Architecture Articles
Deep dives on modern data architecture: Lakehouse patterns, distributed query engines (DuckDB, Trino), and building fast analytical databases.
Stack Drift
Why mature data stacks keep accumulating copy layers, and how direct relational federation gives dbt a simpler path over live operational sources.
Know What a Query Reads Before It Runs
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.
Let Agents Render the Platform
Charts, queries, and interactive UI now render inside AI agents through MCP Apps—the same components as altertable.ai.
Memory Is Not a Database
Intelligence without memory is nothing. The right model is memory that lives, forgets, and knows where it belongs.
Pruning Top-N Queries
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.
Upside-Down Architecture
Most analytics queries scan <100MB. We explore a hybrid architecture where compute moves between servers and your local machine, powered by DuckDB and DuckLake.
Lessons from Search
Real-time analytics faces the small-file problem search engines solved. DuckLake's tiered compaction brings those merge strategies to streaming analytics.
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.
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.
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.
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.