Selected work

Private product · Analytics infrastructure

Martian Bee

A private B2B analytics platform that turns business-facing definitions into the ingestion, transformation, caching, and execution required to answer them.

Type
B2B analytics platform
Role
Founder / engineer
Status
Active development
Focus
Execution, data platform, product, AI
RustDataFusionApache ArrowDelta LakePostgreSQLCDCSemantic metricsApplied AI
System map

The operating path.

A deliberately simplified architecture view. The case study below explains where the important guarantees and decisions live.

  1. 01Data sources
  2. 02CDC + ingestion
  3. 03Delta + PostgreSQL
  4. 04Rust execution
  5. 05Metrics + AI
  6. 06Product workflows

The problem

Why this system needed to exist.

Smaller teams often face a bad trade: live inside disconnected SaaS dashboards, or assemble a warehouse and lakehouse stack that requires specialists to operate. Both paths make cross-system questions slower and more expensive than they should be.

The product challenge is not simply to draw another dashboard. It is to translate business-facing definitions into dependable data movement, transformation, caching, execution, and explanation while keeping the underlying system inspectable.

The system

How the pieces work together.

Martian Bee combines a Rust-based execution layer with Apache DataFusion and Arrow, Delta Lake storage, PostgreSQL control metadata, CDC ingestion, semantic metrics, and AI-assisted querying.

Metric and analysis definitions inform the work the platform performs: what data to acquire, how it should be shaped, which results can be cached, and how a query should be executed. The product layer sits on top of that engine rather than replacing it.

01

Definition-driven execution

Business-facing metrics and analyses become inputs to planning, ingestion, transformation, caching, and execution - not a decorative layer after the data work is already done.

02

Product and platform together

The same build spans execution internals, APIs, data architecture, cloud operations, product workflows, and the interface presented to non-technical users.

03

Private by design

The core repository and deeper product architecture remain private while the public description stays specific enough to show the engineering shape without exposing product IP.

Source boundary

Martian Bee is an active private product. The case study intentionally describes the architecture at a safe level and does not publish source code, customer configuration, or unreleased product details.

A system like this on your roadmap?

Let’s make the hard parts explicit.

Discuss the project