Case Study_03

Instagram Analytics Platform.

A high-concurrency analytics engine processing over 10,000 daily queries with near real-time insight delivery.

ROLE

Full-Stack Developer

TIMELINE

Jan 2024 - Dec 2024

0k+

Daily Analytics Queries

0ms

P95 API Response

0.00%

Query Success Rate

Analytics Architecture

The platform combines streaming ingestion, caching, and partitioned persistence to deliver fast dashboard reads.

Next.jsNestJSRedisPostgreSQLQueues
Analytics charts and real-time query stream

FIGURE C.01

Streaming Analytics Topology

Engineering Challenges.

01

Campaign windows produced high read bursts that saturated data nodes.

Solution

Added multi-tier caching and adaptive query shaping to maintain stable response times during spikes.

02

Out-of-order ingestion caused temporary dashboard inconsistencies.

Solution

Introduced sequence guards and replay-safe merges to guarantee deterministic rollups.

Analytics reliability depends on predictable ingestion pipelines and aggressive read optimization.

Cache Strategically

A layered cache strategy provided the biggest performance lift for dashboard workloads.

Backpressure Matters

Proper queue backpressure prevented cascading failures under traffic bursts.

Separate Read and Write Paths

Splitting pipelines improved resilience and simplified tuning.

Data Integrity First

Consistency checks at ingestion avoided downstream analytical drift.

analytics-rollup.service.ts

Brian Mutai / Full-Stack Developer