The AI-Native Data Lakehouse
A lakehouse built for 1000x more questions, simplifying fragmented data stacks with one foundation for warehouse-grade performance, storage economics, live federation, and agent-ready context.
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Building Forward: Thoughts on Data, AI, and the Tools We Deserve
Why mature data stacks keep accumulating copy layers, and how direct relational federation gives dbt a simpler path over live operational sources.
Row estimates only hint at what an agent will read. Bytes pin it down. Our explain returns them from catalog metadata, before the engine touches a file.
Agents shouldn't get static screenshots. We ship the same interactive UIs to our product and to any MCP host through MCP Apps.
Charts, queries, and interactive UI now render inside AI agents through MCP Apps—the same components as altertable.ai.
Intelligence without memory is nothing. The right model is memory that lives, forgets, and knows where it belongs.
Most data platforms ask you to send the data to them. Altertable Workers flip that model; the runtime moves to where the data already lives.
There is no single “winning” lakehouse table format in 2026. What has emerged instead is a more interesting split.
Agents fail when they cannot retrieve the right data slice before writing SQL—not because they cannot generate queries.
AI-native products generate a new kind of infrastructure problem. Here's how to build the event backbone for your AI system.
At 1 billion rows, every shortcut comes back to collect interest. Here's how we achieved sub-second queries with near-realtime ingestion.
A deep dive into DuckLake PR #668 and how Top-N dynamic filter pruning turns ORDER BY + LIMIT from full scans into metadata-driven execution.
AI is transforming data roles from task execution to strategic ownership. Learn how data teams are evolving in 2026 and what skills matter most in the AI era.
Most analytics queries scan <100MB. We explore a hybrid architecture where compute moves between servers and your local machine, powered by DuckDB and DuckLake.
Real-time analytics faces the small-file problem search engines solved. DuckLake's tiered compaction brings those merge strategies to streaming 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.
How MCP evolved from local stdio to OAuth 2.0 for cloud-scale AI, using Dynamic Client Registration for secure agent access.
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.
We rewrote NetQuack DuckDB extension, replacing regex with character parsing. Result: 4000x faster—37 seconds down to 0.012 seconds.
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.
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.
Altertable agents think ahead. Powered by custom lakehouse and MCP, they monitor, investigate, and act on your data autonomously.
Most data platforms wait for questions. Altertable doesn't. We're building an AI-native data OS that turns raw data into continuous insight.
We tested four semantic layer approaches: Looker, Omni, Cube.dev, and dbt MetricFlow. Here's what we learned about each.
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.
AI-first development changed everything—prompts are code, green CI means nothing. Here's how we're adapting and relearning.
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 lags behind modern teams. Why per-seat billing persists, how companies innovate, and value-aligned pricing in the AI era.
Trust is our foundation. Drawing on Algolia, Front, and Sorare experience, we build a data platform where security comes standard.
Most data sits idle—trapped behind complexity, bloated budgets, and brittle tooling. The modern data stack promised agility but delivered a slow, siloed maze.
