Announcing det: - Fast, local-first SQL ETL workspace and pipeline orchestration engine
What if you could build complete, production-grade ETL pipelines in Pure SQL… without Jinja templates, without a database server, and without heavy…
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5 SQL window-function traps that show up in data engineering interviews (with runnable DuckDB examples)
Window functions are the most common SQL topic in data engineering interviews, and most candidates know the syntax. The follow-up questions are wher…
An AI-Assisted Development Methodology, Earned from Real Incidents
This document is a methodology for building software with AI assistance, compiled from the rules that survived real use on a production data platfor…
I Gave My Data Pipelines a Memory: Building PipeMind with Hindsight and Groq
The 2 AM problem Every data team has lived this. A pipeline fails in the middle of the night. Somebody gets paged, stares at a stack trace, and thin…
Normalizing public job-board data with Python
Many companies host their careers page on Greenhouse, Lever or Ashby, and each of these vendors documents a public, read-only JSON API for those boa…
Masa Depan Manajemen Data: Mengenal Konsep Data Mesh yang Revolusioner
Dalam dunia IT yang berkembang pesat, pengelolaan data dalam skala besar seringkali menjadi tantangan tersendiri bagi perusahaan teknologi global. J…
Your Data Engineering Roadmap Is Probably Too Long
A data engineering roadmap crossed my feed recently. It is thoughtful, detailed, and 571 lines long. The associated Reddit discussion had a predicta…
Designing a Production-Ready Data Science Pipeline: From Raw Events to Monitored Models
A model is only one artifact in a production data science system. The harder engineering problem is creating a reliable path from changing source da…
Moving product analytics safely: start with the ingestion boundary
Replacing an analytics system is rarely a dashboard decision. The risky part is the event-ingestion boundary: web, Android, iOS, mini-program, and b…
AI-Enhanced Data Pipelines: Integrating NVIDIA Triton Inference Server into Modern ETL Architectures
-- Executive Summary -- The evolution of Extract, Transform, Load (ETL) processes is undergoing a paradigm shift. Traditional ETL pipelines, designe…
The Hard Truth About Data Engineering Interviews Nobody Tells You
It's 11 PM. You've solved forty LeetCode mediums this month. Your GitHub has a shiny new Airflow project with green checkmarks all the way down. Tom…
Mask, Hash, Tokenize, or Encrypt? Choosing the Right PII Protection for Your Pipeline
In Part 1 you built a map: every column that holds PII, tagged with a tier. Feels like the hard part is over. Then someone in #data-platform asks th…
Azure Synapse Databricks: A Migration Playbook That Survives Production
Azure Synapse → Databricks: A Migration Playbook That Survives Production A field-style guide for Lead / Senior Data Engineers moving Dedicated SQL…
I built a free, interactive SQL practice tool for data engineering interviews
Most data engineering interview prep resources I found online were static — a list of questions with answers underneath. You read the answer, nod, a…
AI-Driven Dynamic Pricing in Hotels: A Data Engineer's Deep Dive
I've spent the better part of a decade building data systems that power pricing decisions in the travel industry, and I can tell you this: dynamic p…
Microsoft Fabric Now Encrypts Spark Temporary Data with Customer-Managed Keys;What It Actually Means
Microsoft Fabric has extended customer-managed key (CMK) encryption to cover temporary data generated during Spark job execution. This is not a mino…
Reflecting on 7-8 Years of Career Growth: Adaptability and Continuous Learning Key to Senior Data Engineer Success
Analytical Insights: The Mechanisms Driving Career Growth in Data Engineering In the rapidly evolving field of data engineering, career progression…
Your Extraction Pipeline Doesn't Need a Smarter Model. It Needs a Schema Contract Test.
Last quarter I inherited a small pipeline that does something increasingly common: pull unstructured text (vendor emails, PDF invoices, changelog en…
Why We Built MicroLeague Sports Vol. 3
Why Sports Data Is Harder Than Most People Think Building believable cross-era simulations turned out to be less about the engine and more about the…
Stop Making Your Database Pretend It Can Search
Databases are optimized for durable storage: transactions, constraints, consistency, recovery. Amazon OpenSearch Service is optimized for a differen…
The Most Underused Prompt in Data Engineering
You've learned not to trust the first answer. So you read it carefully. You spot two problems. You fix them yourself, ship it, and move on. That's a…
Designing a Community Skill for AWS Transform Custom: AWS Glue 5.0 Upgrade Readiness
TL;DR I designed a proposed AWS Transform Custom community skill that prepares Glue 2.0, 3.0, and 4.0 repositories for Glue 5.0. It separates safe m…
Entry-Level Data Engineering Is Gone. Here's the Proof.
I've been on both sides of the data engineering hiring table for years. Interviewed candidates, been the candidate, watched the market shift underne…
Databricks Workflows vs Airflow vs Dagster: Picking an Orchestrator
Every data team eventually asks the same question: what runs our pipelines, on what schedule, with what retry logic, and who gets paged when it fail…
AI Data Lake and Lakehouse
The word "AI" appears in every data platform pitch now. Most of the time, it means nothing — a chatbot bolted onto a dashboard, an LLM that generate…
One Copy, Two Engines: Architecting a Governed Lakehouse Across Fabric and Azure Databricks
For most of the last decade, the honest answer to "should we standardize on Databricks or on the Microsoft data platform?" was: pick one and live wi…
Cloud Bills Took a Decade to Get FinOps. AI Bills Won't Get That Long.
Watch what happened this month. Databricks moved Genie to pay-as-you-go. Every user gets a small free allowance, then the meter runs. And notice wha…
What Is a Semantic Layer? A Practical Guide for Data Engineers
Your data warehouse has a table called orders . It has columns like amount , status , created_at , and customer_id . Now three people ask "What was…
The AI Cheat Tool Your Interview Cannot See
I've been on hiring panels where we spent 45 minutes convinced a candidate was sharp. Articulate answers. Clean code. Solid reasoning. Then in the d…
Privatise your Data Streams with Bring Your Own Cloud (BYOC)
TL;DR Traditional SaaS streaming requires exporting sensitive data to a vendor cloud, creating security risks and egress costs. BYOC reverses this m…