Architecture

Altertable uses a lakehouse architecture with distributed storage and DuckDB-based query workers. The platform uses cache layers and elastic worker capacity to handle mixed analytical workloads.

A visualization layer is built on top of the query engine, providing a user-friendly interface for querying and visualizing data through insights and dashboards.

Humans and AI agents use the same data and query surfaces.

Infrastructure

Altertable operates on high-end servers optimized for analytical workloads, offering:

• High-memory Configurations: Optimized for processing large datasets
• Fast NVMe SSDs: Used for local caching and temporary storage
• Multi-core CPUs: Optimized for parallel query execution

The cache layers are:

• Local SSD Cache: Frequently accessed Parquet files are cached locally on each worker
• Memory Cache: Hot data and query results are stored in memory for sub-second access

Compute resources scale dynamically to meet workload demands:

• Auto-scaling Workers: Additional workers are automatically provisioned during periods of high usage
• Load Distribution: Queries are distributed across available workers
• Elastic Capacity: Worker count increases when workload increases

Learn More

• Analytical Database: Learn how DuckDB workers query your data
• SQL Engine: Understand query processing and optimization
• External Catalogs: Connect external data sources to your lakehouse
• Performance: Deep dive into performance characteristics
• Limits: Understand system quotas and constraints