Modules

10 Data Engineering Myths That Are Holding You Back (And How to Beat Them)

Dreaming of a high-paying data engineering job, but stuck on what you “need” to break in? You’ve probably heard a thousand things: you need a computer science degree, years of experience, a never-ending list of certificates… The list goes on. No wonder it feels confusing and overwhelming. I’m Chris Garzon, founder of Data Engineer Academy...

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Mock SQL Interview Tactics: How To Ace Data Engineering Questions With Confidence

Landing your first data engineering job can feel like trying to solve a riddle with missing clues. Everybody talks about practicing SQL interviews, but how do you stand out and prove you know your stuff? Let’s get practical. In this post, I’ll walk you through real mock interview tactics for SQL, using examples straight from...

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How to Really Nail Your Next FAANG SQL Interview (With a Step-by-Step Mock Breakdown)

Looking for the secret sauce to scoring your dream job at places like Google, Amazon, Meta, Netflix, or Apple? Here’s the good news: You don’t need to be a genius to crack those interviews. You just need to show them how you think through problems, especially in SQL interviews. This post walks you through a...

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Data Engineering AMA: Real Questions, Real Advice for Breaking into Data Careers

Thinking about a career in data? Not sure if you should go for data engineering, data science, or something else entirely? If you’ve got more questions than answers, you’re in the right place. Every week, I participate in a live Ask Me Anything session with data-curious professionals and career changers who want direct answers. People join us...

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AWS Data Engineering System Design for Beginners: Your Complete Guide to Building Reliable Data Pipelines

If you’re new to AWS and data engineering, you might feel lost in a sea of buzzwords and cloud icons. Maybe you’re just trying to figure out how all these AWS services fit together, especially when it comes to getting data from business tools into your data warehouse. You’re not alone. System design in AWS...

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What Does a Data Engineer Actually Do All Day? The Real Story Behind the Job

Think working as a data engineer means sitting in front of your laptop slinging code all day? Here’s the real deal: coding is just a slice of the workday. Around 90% of a data engineer’s day involves far more than writing code. From meetings that shape big ideas, to reviewing systems, to the deep focus...

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Automate Multinational Sales Data ETL with AWS Glue and Snowflake Snowpark

Managing and combining sales data from different countries is never as simple as copying files. Every country might send you files in a different format—CSV from India, Parquet from the USA, JSON from France. Not only does this slow things down, but it can also create a mess if you try to analyze it all...

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Don’t Get Replaced By AI: How Data Engineers Can Thrive in an AI-Powered World

Worried about how AI will change your data engineering career? You’re not alone. If you ever ask yourself, “Will AI take my job?” or “Should I even bother learning data engineering now that AI can write code?”… this post is for you. Christopher Garzon, CEO and founder of Data Engineering Academy, has helped over 1,500...

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Find Your Best Data Career: Fastest Paths to $150K+ Based on Where You Start

Sometimes, breaking into the data world feels confusing. There are so many job titles and career paths, you can feel stuck before you even start. Should you become a data analyst, data engineer, analytics engineer, or maybe something else? The good news is you don’t have to guess. Your next step depends on where you...

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5 Python String Manipulation Problems to Boost Your Programming Confidence

String manipulation in Python is an essential skill for anyone looking to boost their programming prowess—especially for data engineers and career changers. Whether you’re parsing data, formatting output, or even cleaning text, getting comfortable with string manipulation can make a big difference in your coding confidence. In this post, we’re going to tackle five practical...

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How to Leverage AI for Automated Data Quality Checks

In today’s data-driven world, maintaining high data quality is critical for businesses of all sizes. Ever wondered how you can automate this process? AI offers powerful solutions for automated data quality checks, significantly reducing manual effort while improving accuracy. With AI tools, you can ensure your data is accurate, complete, and consistent, freeing up time...

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How to Use Machine Learning for Data Pipeline Optimization

With the growing complexity of data ecosystems, optimizing data pipelines is no longer just a nice-to-have; it’s essential. So, how can machine learning help with that? By automating processes and enhancing decision-making, machine learning offers powerful tools that can significantly streamline your data workflows. In this post, we’ll explore practical strategies for applying machine learning...

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AI-Generated Data Models – The Next Big Thing?

AI-generated data models are making headlines, and for good reason. As technology advances, these models are not just a trend; they’re transforming how we think about data engineering. With the potential to streamline processes and enhance decision-making, their appeal spans across various industries. So, are they the next big thing? Absolutely. These models could change...

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Why More Data Engineers Are Learning Rust for Performance Gains

If you’re a data engineer, you’ve probably noticed the buzz around Rust lately. It’s not just hype—Rust has become a go-to language for tackling performance bottlenecks in modern data workflows. Many engineers are making the switch or adding it to their toolkit because its speed, safety, and memory efficiency can handle today’s data-intensive applications without...

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