20 years of hacking Postgres with Heikki Linnakangas (cofounder of Neon)

Hunter Leath, CEO of Archil, explains how they’re building a “universal storage engine” that sits between your apps and S3—making an S3 bucket behave like a fast, POSIX-compatible disk for containers, servers, and even Lambda. Along the way, we dig into how their SSD-backed clusters and custom protocol avoid the usual small-file pain and where this approach shines (and where it doesn’t). Follow Hunter: Twitter/X: https://twitter.com/jhleath Archil Twitter/X: https://twitter.com/archildata Archil: https://archil.com/ Follow Aaron: Twitter/X: https://twitter.com/aarondfrancis Database School: https://databaseschool.com Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today) LinkedIn: https://www.linkedin.com/in/aarondfrancis Website: https://aaronfrancis.com - find articles, podcasts, courses, and more. Chapters: 00:00 - Intro: Archil Data and “S3 as a disk” 01:05 - Hunter’s background and the core pitch 02:32 - The real problem: state management (S3 vs block storage) 05:02 - SQLite on S3: what the stack looks like 07:13 - The missing layer: durable SSD-backed clusters 10:14 - Who uses this: unstructured data, CI/CD, Git, agents 12:15 - Small files + Git performance and avoiding S3 request explosion 16:22 - Why they built a new protocol (NFS vs Luster) 20:00 - What gets written to S3: real files in your bucket 22:29 - S3 limits, throttling, and the “keep it on SSD” escape hatch 25:32 - Multi-cloud + R2, and why regions/latency matter 32:10 - Pricing model: “pay only when data is active” 34:41 - Tradeoffs: random reads and ultra-low-latency metal 37:19 - Storage/compute separation and AI/agent-native workflows 43:21 - YC timeline + the marketing challenge of a “universal layer” 47:34 - Single-tenant clusters for enterprises and why it’s hard 50:27 - Where the company is now, hiring, and how to try it (disk.new)

Hammad Bashir, CTO of Chroma, joins the show to break down how modern vector search systems are actually built from local, embedded databases to massively distributed, object-storage-backed architectures. We dig into Chroma’s shared local-to-cloud API, log-structured storage on object stores, hybrid search, and why retrieval-augmented generation (RAG) isn’t going anywhere. Follow Hammad: Twitter/X: https://twitter.com/HammadTime LinkedIn: https://www.linkedin.com/in/hbashir Chroma: https://trychroma.com Follow Aaron: Twitter/X: https://twitter.com/aarondfrancis Database School: https://databaseschool.com Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today) LinkedIn: https://www.linkedin.com/in/aarondfrancis Website: https://aaronfrancis.com - find articles, podcasts, courses, and more. Chapters: 00:00 – Introduction From high-school ASICs to CTO of Chroma 01:04 – Hammad’s background and why vector search stuck 03:01 – Why Chroma has one API for local and distributed systems 05:37 – Local experimentation vs production AI workflows 08:03 – What “unprincipled data” means in machine learning 10:31 – From computer vision to retrieval for LLMs 13:00 – Exploratory data analysis and why looking at data still matters 16:38 – Promoting data from local to Chroma Cloud 19:26 – Why Chroma is built on object storage 20:27 – Write-ahead logs, batching, and durability 26:56 – Compaction, inverted indexes, and storage layout 29:26 – Strong consistency and reading from the log 34:12 – How queries are routed and executed 37:00 – Hybrid search: vectors, full-text, and metadata 41:03 – Chunking, embeddings, and retrieval boundaries 43:22 – Agentic search and letting models drive retrieval 45:01 – Is RAG dead? A grounded explanation 48:24 – Why context windows don’t replace search 56:20 – Context rot and why retrieval reduces confusion 01:00:19 – Faster models and the future of search stacks 01:02:25 – Who Chroma is for and when it’s a great fit 01:04:25 – Hiring, team culture, and where to follow Chroma

In this episode of Database School, Aaron Francis sits down with Jordan Tigani, co-founder and CEO of MotherDuck, to break down what DuckDB is, how MotherDuck hosts it in the cloud, and why analytics workloads are shifting toward embedded databases. They dig into Duck Lake, pricing models, scaling strategies, and what it really takes to build a modern cloud data warehouse. Follow Jordan: Twitter/X: https://twitter.com/jrdntgn LinkedIn: https://www.linkedin.com/in/jordantigani MotherDuck: https://motherduck.com Follow Aaron: Twitter/X: https://twitter.com/aarondfrancis Database School: https://databaseschool.com Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today) LinkedIn: https://www.linkedin.com/in/aarondfrancis Website: https://aaronfrancis.com - find articles, podcasts, courses, and more. Chapters: 00:00 - Introduction 01:44 - What DuckDB is and why embedded analytics matter 04:03 - How MotherDuck hosts DuckDB in the cloud 05:18 - Is MotherDuck like the “Turso for DuckDB”? 07:38 - Isolated analytics per user and scaling to zero 08:51 - The academic origins of DuckDB 10:00 - From SingleStore to founding MotherDuck 12:28 - Getting fired… and funded 12 days later 16:39 - Jordan’s background: Kernel dev, BigQuery, and Product 18:36 - Partnering with DuckDB Labs and avoiding a fork 20:52 - Why MotherDuck targets startups and the long tail 24:22 - Pricing lessons: why $25 was too cheap 28:11 - Ducklings, instance sizing, and compute scaling 34:16 - How MotherDuck separates compute and storage 37:09 - Inside the AWS architecture and differential storage 43:12 - Hybrid execution: joining local and cloud data 45:14 - Analytics vs warehouses vs operational databases 47:41 - Data lakes, Iceberg, and what Duck Lake actually is 53:22 - When Duck Lake makes more sense than DuckDB alone 56:09 - Who switches to MotherDuck and why 58:02 - PG DuckDB and offloading analytics from Postgres 1:00:49 - Who should use MotherDuck and why 1:03:39 - Hiring plans and where to follow Jordan 1:05:01 - Wrap-up

In this episode, Aaron talks with Dennis Magda, author of Just Use Postgres!, about the wide world of modern Postgres, from JSON and full-text search to generative AI, time-series storage, and even message queues. They explore when Postgres should be your go-to tool, when it shouldn’t, and why understanding its breadth helps developers build better systems. Use the code DBSmagda to get 45% off Denis' new book Just Use Postgres! Order Just Use Postgres! Follow Denis: Twitter/X: https://twitter.com/denismagda LinkedIn: https://www.linkedin.com/in/dmagda

In this episode, Gajus Kuizinas, co-founder and CTO of Contra, joins Aaron to talk about building the engineering world you want to live in, from strict runtime-validated SQL with Slonik to creating high-ownership engineering cultures. They dive into developer experience, runtime assertions, SafeQL, and even “Loom-driven development,” a powerful review process that lets teams move fast without breaking things. Follow Gajus: Twitter/X: https://twitter.com/kuizinas Slonk: https://github.com/gajus/slonik Scaling article: https://gajus.medium.com/lessons-learned-scaling-postgresql-database-to-1-2bn-records-month-edc5449b3067 Follow Aaron: Twitter/X: https://twitter.com/aarondfrancis Database School: https://databaseschool.com Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today) LinkedIn: https://www.linkedin.com/in/aarondfrancis Website: https://aaronfrancis.com - find articles, podcasts, courses, and more. Chapters: 00:00 – Introduction 01:03 – Meet Gajus and Contra 01:48 – What Contra does and how it’s different 05:34 – Why Slonik exists & early career origins 07:47 – The early Node.js era and frustrations with ORMs 09:50 – SQL vs abstractions and the case for raw SQL 10:35 – Template tags and the breakthrough idea 12:03 – Strictness, catching errors early & data shape guarantees 13:37 – Runtime type checking, Zod, and performance debates 16:02 – SafeQL and real-time schema linting 17:01 – Synthesizing Slonik’s philosophy 21:29 – Handling drift, static types vs reality 22:52 – Defining schemas per-query & why it matters 27:59 – Integrating runtime types with large test suites 31:00 – Scaling the team and performance tradeoffs 33:41 – Runtime validation cost vs developer productivity 35:21 – Real drift examples from payments & external APIs 38:21 – User roles, data shape differences & edge cases 39:51 – Integration test safety & catching issues pre-deploy 40:52 – Contra’s engineering culture 41:47 – Why traditional PR reviews don’t scale 43:22 – Introducing Loom-Driven Development 45:12 – How looms transformed the review process 52:38 – Using GetDX to measure engineering friction 53:07 – How the team uses AI (Claude, etc.) 56:26 – Closing thoughts on DX and engineering philosophy 58:05 – Contra needs Postgres experts 59:00 – Where to find Gajus