Near Real-Time Data Ingestion

Near-real-time data sync processing 2M+ records/day at 99.9% reliability with 60–70% compute cost reduction.

Overview

Built a serverless, event-driven streaming pipeline that syncs NoSQL document database changes to a cloud analytical warehouse in near-real-time. The system uses event-driven triggers to capture data mutations, serverless compute functions for transformation and schema validation, and message queue dead-letter queues for fault tolerance, all provisioned via Infrastructure as Code (IaC).

This replaced an aging batch-based approach that suffered from hours of latency and significantly higher cloud compute costs.

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Architecture & Tech Stack

Layer	Technology
Source	NoSQL Document Database (CDC stream)
Trigger	Event-Driven Cloud Router / Trigger
Processing	Serverless Compute Functions (Python)
Storage	Cloud Analytical Data Warehouse
Error Handling	Message Queue Dead Letter Queue (DLQ)
IaC	Terraform (declarative infrastructure modules)

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Challenges & Decisions

1. Serverless Functions vs. Always-On Streaming Workers

• Problem: Initial design considered always-on streaming cluster workers, but the event volume did not justify continuous idle compute costs.
• Approach: Serverless compute functions with event-driven triggers — pay-per-invocation with zero idle resource cost.
• Outcome: 60–70% cost reduction while maintaining sub-minute end-to-end data latency.

2. Dead-Letter Queue (DLQ) for Fault Tolerance

• Problem: Transient streaming insert failures or schema mismatches could result in permanent data loss.
• Approach: Message queue DLQ captures failed mutation events; a dedicated retry handler function processes them with exponential backoff.
• Outcome: 99.9% end-to-end reliability with zero manual intervention required during upstream network blips.

3. Declarative Infrastructure as Code (IaC)

• Problem: Multiple environments (dev, staging, prod) required identical infrastructure configurations and access policies.
• Approach: Full IaC implementation using modular Terraform definitions — a single automated deployment command provisions the entire streaming architecture.
• Outcome: Guaranteed environment parity, auditable infrastructure changes, and rapid rollback capability.

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Key Learnings

• Serverless Efficiency: Serverless event-driven architectures can match dedicated streaming cluster throughput at a fraction of the cost for mid-volume workloads.
• DLQs are Mandatory: Dead-Letter Queue patterns are non-negotiable for production near-real-time systems — silent data loss is far worse than temporary latency.
• IaC Pays Off Immediately: Modular Infrastructure as Code pays for itself the moment an engineering team needs to spin up a second environment or audit security policies.