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

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 – Welcome
01:28 – Dennis’ Background: Java, JVM, and Databases
03:20 – Bridging Application Development & Databases
04:05 – Moving Down the Stack: How Dennis Entered Databases
07:28 – Apache Ignite, Distributed Systems & the Path to Postgres
08:02 – Writing Just Use Postgres!: The Origin Story
10:26 – Why a Modern Postgres Book Was Needed
11:01 – The Spark That Led to the Book Proposal
13:06 – Developers Still Don’t Know What Postgres Can Do
15:40 – Connecting With Manning & Refining the Book Vision
16:38 – What Just Use Postgres! Covers
17:40 – The Book’s Core Thesis: The Breadth of Postgres
19:50 – Favorite Use Cases & Learning While Writing
20:30 – When to Use Postgres for Non-Relational Workloads
23:08 – Full Text Search in Postgres Explained
29:31 – When Not to Use Postgres (Pragmatism Over Fanaticism)
34:01 – Using Postgres as a Message Queue
42:09 – When Message Queues Outgrow Postgres
48:10 – Postgres for Generative AI (PGVector)
55:34 – Dennis’ 14-Month Writing Process
01:00:50 – Who the Book Is For
01:04:10 – 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/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

In this episode, Aaron Francis talks with Simon Eskildsen, co-founder and CEO of TurboPuffer, about building a high-performance search engine and database that runs entirely on object storage. They dive deep on Simon's time as an engineer at Shopify, database design trade-offs, and how TurboPuffer powers modern AI workloads like Cursor and Notion.

Follow Simon:
Twitter: https://twitter.com/Sirupsen
LinkedIn: https://ca.linkedin.com/in/sirupsen
Turbopuffer: https://turbopuffer.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
01:11 - Simon’s background and time at Shopify
03:01 - The Rails glory days and early developer experiences
04:55 - From PHP to Rails and joining Shopify
06:14 - The viral blog post that led to Shopify
09:03 - Discovering engineering talent through GitHub
10:06 - Scaling Shopify’s infrastructure to millions of requests per second
12:47 - Lessons from hypergrowth and burnout
14:46 - Life after Shopify and “angel engineering”
16:31 - The Readwise problem and discovering vector embeddings
18:22 - The high cost of vector databases and napkin math
19:14 - Building TurboPuffer on object storage
21:20 - Landing Cursor as the first big customer
23:00 - What TurboPuffer actually is
25:26 - Why object storage now works for databases
28:37 - How TurboPuffer stores and retrieves data
31:06 - What’s inside those S3 files
33:02 - Explaining vectors and embeddings
35:55 - How TurboPuffer v1 handled search
38:00 - Transitioning from search engine to database
44:09 - How Turbopuffer v2 and v3 improved performance
47:00 - Smart caching and architecture optimizations
49:04 - Trade-offs: high write latency and cold queries
51:03 - Cache warming and primitives
52:25 - Comparing object storage providers (AWS, GCP, Azure)
55:02 - Building a multi-cloud S3-compatible client
57:11 - Who TurboPuffer serves and the scale it runs at
59:31 - Connecting data to AI and the global vision
1:00:15 - Company size, scale, and hiring
1:01:36 - Roadmap and what’s next for TurboPuffer
1:03:10 - Why you should (or shouldn’t) use TurboPuffer
1:05:15 - Closing thoughts and where to find Simon

Aaron talks with Ovais Tariq, co-founder and CEO of Tigris Data and former Uber engineer who helped scale one of the world’s largest distributed systems. They discuss Uber’s hyperscale infrastructure, what it takes to build an S3-compatible object store from scratch, and how distributed storage is evolving for the AI era.

Follow Ovais:
Twitter: https://twitter.com/ovaistariq
LinkedIn: https://www.linkedin.com/in/ovaistariq
Tigris: https://www.tigrisdata.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 and overview of the episode
01:35 - Ovais’s background and introduction to Tigris
03:00 - Building distributed databases and infrastructure at Uber
06:00 - Uber’s in-house philosophy and massive data scale
09:00 - Hardware, power density, and talking to chip manufacturers
12:00 - Learning curve of scaling hardware and data centers
14:00 - The Halloween outage and lessons from Cassandra
16:00 - Building data centers across the world for Uber
17:00 - Founding Tigris and the vision for global storage
18:45 - How Tigris differs from AWS S3
20:00 - The architecture of Tigris: caching, metadata, and replication
32:00 - Why Tigris uses FoundationDB and its reliability
36:00 - Managing global and regional metadata
38:00 - How Tigris dynamically moves and caches data
41:30 - Building their own data centers and backbone
43:45 - Specialized storage for AI workloads
46:00 - Small file optimization and real-world use cases
49:00 - Snapshots, forking, and agentic AI workflows
51:00 - How AI transformed Tigris’s customer base
54:00 - Partnership with Fly.io and the distributed cloud ecosystem
57:00 - Growth, customers, and focus on media and AI companies
59:00 - What’s next for Tigris: distributed file system plans
1:01:00 - Technical challenges and building trust in durability
1:03:00 - Call to action: try Tigris and upcoming snapshot feature
1:05:00 - Advice for engineers leaving big companies to start something new
1:06:30 - Where to find Ovais online and closing remarks

In this episode of Database School, Aaron talks with Preston Thorpe, a senior engineer at Turso who is currently incarcerated, about his incredible journey from prison to rewriting SQLite in Rust. They dive deep into concurrent writes, MVCC, and the challenges of building a new database from scratch while discussing redemption, resilience, and raw technical brilliance.

Follow Preston and Turso:
LinkedIn: https://www.linkedin.com/in/PThorpe92
Preston's Blog: https://pthorpe92.dev
GitHub: https://github.com/PThorpe92
Turso: https://turso.tech

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 and Preston’s story
02:13 - How Preston learned programming in prison
06:06 - Making his parents proud and turning life around
09:01 - Getting his first job at Unlock Labs
10:47 - Discovering Turso and contributing to open source
12:53 - From contributor to senior engineer at Turso
22:27 - What Preston works on inside Turso
24:00 - Challenges of rewriting SQLite in Rust
26:00 - Why concurrent writes matter
27:57 - How Turso implements concurrent writes
35:02 - Maintaining SQLite compatibility
37:03 - MVCC explained simply
43:40 - How Turso handles MVCC and logging
46:03 - Open source contributions and performance work
46:23 - Implementing live materialized views
50:55 - The DBSP paper and incremental computation
52:55 - Sync and offline capabilities in Turso
56:45 - Change data capture and future possibilities
1:02:01 - Implementing foreign keys and fuzz testing
1:06:02 - Rebuilding SQLite’s virtual machine
1:08:10 - The quirks of SQLite’s codebase
1:10:47 - Preston’s upcoming release and what’s next
1:14:02 - Gratitude, reflection, and closing thoughts