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Stop Building Salesforce Integrations From Scratch

Let me tell you about Marcus. Marcus was on a team I led a few years back. Sharp, motivated, the kind of engineer who actually read documentation before writing code. When the business asked us to get Salesforce data into our warehouse, Marcus volunteered. He’d done API work before. He figured a few weeks, tops. He scoped it carefully. Built a Python service that authenticated via OAuth, pulled Account, Contact, and Opportunity objects through the Bulk API, flattened the nested JSON into relational tables, handled pagination, managed rate limits. Wrote solid tests. Documented everything. The kind of work you’d point to in a code review and say this is how it’s done.

  • Data Engineering
  • Snowflake
  • OpenFlow
  • Salesforce
  • API Integration
  • Schema Evolution
  • Fivetran
  • Data Pipelines
Saturday, April 4, 2026 Read
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Your Data Model Isn't Broken, Part II: The Refactoring Playbook

In [Part I], I made the case that your legacy data model isn’t the disaster it looks like. That the strange WHERE clauses, the bridge tables nobody can explain, and the slowly-changing-dimension-within-a-slowly-changing-dimension aren’t bugs — they’re business rules earned through years of production reality. I argued that big-bang rebuilds fail at alarming rates, that the complexity you’re fighting is mostly essential rather than accidental, and that the impulse to “start from scratch” is driven more by cognitive bias than by engineering judgment.

  • Data Engineering
  • Refactoring
  • Data Warehousing
  • dbt
  • Snowflake
  • Apache Iceberg
  • Write-Audit-Publish
  • Strangler Fig
  • Data Quality
Saturday, March 28, 2026 Read
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Your Data Model Isn't Broken, Part I: Why Refactoring Beats Rebuilding

In the early 2000’s - Netscape’s decision to rewrite their browser from scratch was the single worst strategic mistake a software company could make. At the time, Netscape was winning. They had the dominant browser. They had market share. They had momentum. And then they decided the codebase was too messy, too tangled, too hard to work with — so they threw it all away and started over. Navigator 4.0 became the foundation for a rewrite that would eventually ship as version 6.0. There was no 5.0. Three years of development. No shipping product. And while Netscape’s engineers were busy building their beautiful new browser in a vacuum, Internet Explorer ate their lunch, their dinner, and most of their market share.

  • Data Engineering
  • Refactoring
  • Data Warehousing
  • Technical Debt
  • Snowflake
  • dbt
  • Legacy Systems
  • Data Quality
Saturday, March 14, 2026 Read
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12 Steps to Better Data Engineering

Let me tell you about the moment I stopped trusting architecture diagrams. I was three days into a new role, getting up to speed with the data team. Smart people. Modern stack. On paper, everything looked right. They walked me through a beautiful data platform diagram: clean lines, labelled layers, colour-coded domains. It looked like something you’d see in a data conference. Then I asked a question that changed everything: “Can you rebuild your finance table from scratch right now?”

  • Data Engineering
  • dbt
  • Snowflake
  • GitHub Actions
  • AWS
  • Data Quality
  • CI/CD
  • Data Contracts
Saturday, March 7, 2026 Read
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Write-Audit-Publish with Iceberg Tables in Snowflake

It was a Tuesday afternoon when the analyst pinged me on Microsoft Teams: “Hey, the Total Portfolio numbers just jumped 40% overnight. Did we land a whale?” We hadn’t. What actually happened was more mundane and significantly more painful. A schema change in the source system introduced a currency conversion bug. Our pipeline dutifully loaded the corrupted data into production at 3 AM, the dashboards updated by 6 AM, and the Department Head opened her morning report to numbers that looked like champagne-worthy growth.

  • Apache Iceberg
  • Snowflake
  • WAP Pattern
  • Data Quality
  • SQL
  • Lakehouse
  • Data Pipelines
  • Best Practices
Friday, February 27, 2026 Read