Architecture
Altertable implements a Lakehouse architecture, featuring a distributed storage layer and a query engine. This architecture is designed to be performant and scalable, featuring a multi-tier caching strategy and dynamic scaling of compute resources.
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.
The platform is designed to be used by both humans and AI agents, providing a unified interface for querying and visualizing data.
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
Our multi-tier caching strategy ensures optimal performance:
- 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 intelligently distributed across available workers
- Elastic Capacity: Seamlessly scales from single queries to thousands of concurrent users
Learn More
- Analytical Database: Learn how DuckDB workers query your data
- SQL Engine: Understand query processing and optimization
- Connections: Connect external data sources
- Performance: Deep dive into performance characteristics
- Limits: Understand system quotas and constraints