Explore our collection of 38 in-depth articles tagged with data-engineering on DataEngineer Hub. Each tutorial provides practical, hands-on guidance with real-world examples to help you master data-engineering concepts and best practices in data engineering.
The question came up in a Slack channel for a platform team I was advising: “We want to give our AI coding agent access to the pipeline metadata so it…
The table format question is settled. Apache Iceberg won. Snowflake, Databricks, AWS, Google, and every serious data platform has committed to it. What hasn’t settled — what’s actively being fought…
Your data engineer says the customer data already lives in Snowflake — all of it, clean, modeled, production-ready. Your architect wants to copy it into Salesforce Data Cloud. You’re running…
The 2 a.m. page said the pipeline “succeeded.” The dashboard was green. And the finance team was still staring at yesterday’s numbers, because one task in a forty-task DAG had…
The pitch is intoxicating and mostly true: keep your data in open Apache Iceberg format on your own object storage, let external engines read it, and let Snowflake’s Dynamic Tables…
In March 2026, two days after Snowflake shipped Cortex Code, security researchers at PromptArmor published something that should have changed how every data team thinks about AI agents. They didn’t…
Our CI got slower and more expensive at exactly the same rate our test suite got better, and for a while nobody connected the two. We’d done everything the best-practice…
I once inherited an Airflow repo with 214 DAG files that were, functionally, the same DAG. Each one extracted a table from a source system, loaded it into Snowflake, and…
The Snowflake bill for our CI account had roughly tripled over a quarter, and nobody could point to why. We hadn’t loaded meaningfully more data. Compute was flat. But storage…
The first thing I tried to do in CoCo Desktop was ask it to pull the open tickets for a data pipeline I was debugging. It couldn’t. Not because it…
The bug report said: “The model is broken. It gives a different answer every time I ask the same question.” I’ve gotten some version of this from three different engineers…
The first time a product manager asked me why our “real-time” Snowflake dashboard took four seconds to load on a Monday morning, I didn’t have a good answer. The data…
You built a RAG pipeline. You retrieved relevant documents. You fed them to Claude. You got back a confident, well-structured answer. The answer sounds great. It cites sources. It reads…
For years, the pattern was: Airflow sits in one corner of your infrastructure, dbt runs on a server somewhere else, they pass data between each other via manual credential handoffs…
You read about dbt State. You understood the pitch — skip unchanged models, cut compute, stop paying to rebuild what didn’t change. Then you opened your project and stared at…
Your team has a 200GB Parquet file on S3. Someone suggests running the analysis in Spark. You spin up a four-node cluster, configure executors, tune shuffle partitions, wait three minutes…
Someone on the backend team renamed order_total to order_amount. Clean name. Makes total sense for their domain model. They shipped it on a Thursday afternoon.
Your AI agent isn’t getting dumber over time. It’s getting amnesiac. It forgets a constraint you set ten turns ago, even though it followed it perfectly at turn three. It contradicts…
You’ve probably heard the buzz: dbt’s new Fusion engine is 30x faster. But what nobody says clearly is faster at what, for whom, and does it break your project? The answer is…
Most data engineers I talk to still store everything in Snowflake native format. It’s simple: load data, query data, done. But here’s what nobody’s talking about: if you’re querying that…
Ask ten data engineers what happens when you run a query in Snowflake and most of them will tell you the same thing: the warehouse runs it. SQL goes in,…
A dashboard that ran in four seconds on Monday took nineteen seconds on Tuesday. Same query, same data, same warehouse size. I spent the better part of an hour convinced…
In 2018, a wave of Medium posts declared SQL obsolete. NoSQL was the future. Python would handle everything. Data lakes would make relational thinking irrelevant. The hot take had a…
I evaluated Prefect seriously. Ran it in a staging environment for six weeks. Built three real flows. Had the internal conversation about migrating. And then stayed with Airflow. That was…
TL;DR→ Delta Lake is easier to start with, especially if you’re already on Databricks→ Iceberg wins on engine flexibility — works natively with Spark, Flink, Trino, Snowflake, and more without…
I passed the SnowPro Gen AI certification not too long ago. Within the same week I was back at my desk staring at a broken pipeline that no multiple-choice question…
Every time I demo Snowflake to someone new, zero-copy cloning gets the biggest reaction. You type one line. You get an instant copy of a table — or an entire…
Three practical methods to query Snowflake data in DuckDB — via Iceberg tables, ADBC, or a hybrid architecture — with real cost breakdowns showing 70–90% savings on BI and dev workloads.
I’ve been running dbt in production for a while now. And I’ll be honest — there was a phase where I genuinely believed that if my dbt tests were green,…
I still remember the afternoon I burned four hours debugging a production pipeline — convinced the problem was in the model logic — only to find the real culprit was…
Revolutionary Performance Without Lifting a Finger On October 8, 2025, Snowflake unveiled Snowflake Optima—a groundbreaking optimization engine that fundamentally changes how data warehouses handle…
Introduction to Data Pipelines in Python In today’s data-driven world, creating robust data pipelines solutions is essential for businesses to handle large volumes of information efficiently.
In Part 1 of our guide, we explored Snowflake’s unique architecture, and in Part 2, we learned how to load data. Now comes the most important part: turning that raw…
In Part 1 of our guide, we covered the revolutionary architecture of Snowflake. Now, it’s time to get hands-on. A data platform is only as good as the data within it, so understanding…
Building a powerful data pipeline on AWS is one thing. Building one that doesn’t burn a hole in your company’s budget is another. As data volumes grow, the costs associated…
In the world of data, consistency is king. Manually running scripts to fetch and process data is not just tedious; it’s prone to errors, delays, and gaps in your analytics….
For data engineers, the dream is to build pipelines that are robust, scalable, and cost-effective. For years, this meant managing complex clusters and servers. But with the power of the cloud,…
If you’ve ever inherited a dbt project, you know there are two kinds: the clean, logical, and easy-to-navigate project, and the other kind—a tangled mess of models that makes you…