Tag: data engineering
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When to Hire Your First Data Engineer
When should you hire your first data engineer? Not when you hit a revenue number, but when specific pain points justify the $275K-400K first-year investment. Here’s the decision framework.
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What Hiring Managers Actually Look For in Data Engineers
The Real Story Behind Data Engineering Hiring The job market for data engineers is brutal right now; layoffs, hiring freezes, and 200+ applicants for every decent role. But some candidates still get offers within weeks. What’s the difference? It’s not always the fanciest credentials or the longest list of technologies. It’s something more fundamental, and…
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Data Governance Without the Bureaucracy: A Practical Approach
A lightweight governance framework for startups and mid-size companies that maintains data quality and compliance without killing agility and innovation.
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Build vs. Buy for Data Products: A Decision Framework
A practical decision-making framework covering total cost of ownership, time-to-value, maintenance overhead, and when custom solutions actually make sense.
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Building a Data Team from Scratch: Hiring, Structure, and Culture
A practical guide to hiring data engineers, analysts, and scientists, with insights on team structure, compensation benchmarks, and building an effective data culture.
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A Review of Snowflake Snowpark
After spending several months using Snowflake Snowpark, I’m really impressed with how it enhances the data engineering and data science experience within the Snowflake ecosystem. Essentially, Snowpark allows you to write and execute code directly inside Snowflake using languages like Python, Scala, and Java. This eliminates the need for external processing engines, which reduces complexity…
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Wrangling Data with Databricks Delta Live Tables
Implement the Medallion architecture (Bronze, Silver, Gold) using Databricks Delta Live Tables. Automate data pipelines with declarative transformations and built-in quality checks.
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Book Review: Big Data – Understanding How Data Powers Big Business
Synopsis: A guide for managers to plan for incorporation of Big Data and Analytics in their company. Difficulty: Not really applicable. After reading through “Big Data”, I had to go back and read it again. Not because it was confusing or poorly laid out, but because I had a hard time understanding how author Bill Schmarzo managed…