Challenge
Batch jobs ran overnight with silent failures; product teams needed fresher metrics and trustworthy lineage.
Case study · 2025
Cloud, DevOps & Data Engineering
Real-time data warehousing without the chaos
ETL/ELTReal-Time DataData WarehousingData EngineeringGCPPython
Client
Analytics & AI platform team
Engagement
Streaming data warehouse on GCP
Duration
9 months
Year
2025
StreamVault replaced overnight batch chaos with near-real-time curated tables. Streaming ingestion, contract-tested transforms, warehouse governance, and quality monitors give BI and AI a shared semantic layer with freshness SLAs measured in minutes — and failed jobs that page humans instead of failing silently.
ETL/ELT and streaming pipelines on GCP that feed a governed warehouse for analytics and AI features.
Narrative
How the engagement moved from constraints to a shippable system.
Approach
We introduced streaming ingestion, dbt-style transforms, warehouse contracts, and data quality monitors.
Solution
StreamVault delivers near-real-time curated tables with lineage and SLAs that analytics can depend on.
Scope
What we delivered
Streaming ingestion paths on GCP
Transform layer with warehouse contracts
Governed curated marts for BI and AI
Data quality monitors and lineage
On-call runbooks and freshness SLAs
Semantic layer documentation
Highlights
What stood out
Core event freshness under 5 minutes
Failed-job MTTR reduced by 60%
Shared semantics for BI and AI features
Lineage visible to analysts and engineers
Engagement flow
A repeatable rhythm from discovery through hardening — tuned to this product’s constraints.
01
Batch post-mortem
Traced silent failures, freshness gaps, and conflicting metric definitions.
02
Ingest & contracts
Stood up streaming paths and table contracts for critical domains.
03
Curated layer
Built marts, quality monitors, and lineage for consumer teams.
04
Operate
Defined SLAs, alerts, and ownership for ongoing freshness.
Technology stack
The delivery stack behind this engagement — client surfaces paired with services, data, and infrastructure.
Frontend
Ops DashboardsNuxt.jsData Quality Views
Data Platform
PythonETL/ELTReal-Time StreamingData Warehousing
Cloud
GCPPub/SubDataflowBigQuery
Outcomes
01
Freshness SLA: under 5 minutes for core events
02
Failed-job MTTR reduced by 60%
03
Shared semantic layer for BI and AI
Looking ahead
Expose self-serve feature tables for ML with stricter PII controls.
More work
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