How I Think About Technical Debt
Refactoring a fragile data pipeline by pushing computation from Python into PostgreSQL. 14 scripts became 3. A deep look at where transformation logic should live.
Read article →The Utilisation Pipeline
A worked example: facility utilisation percentage by month. Bucket to facility-month grain, manufacture the denominator the tables don't contain, then divide safely past the integer-division trap.
Open the notes →The Rolling-Average Pipeline
One hard question that chains everything: SUM(CASE) revenue, zero-fill via a generated calendar, and a frame-clause rolling average, decomposed as a data pipeline and traced with real numbers.
Open the notes →Compute Low, Filter High
One portable model that answers most intermediate SQL problems: when a value is not available where you need it, add a level. Compute it inside, filter or order by it outside.
Open the notes →Aggregation Fundamentals
One mental model and six rules. Everything GROUP BY can throw at you falls out of the bucket picture, with no new syntax beyond what you already know.
Open the notes →SQL Thinking Traces
Working notes we use internally: reason a query into existence by tracing the grain down the execution pipeline. Worked patterns, an error catalog, and blank worksheets.
Open the notes →dbt: Data Pipelines Done Right
Putting the transformation on the database. How dbt changes the way you think about data pipelines.
Read article →The Risk You Carry Until You Can't
On the invisible weight of technical risk and why most teams don't see it until it's too late.
Read article →8 Silent Failures in 2 Workflow Files
Zero warnings. Zero errors. Eight things that would break in production. A deep dive into CI/CD pipeline failures.
Read article →The Problem Was Already Solved
Why the best engineering decisions often come from recognizing that someone has already solved your problem.
Read article →Two Workflow Files, One Missing Step, and a Broken Kitchen Dashboard
How duplicated CI/CD logic drifted, broke production, and why one shared workflow fixed it permanently.
Read article →Case Study: E-Commerce Automation
How an overworked founder saved time, cut errors, and grew revenue with the right automation strategy.
Read article →Building a Data-Driven Culture
How organizations can build better decision-making habits by making data accessible, understandable, and part of everyday work.
Read article →How to Send Emails from Your Gmail Account using Python
Learn how to send emails programmatically using Python, Gmail SMTP, SSL, TLS, and HTML email templates with secure authentication.
Read article →How to Send Messages in Microsoft Teams using Incoming Webhook Adaptive Card
Learn how to configure Microsoft Teams Incoming Webhooks, build Adaptive Card payloads, and automate team notifications using Python.
Read article →Microsoft Teams Webhooks Using PowerShell – Create an Alert System
Learn how to monitor Windows services using PowerShell, send automated Microsoft Teams alerts with Incoming Webhooks, and schedule monitoring with Windows Task Scheduler.
Read article →How to Extract Data from Facebook using Graph API
Learn how to authenticate with Facebook Graph API and extract Page Insights data including reactions, fans, reach, and demographics using Python.
Read article →Data Extraction from Instagram API
A comprehensive guide to extracting Instagram analytics data using the Instagram Graph API, Python, authentication, and insights endpoints.
Read article →How to Get Data from Medium API
Learn how to authenticate with the Medium API, generate OAuth tokens, retrieve publications, and analyze Medium account statistics using Python.
Read article →How to Upload and Download Blobs from Azure Blob Storage Using Python
Learn how to use Python and the Azure Storage SDK to download, transform, and upload blob files using Azure Blob Storage.
Read article →Connect CSV Data in S3 Bucket with Athena Database
A step-by-step guide to querying CSV data in Amazon S3 using AWS Athena and Glue Crawler, including how to avoid common CSV formatting pitfalls.
Read article →How to Connect Python with SQL Server Database Using Pyodbc
A practical guide to connecting Python with Microsoft SQL Server using Pyodbc, including reading and writing DataFrames with Pandas integration.
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