Data Engineering Central Podcast

What happens when you spend decades building a successful career in tech, finally reach Big Tech, and then suddenly get laid off?
In this episode of the Data Engineering Central Podcast, I sit down with AsianDadEnergy to talk about his journey from writing BASIC on an IBM 386 and working with COBOL mainframes to consulting, becoming a chief architect in Big Tech, and eventually losing his job after seven years.
But the layoff wasn’t the end of the story.
Years earlier, the 2008 financial crisis convinced him and his wife to live below their means, eliminate debt, save aggressively, and invest. By the time his Big Tech layoff arrived, those investments had grown enough to cover their living expenses—and he eventually realized he didn’t actually need to find another job.

We also get into AI and the future of software engineering, why junior engineers may be particularly vulnerable, whether AI is taking away some of the struggle that traditionally made programmers better, and why communication and other human skills are becoming survival skills for engineers—not just skills you need to get promoted.
Finally, we talk about separating your identity from your job, building multiple streams of income, creating content on YouTube and Substack, and why there is a lot more to life than what happens in front of a computer screen.
https://www.youtube.com/@AsianDadEnergy
Big Tech. AI. Layoffs. Financial independence. Content creation. And figuring out what actually matters when the career you’ve spent decades building suddenly disappears.
I think this one will resonate with a lot of people working in tech right now.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

In this episode, I sit down with Shehab Amin, co-founder and CEO of LakeSail, to talk about what happens when you decide to rebuild one of data engineering’s most important technologies, Apache Spark, in Rust … sorta.
We dig into Shehab’s journey from writing C++ as a kid to studying computer science at Berkeley, becoming a founder, and eventually building Sail. We talk about why Spark has remained so sticky, what Rust, Apache Arrow, and DataFusion are changing about data infrastructure, and why rebuilding Spark compatibility turned out to be much harder—and more interesting—than expected.
We also get into the increasingly complicated modern data stack: Delta Lake vs. Iceberg, catalogs, streaming vs. batch, agentic coding, and what happens when AI agents start interacting directly with data and compute.

Most importantly, we explore LakeSail’s bigger idea: what if the data pipelines companies already have could become the foundation for their AI pipelines?
A wide-ranging conversation about Spark, Rust, AI, and where data engineering goes from here.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

AI can now write SQL, generate Python, review pull requests, and build entire applications with a few well-crafted prompts. Yet despite all of that progress, data engineering still feels oddly resistant to the full AI revolution. Pipelines break. Data models drift. Production data needs validation. Someone still has to close the loop.
In this episode of the Data Engineering Central Podcast, I sat down with Hugo Lu, founder and CEO of Orchestra, to talk about what “agentic data engineering” actually means beyond the buzzwords. Rather than another conversation about AI replacing engineers, we dug into the infrastructure that’s still missing before autonomous data platforms become reality.
* Hugo shares his unlikely path into data engineering, from investment banking to helping build data systems at Juul, before eventually founding Orchestra.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.

What started as an effort to simplify orchestration has evolved into a broader vision where AI agents don’t just generate code, but can safely execute work, observe the results, validate changes, and iteratively improve pipelines inside secure environments.
* That ability to observe outcomes, what many are calling “closing the loop,” may be the missing ingredient preventing today’s coding agents from becoming truly autonomous.
We also explore why data engineering has not experienced the same AI disruption as traditional software engineering. While AI can produce application code remarkably well, production data systems introduce a completely different set of problems. Branching production data, validating schema changes, testing transformations against realistic datasets, and understanding business semantics all remain difficult challenges that require much more than simply generating code.
The conversation naturally turns toward the future of the profession itself. We discuss whether junior engineers are losing the traditional apprenticeship path, how senior engineers are shifting from writing code to reviewing AI-generated work, and why decades of experience debugging production systems may actually become even more valuable in an AI-first world. Rather than eliminating engineering expertise, AI may simply be changing where that expertise is applied.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Finally, we dive into the rapidly changing data infrastructure landscape. From DuckDB and Polars to serverless compute, Iceberg, semantic layers, AI-native orchestration, and the growing concern over rising LLM token costs, we discuss where the industry appears to be heading and which trends are likely to stick long after today’s hype cycle fades.
If you’ve been wondering what comes after Orchestration, how AI agents will actually manage production data pipelines, or whether data engineering itself is about to undergo its biggest transformation in a decade, I think you’ll enjoy this conversation.
In this episode we discuss
* What “agentic data engineering” actually means.
* Why AI still struggles to fully automate data engineering.
* The importance of closing the loop with production feedback.
* Why orchestration may become the operating system for AI agents.
* The changing role of data engineers in an AI-first world.
* Why junior engineers face a very different career path than previous generations.
* DuckDB, Polars, serverless data platforms, and where modern infrastructure is heading.
* Whether today’s dependence on proprietary LLMs will create tomorrow’s vendor lock-in.
* How Orchestra is building infrastructure for AI-native data platforms.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

When people think about starting a software company, they usually imagine raising venture capital, hiring engineers, and growing a team as quickly as possible.
Michael Drogalis chose a different path.
After helping build technology in the Kafka ecosystem, founding a startup that was ultimately acquired by Confluent, and leading product for stream processing, he walked away from big tech to see if one person could build a serious B2B software company.
* The result became ShadowTraffic, a product that helps engineering teams generate realistic production traffic for testing, demos, and development.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

In this conversation, we talk about much more than streaming systems. We discuss why most engineers underestimate the importance of understanding customers, how AI is changing software development without replacing experienced engineers, what it takes to market technical products, and why writing publicly can become one of the biggest accelerators of your career.
If you’ve ever considered building your own product, becoming a solopreneur, or simply becoming a better engineer, this conversation is packed with practical advice from someone who’s actually done it.
I think this episode has broad appeal beyond data engineering. It’s really about engineering careers, entrepreneurship, and building products that solve real problems, which should make it one of your more accessible interviews.
* Building and selling a Kafka startup
* Life inside Confluent during its rapid growth
* Why Michael left big tech to become a solopreneur
* Building ShadowTraffic from scratch
* Finding customers before writing code
* Why marketing matters more than most engineers think
* Using AI without becoming dependent on it
* The future of software engineering
* Writing online and building an audience
* Advice for engineers who want to start their own business
Thanks for reading Data Engineering Central! This post is public so feel free to share it.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

The creator of Pandas and co-creator of Apache Arrow, Wes McKinney, joins the Data Engineering Central Podcast for an in-depth conversation about how modern data engineering came to exist, where AI is taking software development, and why good engineering still matters more than ever.
We start with Wes’ journey from building GoldenEye fan websites as a teenager to creating Pandas while working at a quantitative hedge fund, and eventually launching Apache Arrow, one of the foundational technologies behind today’s modern data ecosystem. Along the way, we discuss Cloudera, Parquet, DuckDB, DataFusion, Spark, and how the industry evolved from Hadoop to today’s lakehouse architectures.
Thanks for reading Data Engineering Central! This post is public so feel free to share it.

The second half of the conversation dives deep into AI. Wes explains why large language models make experienced engineers more productive but won’t magically replace software engineering, why architecture and good taste are becoming more valuable than writing individual lines of code, and why projects like DuckDB and
* Apache Arrow remains incredibly difficult to recreate with AI alone. We also discuss open-source, local AI models, token costs, multimodal data platforms, and what new engineers should focus on to build long-term careers in software and data.
If you’re a data engineer, software engineer, architect, engineering leader, or simply interested in where AI is taking our industry, this is a conversation you won’t want to miss.
Topics We Cover
* How Pandas was created
* The story behind Apache Arrow
* Why Arrow became the standard for modern data systems
* DuckDB, DataFusion, and the next generation of data tools
* The evolution from Hadoop to lakehouses
* Why AI won’t replace great software engineers
* Architecture vs. coding in the AI era
* Building trustworthy open source software
* The future of data engineering
* Advice for new engineers entering the industry
If you enjoy conversations with the people building the future of data engineering, subscribe for more interviews with the creators of the tools we use every day.
Data Engineering Central is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.



This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe