
Template· Template 1-2 days per dimension

Slowly Changing Dimensions (SCD) Implementation in PySpark

Implement SCD Type 1, 2, 3, and 6 patterns in PySpark with Delta Lake MERGE — for dimensional modeling in modern data platforms.

Use this when: A new domain has its own dimensional models with SCD requirements

azurepysparkdelta-lakefabric
View template
Template· Template 1-2 days for initial framework design

ADF / Fabric Pipeline Design Patterns

Design patterns for Azure Data Factory and Microsoft Fabric pipelines: metadata-driven, parameterized, idempotent, and observable.

Use this when: Refining the pipeline framework as new patterns emerge

azureadffabric
View template
Template· Template 1-2 days for initial setup

Data Quality Test Suite

Generate comprehensive data quality tests for ETL pipelines: schema validation, freshness checks, null/duplicate/range checks, and business invariants.

Use this when: Expanding DQ coverage as new domains come online

azurepysparkfabric
View template
Recommended skills

Drop these into Claude Code for this phase

Skills auto-trigger on the right kind of request. Install once; they apply to every prompt that fits.

Skill· Skill 5 min setup

Spec-Driven Builder Skill

Tool-neutral skill that walks developers through PRD → stories → schema → API → tests for any new feature, producing real artifacts at each step. The methodology is identical on every supported tool.

claude-codecopilotcursor
Skill· Skill 5 min setup

Migration Planner Skill

Flagship migration skill that walks Claude Code through audit → strategy → slicing → cutover for any legacy system migration.

claude-code
Skill· Skill 5 min setup

Test Generator Skill

Claude Code skill that picks the right test type (unit/integration/E2E) based on context and applies Evoke's testing patterns automatically.

claude-code
Skill· Skill 5 min setup

Code Reviewer Skill

Claude Code skill that performs comprehensive code review on PRs and diffs, prioritized by severity with concrete fixes.

claude-code
Recommended MCP configs

Wire these tools into Claude Code first

MCP servers give Claude Code direct access to external systems (Jira, browsers, databases). Configure once.

MCP config· MCP config 10 min setup

Azure DevOps MCP for Evoke

Pre-configured Azure DevOps MCP server for Claude Code — work items, repos, PRs, and pipelines from chat.

claude-codemcp
MCP config· MCP config 10 min setup

GitHub MCP for Evoke

Pre-configured GitHub MCP server for Claude Code — issues, PRs, code search, and Actions from chat.

claude-codemcp
MCP config· MCP config 5 min setup

Filesystem MCP for Evoke

Pre-configured filesystem MCP server for Claude Code — safe, scoped read/write access to project files.

claude-codemcp
When you're done

Verify these in your own work before moving on

This is a checklist for you to mentally tick off in your repo and IDE — the site doesn't track it, you do.

  • All in-scope domains modeled in the platform
  • All in-scope reports migrated
  • DQ coverage acceptable (every Silver+ table has tests)
  • Lineage tracked for all pipelines
  • Documentation complete
Common pitfalls

What goes wrong at this step

  • Long tail of low-value data — migration takes 50% of time on last 10% of value
  • Burnout — multi-quarter projects need rotation and small wins
  • Scope creep — "while we're modernizing, let's also add ML / streaming..."
  • Pattern divergence — without governance, each domain reinvents patterns. Maintain code review and shared library
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