# Datadependency

*/Startups/Datadependency*

## Startup Overview

This system maps data pipeline topology by extracting lineage directly from runtime query execution metadata. Instead of relying on manual tagging or static code analysis, it reads the actual queries executed in production to build an exact dependency graph. Data engineers use this to trace the path of records from source tables to final materialized views without maintaining separate lineage registries.

Legacy data observability and cataloging tools like Monte Carlo and Atlan require heavy API integrations, proprietary agents, or brittle custom parsing scripts to track data flow. This solution replaces those methods with a zero-instrumentation architecture. It parses the native execution logs and query histories already generated by the data infrastructure, identifying structural relationships and transformations automatically.

Operating as a fully query-engine agnostic layer, the platform traces dependencies across heterogeneous data stacks. Whether transformations run in cloud data warehouses, distributed computing frameworks, or transactional databases, it links the cross-engine handoffs into a single map. This gives engineering teams immediate visibility into the downstream impacts of schema changes and broken jobs without deploying any tracking code.

## Startup Founding Hypothesis

**Approach**: that extracts pipeline topology from runtime query execution metadata
**Competitors**:
- [Monte Carlo](/Competitors/Monte_Carlo)
- [Atlan](/Competitors/Atlan)
- [custom parsing scripts](/Competitors/custom_parsing_scripts)
**Differentiator2x2**: both zero-instrumentation and fully query-engine agnostic

## Startup Solution Coordinate

**Solution**: [Query Topology Engine](/Software/Query_Topology_Engine)

## Startup Position2x2

```mermaid
quadrantChart
x-axis Engine Specific --> Engine Agnostic
y-axis Heavy Instrumentation --> Zero Instrumentation
"Monte Carlo": [0.65, 0.35]
"Atlan": [0.75, 0.25]
"custom parsing scripts": [0.15, 0.85]
"Datadependency": [0.90, 0.90]
```

## Startup Customer Journey

```mermaid
flowchart LR; A[Documentation Hub]-->B[Self-Serve CLI Tool]; B-->C[Local Topology Graph]; C-->D[CI-CD Integration]; D-->E[Platform License Contract]; E-->F[Cross-Engine Lineage Map]; F-->G[Agent Tool Catalog];
```

## Startup Proof Points

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**Pilot Goals**:
- 14-Day Single Warehouse Pilot: Connect the system to a single data warehouse's metadata logs to prove the automatic rendering of the initial pipeline topology within two weeks, triggering the money-back guarantee validation.
- 30-Day Enterprise Cross-Engine Proof of Concept: Scope a deployment across two distinct analytical engines and a BI tool to prove accurate cross-engine dependency mapping without touching pre-compiled application code.
**Target Metrics**:
- Target: 100% automated lineage coverage generated solely from query execution logs
- Target: 0 compute overhead added to primary analytical database engines
- Aim: Under 15 minutes to fully map an initial cross-engine data topology
- Target: 0 lines of manual instrumentation code required to achieve baseline pipeline visibility
**Target Case Studies**:
- Mid-Market Data Engineering Team: Moving from manual documentation to automated lineage extraction, targeting the elimination of broken downstream BI dashboards caused by upstream schema changes.
- Enterprise Analytics Platform Group: Targeting the mapping of a complex cross-engine topology to identify unused tables and dependent pipelines prior to executing a major data warehouse migration.
- Fast-Growing Fintech Data Squad: Aiming to integrate CI/CD API checks to automatically halt destructive schema deployments before they reach production by referencing the extracted lineage graph.
**Testimonial Targets**:
- Lead Data Engineer: Seeking confirmation that the system accurately parses complex dynamic SQL executed by dbt or Looker without requiring any read access to underlying table contents.
- VP of Data Infrastructure: Aiming for praise regarding the complete decoupling from primary compute, validating the strict reliance on system metadata tables and scheduled intervals.
- Analytics Engineer: Targeting relief that downstream dependency checks are now fully automated, eliminating the anxiety of deploying complex data model updates.

## Startup Top Risks

**Risks**:
- Severity: existential · Description: Major cloud data warehouses restrict or monetize access to runtime query metadata, breaking the core zero-instrumentation extraction mechanism. · Mitigation Status: unmitigated
- Severity: high · Description: Incumbent data catalogs like Atlan replicate the runtime parsing approach natively within their platforms, neutralizing the primary competitive differentiator. · Mitigation Status: unmitigated
- Severity: high · Description: Parsing millions of runtime queries introduces massive computational overhead, making the extraction process too expensive or slow for enterprise-scale deployments. · Mitigation Status: in-progress
- Severity: moderate · Description: Highly dynamic SQL generated by dbt macros or BI tools obscures actual table references in the execution metadata, leading to broken or incomplete lineage graphs. · Mitigation Status: in-progress

## Startup Competitors

- [Monte Carlo](/Competitors/Monte_Carlo) — Data Observability
- [Atlan](/Competitors/Atlan) — Active Metadata
- [Custom Parsing Scripts](/Competitors/Custom_Parsing_Scripts) — Status Quo
- [Select Star](/Competitors/Select_Star) — Data Lineage
- [Manta](/Competitors/Manta) — Incumbent

## Startup Token Hero

**Genre**: founding-hypothesis
**Rendered**: Every deployment, data engineers risk broken dashboards. Datadependency extracts runtime query metadata so your entire pipeline topology is mapped automatically.
**Mechanism**: spine-derived-v1
**Template Id**: spine-founding-hypothesis
**Vocab Fingerprint**: b6887af17bdbb5ec

## Startup Token Positioning

**Genre**: moore-positioning
**Rendered**: Automated Data Lineage and Observability for data engineers in complex warehouse environments. Unlike Monte Carlo and custom scripts — map cross-engine dependencies with zero code instrumentation.
**Mechanism**: spine-derived-v1
**Template Id**: spine-moore-positioning
**Vocab Fingerprint**: ddf446b1e1c6c5dd

## Startup Token Pitch Deck

**Genre**: pitch-deck
**Rendered**: Problem: Tracing dependencies across dbt and Looker requires maintaining brittle custom parsing scripts or proprietary agents in Monte Carlo.
Solution: Every deployment, data engineers risk broken dashboards. Datadependency extracts runtime query metadata so your entire pipeline topology is mapped automatically.
Customer: data engineers in complex warehouse environments
Unlike: Monte Carlo and custom scripts
**Mechanism**: spine-derived-v1
**Template Id**: spine-pitch-deck
**Vocab Fingerprint**: 398d090441121acb

## Startup Token M E D D P I C C

**Pain**: Tracing dependencies across dbt and Looker requires maintaining brittle custom parsing scripts or proprietary agents in Monte Carlo.
**Metrics**: Target: Your entire data pipeline is mapped in under 15 minutes, giving you instant visibility into every downstream dependency without touching a line of code.
**Rendered**: Pain: Tracing dependencies across dbt and Looker requires maintaining brittle custom parsing scripts or proprietary agents in Monte Carlo.
Economic buyer: Data Platform Engineer
Metrics: Target: Your entire data pipeline is mapped in under 15 minutes, giving you instant visibility into every downstream dependency without touching a line of code.
Competition: Monte Carlo and custom scripts
**Mechanism**: spine-derived-v1
**Competition**: Monte Carlo and custom scripts
**Economic Buyer**: Data Platform Engineer
**Vocab Fingerprint**: 310419075aceb3e8

## Startup Token Cold Email

**Genre**: cold-email
**Rendered**: Subject: Automated Data Lineage and Observability for data engineers in complex warehouse environments

data engineers in complex warehouse environments — Tracing dependencies across dbt and Looker requires maintaining brittle custom parsing scripts or proprietary agents in Monte Carlo. Every deployment, data engineers risk broken dashboards. Datadependency extracts runtime query metadata so your entire pipeline topology is mapped automatically.
**Mechanism**: spine-derived-v1
**Template Id**: spine-cold-email
**Vocab Fingerprint**: c23e613e5e132b51

## Startup Token Agent Spec

**Genre**: ai-agent-spec
**Rendered**: Automated Data Lineage and Observability. Every deployment, data engineers risk broken dashboards. Datadependency extracts runtime query metadata so your entire pipeline topology is mapped automatically. Serves data engineers in complex warehouse environments.
**Mechanism**: spine-derived-v1
**Template Id**: spine-ai-agent-spec
**Vocab Fingerprint**: ee183824c5d16ea2

## Neighborhood

### Entrant startups

- [AI Supply Chain Visibility For Manufacturers](/Opportunities/AI_Supply_Chain_Visibility_For_Manufacturers) — is entrant in · Opportunities

### What it offers

- [Query Topology Engine](/Software/Query_Topology_Engine) — offers · Software

### Composed of

- [Agnostic Parsing API](/Agents/Agnostic_Parsing_API) — composes · Agents
- [Pipeline Topology Service](/Services/Pipeline_Topology_Service) — composes · Services
- [Runtime Execution Worker](/Agents/Runtime_Execution_Worker) — composes · Agents
- [Query Metadata Agent](/Agents/Query_Metadata_Agent) — composes · Agents
- [Query Topology Engine](/Agents/Query_Topology_Engine) — composes · Agents

### Competitors

- [Custom Parsing Scripts](/Competitors/Custom_Parsing_Scripts) — competes with · Competitors
- [Select Star](/Competitors/Select_Star) — competes with · Competitors
- [Manta](/Competitors/Manta) — competes with · Competitors
- [Monte Carlo](/Competitors/Monte_Carlo) — competes with · Competitors
- [Atlan](/Competitors/Atlan) — competes with · Competitors

### Embodies

- [Software](/Theses/Software) — embodies · Theses

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