Database School

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/jhleathArchil Twitter/X:  https://twitter.com/archildataArchil: https://archil.com/Follow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: 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 pitch02:32 - The real problem: state management (S3 vs block storage)05:02 - SQLite on S3: what the stack looks like07:13 - The missing layer: durable SSD-backed clusters10:14 - Who uses this: unstructured data, CI/CD, Git, agents12:15 - Small files + Git performance and avoiding S3 request explosion16:22 - Why they built a new protocol (NFS vs Luster)20:00 - What gets written to S3: real files in your bucket22:29 - S3 limits, throttling, and the “keep it on SSD” escape hatch25:32 - Multi-cloud + R2, and why regions/latency matter32:10 - Pricing model: “pay only when data is active”34:41 - Tradeoffs: random reads and ultra-low-latency metal37:19 - Storage/compute separation and AI/agent-native workflows43:21 - YC timeline + the marketing challenge of a “universal layer”47:34 - Single-tenant clusters for enterprises and why it’s hard50:27 - 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/HammadTimeLinkedIn: https://www.linkedin.com/in/hbashirChroma: https://trychroma.comFollow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Chapters:00:00 – Introduction From high-school ASICs to CTO of Chroma01:04 – Hammad’s background and why vector search stuck03:01 – Why Chroma has one API for local and distributed systems05:37 – Local experimentation vs production AI workflows08:03 – What “unprincipled data” means in machine learning10:31 – From computer vision to retrieval for LLMs13:00 – Exploratory data analysis and why looking at data still matters16:38 – Promoting data from local to Chroma Cloud19:26 – Why Chroma is built on object storage20:27 – Write-ahead logs, batching, and durability26:56 – Compaction, inverted indexes, and storage layout29:26 – Strong consistency and reading from the log34:12 – How queries are routed and executed37:00 – Hybrid search: vectors, full-text, and metadata41:03 – Chunking, embeddings, and retrieval boundaries43:22 – Agentic search and letting models drive retrieval45:01 – Is RAG dead? A grounded explanation48:24 – Why context windows don’t replace search56:20 – Context rot and why retrieval reduces confusion01:00:19 – Faster models and the future of search stacks01:02:25 – Who Chroma is for and when it’s a great fit01:04:25 – 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/jrdntgnLinkedIn: https://www.linkedin.com/in/jordantiganiMotherDuck: https://motherduck.comFollow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Chapters:00:00 - Introduction01:44 - What DuckDB is and why embedded analytics matter04:03 - How MotherDuck hosts DuckDB in the cloud05:18 - Is MotherDuck like the “Turso for DuckDB”?07:38 - Isolated analytics per user and scaling to zero08:51 - The academic origins of DuckDB10:00 - From SingleStore to founding MotherDuck12:28 - Getting fired… and funded 12 days later16:39 - Jordan’s background: Kernel dev, BigQuery, and Product18:36 - Partnering with DuckDB Labs and avoiding a fork20:52 - Why MotherDuck targets startups and the long tail24:22 - Pricing lessons: why $25 was too cheap28:11 - Ducklings, instance sizing, and compute scaling34:16 - How MotherDuck separates compute and storage37:09 - Inside the AWS architecture and differential storage43:12 - Hybrid execution: joining local and cloud data45:14 - Analytics vs warehouses vs operational databases47:41 - Data lakes, Iceberg, and what Duck Lake actually is53:22 - When Duck Lake makes more sense than DuckDB alone56:09 - Who switches to MotherDuck and why58:02 - PG DuckDB and offloading analytics from Postgres1:00:49 - Who should use MotherDuck and why1:03:39 - Hiring plans and where to follow Jordan1:05:01 - 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/denismagdaLinkedIn: https://www.linkedin.com/in/dmagdaFollow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Chapters:00:00 – Welcome01:28 – Dennis’ Background: Java, JVM, and Databases03:20 – Bridging Application Development & Databases04:05 – Moving Down the Stack: How Dennis Entered Databases07:28 – Apache Ignite, Distributed Systems & the Path to Postgres08:02 – Writing Just Use Postgres!: The Origin Story10:26 – Why a Modern Postgres Book Was Needed11:01 – The Spark That Led to the Book Proposal13:06 – Developers Still Don’t Know What Postgres Can Do15:40 – Connecting With Manning & Refining the Book Vision16:38 – What Just Use Postgres! Covers17:40 – The Book’s Core Thesis: The Breadth of Postgres19:50 – Favorite Use Cases & Learning While Writing20:30 – When to Use Postgres for Non-Relational Workloads23:08 – Full Text Search in Postgres Explained29:31 – When Not to Use Postgres (Pragmatism Over Fanaticism)34:01 – Using Postgres as a Message Queue42:09 – When Message Queues Outgrow Postgres48:10 – Postgres for Generative AI (PGVector)55:34 – Dennis’ 14-Month Writing Process01:00:50 – Who the Book Is For01:04:10 – Where to Follow Dennis & Closing Thoughts

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/kuizinasSlonk: https://github.com/gajus/slonikScaling article: https://gajus.medium.com/lessons-learned-scaling-postgresql-database-to-1-2bn-records-month-edc5449b3067Follow Aaron:Twitter/X:  https://twitter.com/aarondfrancis Database School: https://databaseschool.comDatabase School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g  (Subscribe today)LinkedIn: https://www.linkedin.com/in/aarondfrancisWebsite: https://aaronfrancis.com - find articles, podcasts, courses, and more.Chapters:00:00 – Introduction01:03 – Meet Gajus and Contra01:48 – What Contra does and how it’s different05:34 – Why Slonik exists & early career origins07:47 – The early Node.js era and frustrations with ORMs09:50 – SQL vs abstractions and the case for raw SQL10:35 – Template tags and the breakthrough idea12:03 – Strictness, catching errors early & data shape guarantees13:37 – Runtime type checking, Zod, and performance debates16:02 – SafeQL and real-time schema linting17:01 – Synthesizing Slonik’s philosophy21:29 – Handling drift, static types vs reality22:52 – Defining schemas per-query & why it matters27:59 – Integrating runtime types with large test suites31:00 – Scaling the team and performance tradeoffs33:41 – Runtime validation cost vs developer productivity35:21 – Real drift examples from payments & external APIs38:21 – User roles, data shape differences & edge cases39:51 – Integration test safety & catching issues pre-deploy40:52 – Contra’s engineering culture41:47 – Why traditional PR reviews don’t scale43:22 – Introducing Loom-Driven Development45:12 – How looms transformed the review process52:38 – Using GetDX to measure engineering friction53:07 – How the team uses AI (Claude, etc.)56:26 – Closing thoughts on DX and engineering philosophy58:05 – Contra needs Postgres experts59:00 – Where to find Gajus