Market data pipeline modernisation
A quantitative analytics platform had unpredictable refresh cycles and escalating warehouse costs. Downstream models missed time windows. We restructured the pipeline for predictable time-to-data.
Context
A production analytics pipeline ingested and transformed daily market data for time-sensitive quantitative models.
Constraint
Time-to-data had to become predictable without increasing scan cost or breaking downstream data contracts.
Intervention
Reshaped the pipeline into staged transforms with incremental processing. Aligned partitioning and clustering to access patterns. Replaced deeply nested queries with materialised intermediate steps.
Key decisions
- Partitioning aligned to access patterns
- Staged transforms replacing nested queries
- Incremental processing for cost control
- Idempotent ingestion handling
- Orchestration with retry visibility
- Automated data quality checks
Outcomes
Batch critical path dropped from ~4 hours to ~35 minutes. On-demand slices returned in seconds. Scan costs stabilised.
Why it matters
Fresher model inputs, fewer missed refresh windows, and predictable cloud spend—without increasing operator burden.
Implementation
Practical technology choices that matched the constraints.
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