Case Study: Scaling Ad-hoc Analytics for a Fintech Startup

This case study explores how a mid-stage fintech startup transitioned from a single Postgres instance to a serverless analytics stack supporting dozens of analysts while keeping costs predictable and meeting compliance requirements.

Initial Challenges

The company faced:

• Frequent analyst queries that overloaded Postgres replicas.
• Rising costs and manual management of clusters.
• Strict PCI/PII compliance requirements.

Solution Architecture

They adopted a layered architecture:

1. Event ingestion into object storage with schema-on-write via a streaming ETL (Debezium -> Kafka -> Parquet writer).
2. Serverless SQL engine for ad-hoc queries (pay-per-query model).
3. Materialized metrics and regulated views in a controlled access area for dashboards.
4. Strong IAM and column-level access controls for sensitive fields.

Key Actions

• Converted event tables to Parquet and implemented daily partitions.
• Introduced materialized views for common KPIs with strict access control.
• Implemented a query cost estimator and quotas for non-prod environments.
• Built a compliance layer that redacts PII in the exploratory sandbox while allowing full data access in secured environments.

Outcomes

Within three months:

• Average query latency dropped by 60% for analysts.
• Monthly analytics costs stabilized and became predictable.
• Security audits passed with clearly defined data access policies.
• Engineering time spent on query infrastructure fell by 75%.

Lessons Learned

Key lessons include:

• Invest early in storage formats and partitioning—this paid off immediately.
• Separate exploratory sandboxes from production datasets with explicit redaction strategies.
• Provide analysts with prebuilt, curated datasets to reduce inefficient ad-hoc queries.

Advice for Similar Teams

If you're a startup scaling analytics:

• Start with serverless SQL for agility, then add reserved capacity if your workload stabilizes.
• Automate data classification and access control to satisfy audits.
• Monitor and iterate on query patterns—small governance changes often yield big savings.

Conclusion

This fintech’s experience shows that serverless analytics can deliver both agility and security when combined with disciplined data engineering practices. The result: empowered analysts, predictable costs, and compliance-ready controls.