Exercise project for anyone starting in data engineering startdataengineering.com/pos… #dataengineering #bigdata #ETL #ApacheAirflow #AWS #ApacheSpark

Upserting data in a warehouse is tricky. What happens when it fails partway through? Are you sure you're updating only the right rows? MERGE INTO puts all your insert/update/delete logic in one atomic statement. Full guide 👇 startdataengineering.com/pos… #spark #sql #dataengineer

Building throwaway pipelines for quick wins, then losing days fixing them? The fix is boring but real: data modeling upfront I broke down the design decisions that let you move fast without breaking everything 👇 startdataengineering.com/pos… #datapipeline #datamodel

SQL is the bread and butter of data engineering. Whether you are a seasoned pro or new to data engineering, there is always a way to improve your SQL skills. Check out 8 patterns to uplevel your SQL skills. startdataengineering.com/pos… #dataengineering #sql #datapipeline

The project structure dbt recommends for data modeling is complex and confusing. At almost every company I've worked we've used these 3 layers: 1. raw tables as-is from source 2. tables modeled as facts and dims 3. summary tables I'm working on upgrading my dbt tutorial #dbt

If you are a data engineer or looking to break into Data Engineering, Apache Airflow is a must-know. Check out my post on how to think about orchestration and scheduling with Airflow. startdataengineering.com/pos… #dataengineering #apacheairflow #datapipelines

Adding short video explanations to my posts, let's see how that goes startdataengineering.com/pos… #dataengineering

Stuck preparing for data engineering system design interviews? Use this article to guide you through the prep. startdataengineering.com/pos… #dataengineering #interview #systemdesign

Everyone says you need to know Python for data work. But what does that mean exactly? In this post I cover the key Python concepts every DE needs to know ⬇️ startdataengineering.com/pos… #dataengineering #python #datapipeline #softwareengineering #dataanalysis

Pipeline code can be generated; set yourself apart by understanding how to design pipelines. Learn how to design your pipelines based on given inputs and required outputs. startdataengineering.com/pos… #dataengineering #datawarehouse #sql #etl #datapipeline

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.

Your data warehouse bill is high for one reason. Full table scans. Every query reads everything, whether it needs to or not. Here are 6 storage patterns that fix this 👇 startdataengineering.com/pos…

Large datasets are stored as individual files in S3. Too many small files per dataset make reads expensive! Learn to: Detect it, Compact it or use table properties during insert. Read how to here 👇 startdataengineering.com/pos… #dataengineering #data #apachespark #apacheiceberg

PSA: Understand the concepts and read the docs, before using LLMs Claude sent me on a wild goose chase, hallucinations, complex setup that breaks stuff, etc Wasted a lot of time, only to realize the tool(quarto) I work with already does what I needed

Too many small files in your data lake impact performance. Detect it with Spark UI 1. Go to the stages tab, see the event timeline. 2. Many small tasks (1 task = 1 green chunk) indicate a many-small-files (or partitions) problem. Fix coming tomorrow #dataengineering