Case Study: Optimizing Real-time User Analytics for a Large EdTech Platform Using ClickHouse
- Client: A leading EdTech platform with millions of active college students across India.
- Technology Stack: ClickHouse, Kafka, Python (ETL), Grafana, Kubernetes
The Challenge
The EdTech platform was facing challenges in delivering real-time ad personalization for its internal college ad network. The core issues included:
- Slow analytics pipelines due to a legacy OLAP database.
- Inability to track fine-grained clickstream data from internal ad placements.
- Difficulty in aggregating user interaction data (searches, course clicks, page visits) in real time.
- Poor ad relevance leading to low engagement and click-through rates.
The client needed a high-performance analytics solution to:
- Track and analyze user behavior in real time.
- Run complex aggregations on billions of events quickly.
- Optimize ad recommendations using deep user interaction data.
Our Approach at Opstree
We designed and implemented a modern ClickHouse-based analytics pipeline to solve this at scale.
Step 1: Event Pipeline & Modeling
- Built a Kafka-based event ingestion layer for real-time user interaction events (ad clicks, scrolls, hovers, etc.).
- Modeled the data to support wide-column schema design in ClickHouse optimized for analytical queries.
- Partitioned by day, and used MergeTree engines for efficient compression and fast reads.
Step 2: ClickHouse Deployment & Optimization
- Deployed ClickHouse in HA mode on Kubernetes, ensuring scalability and fault tolerance.
- Tuned system configurations (merge settings, buffer sizes) for high-throughput ingestion.
- Created materialized views and pre-aggregated tables for faster dashboard loading and complex joins.
Step 3: Analytics & Visualization
- Integrated ClickHouse with Grafana and Superset for real-time dashboards.
- Built custom ad-performance dashboards showing:
- CTR per college/user cohort
- Heatmaps of ad positions on pages
- Funnel analysis (search → view → click)
- Enabled real-time campaign performance tracking for the internal marketing team.
Step 4: Ad Personalization Engine (Phase 2)
- Leveraged user interaction logs stored in ClickHouse to build a lightweight personalization layer.
- Queried user interest segments and behavior patterns to serve dynamic, more relevant ads.
- Enabled the platform to run A/B experiments efficiently using event data snapshots.
Business Impact
| Metric | Before ClickHouse | After Opstree's Solution |
|---|---|---|
| Ad CTR | ~1.1% | 2.8% |
| Dashboard load time | ~40s | 2s |
| Query time on 1B+ records | ~30s | <1s |
| Infrastructure cost (OLAP) | High | ~35% with ClickHouse compression |
| Ad personalization capability | None | Live & contextual |
Why ClickHouse?
ClickHouse was selected due to its:
- Columnar storage ideal for analytical queries
- Blazing-fast aggregation over large datasets
- Advanced compression for reduced storage cost
- Native support for real-time inserts and joins
Summary
Opstree delivered a high-performance, scalable analytics solution using ClickHouse, enabling real-time user behavior tracking and ad personalization for a leading EdTech platform. The result: faster insights, better user targeting, and measurable business outcomes.