QuantFlow DataInfra
Metadata-driven data infrastructure for quantitative finance
🔗 Where It Fits
📋 Overview
DataInfra is the engine-agnostic, metadata-driven data infrastructure layer of QuantFlow. It provides a unified system for ingesting market data from multiple sources, normalizing it into a QuantFlow Market Data Model, generating production-ready dbt pipelines, and enforcing data quality controls.
Without a Market Data Model, each dataset requires custom transformation logic. Raw tick data, order book snapshots, and reference data arrive in incompatible schemas. DataInfra eliminates this fragmentation by enforcing a unified schema across all sources — ensuring every downstream component operates on consistent, validated inputs.
Terminology: the QuantFlow Market Data Model is QuantFlow's own normalised market-data schema, abbreviated cdm in code and table names. It is unrelated to the FINOS Common Domain Model, which QuantFlow does not use.
📐 Metadata Module
The central metadata registry that defines and governs all data assets:
- QuantFlow Market Data Model — standardized financial entities (trade_tick, lob_incremental, lob_snapshot, etc.) defined declaratively in YAML
- Schema registry — column types, partitioning, clustering, and unique keys per entity
- Lineage tracking — source-to-destination field-level lineage
- Pydantic validation — all metadata validated at project load time, preventing config drift
One metadata module. All engines. No schema duplication across environments.
🔌 Feed Providers
External data sources configured declaratively with field mappings and quality tests:
- Databento Reader — Historical market data via Databento API
- HTTP Reader — REST APIs with retry, pagination, rate limiting
- Field Mappings — Raw columns → Market Data Model columns via QFSQL expressions
- Quality Tests — Uniqueness, null checks, range validation per field
Providers are configured once in YAML. New venues or data sources are a configuration change, not a code change.
📥 Ingestion Pipeline
| Stage | What It Does | Options |
|---|---|---|
| Reader | Download raw data from external source | DatabentoReader, HTTPReader |
| Processor | Transform and validate | Decompressor (gzip/snappy/zstd) |
| Writer | Persist to target engine | DuckDB, OpenLakehouse, BigQuery, Snowflake, Databricks |
🏗️ dbt Transformation Pipeline
| Layer | What It Does |
|---|---|
| Staging | Raw source → typed, validated staging tables with field-level tests |
| Intermediate | Cross-source joins, deduplication, enrichment, business logic |
| Mart | Analysis-ready tables for feature computation and downstream consumption |
Auto-generated from Market Data Model definitions. Follows dbt best practices. Zero manual SQL.
🔌 Engine Agnostic
OpenLakehouse
DolphinDB
BigQuery
Snowflake
Databricks
DuckDB✅ Data Quality
Four-layer validation ensures data integrity at every stage, powered by Elementary for dbt-native monitoring and anomaly detection:
- Schema Validation — Pydantic models enforce type safety at parse time
- Field-level Tests — Per-column uniqueness, null checks, range constraints
- Cross-table Integrity — Referential consistency across Market Data Model entities
- Business Rules — Domain-specific assertions (e.g., bid ≤ ask, positive volume)
Test failures are reported with row-level diagnostics. Configurable severity: warn, error, or abort. Elementary provides dashboards, alerts, and lineage-aware monitoring out of the box.
📚 Design Docs
- DataInfra Overview — Full Market Data Model specification, feed provider YAML reference, dbt generation, metadata governance
- Ingestion Pipeline — Connectors, processors, writers, and multi-source configuration
- dbt Model Generator — Auto-generated staging, intermediate, and mart models
- Data Quality — Four-layer validation architecture with Elementary integration
- Metadata Specifications — Pydantic model reference: ProjectConfig, FeedProvider, CDMEntity, EngineConfig