Blog
Building Forward: Thoughts on Data, AI, and the Tools We Deserve
From Task Executors to Outcome Owners
How AI is transforming data analyst, data engineer, and data scientist roles from task execution to strategic ownership. Learn how data teams are evolving in 2026 and what skills matter most in the AI era.
Upside-Down Architecture
Most analytics queries scan less than 100MB, yet traditional architectures still assume compute must live in a remote warehouse. We explore a hybrid model where compute moves between our servers and your local machine, powered by DuckDB and open table formats.
Lessons from Search
Real-time analytics systems face the same small-file problem that search engines solved decades ago. DuckLake's new tiered compaction primitives bring battle-tested merge strategies to streaming analytics, making low-latency ingestion sustainable.
Stop Batching Analytics
Why we're forcing analytics through complex batch pipelines when append-only data should work like logs. The warehouse constraint that stopped making sense.
From STDIO to OAuth
How MCP evolved from local stdio to OAuth 2.0 for cloud-scale AI, using Dynamic Client Registration for secure agent access.
Speed Shapes Understanding
Speed isn't just a luxury: it's the difference between insight and inertia. We've been deep in TPC-H benchmarks, tuning our analytical engine for AI agents.
NetQuack 4000x Faster
We rewrote NetQuack DuckDB extension, replacing regex with character parsing. Result: 4000x faster—37 seconds down to 0.012 seconds.
Upstreaming with AI
How we contributed 17 upstream PRs in 90 days—where AI accelerated our workflow, what we learned, and practical tips for open source success with AI assistance.
Rethinking the Lakehouse
Breaking down our storage and query architecture: why we're leaning into Apache Iceberg and why DuckDB is emerging as our real-time query engine of choice.
Under the Hood: Agents
Altertable agents think ahead. Powered by custom lakehouse and MCP, they monitor, investigate, and act on your data autonomously.
What We're Building
Most data platforms wait for questions. Altertable doesn't. We're building an AI-native data OS that turns raw data into continuous insight.
Semantic Layers in 2025
We tested four semantic layer approaches: Looker, Omni, Cube.dev, and dbt MetricFlow. Here's what we learned about each.
From Dashboards to Dialogue
Most dashboards are never opened twice. They clutter stacks, lag behind questions, and bury insight in clicks. We're replacing dashboard sprawl with AI agents.
Relearning the Craft
AI-first development changed everything—prompts are code, green CI means nothing. Here's how we're adapting and relearning.
Behind the Stack
Our tech stack: Rails for backend velocity, Rust for high-performance ingestion, React and Urql for UIs, Iceberg for data lake, Trino for federated queries.
SaaS Pricing Starts with Value
SaaS pricing lags behind modern teams. Why per-seat billing persists, how companies innovate, and value-aligned pricing in the AI era.
Building Trust, Byte by Byte
Trust is our foundation. Drawing on Algolia, Front, and Sorare experience, we build a data platform where security comes standard.
The Data Stack Is Broken
Most data sits idle—trapped behind complexity, bloated budgets, and brittle tooling. The modern data stack promised agility but delivered a slow, siloed maze.
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Our Manifesto
The stack is broken: slow, fragmented, reactive. Our manifesto is a call to wake it up. It's time for data that works like your best teammate: always on, always thinking, always ready to help.
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