Our client operated a data-intensive analytics platform that relied heavily on MongoDB as its primary datastore. While MongoDB was excellent for transactional workloads and flexible schema handling, it proved inefficient for analytical queries at scale. Dashboards and reports were slow to load—often taking up to 15 seconds per query—which hindered user experience and made real-time insights impractical.

The client needed a solution to dramatically improve performance, lower latency, and enable advanced analytics without overhauling their entire system.

Slow Analytics Queries: MongoDB’s row-oriented design was not optimized for aggregations and analytical workloads.

User Experience Bottleneck: Dashboards took ~15 seconds to load, frustrating users and slowing decision-making.

Data Integrity & Reliability: The solution needed to ensure consistent, near-real-time syncing of operational data into an analytics-ready format.

We designed and implemented a robust ETL (Extract, Transform, Load) pipeline to offload analytical workloads from MongoDB into ClickHouse, a high-performance, columnar database purpose-built for analytics.

Incremental Data Extraction: Built a connector to extract only new and updated records from MongoDB to minimize overhead.

Data Transformation: Normalized and optimized data formats for columnar storage, ensuring efficient aggregation and query execution.

Batch & Streaming Support: Designed pipelines to handle both batch syncs and near-real-time streaming for critical metrics.

Optimized Schema for Analytics: Restructured schemas in ClickHouse to maximize performance of complex queries and dashboard visualizations.

Monitoring & Recovery: Implemented logging, alerts, and automated retries to ensure data consistency across both databases.

Query Speed Boost: Reduced dashboard load times from 15 seconds → 0.39 seconds (a 38x improvement).

Enhanced User Experience: Users could now interact with charts and extract insights in near real-time, boosting adoption and satisfaction.

Operational Efficiency: Freed MongoDB from heavy read/aggregation workloads, improving its transactional performance.

Scalable Architecture: The ETL design supports future scaling as data volume grows, without sacrificing performance.

This transformation empowered the client to:

Provide real-time insights to end-users.

Scale their analytics platform without incurring massive infrastructure costs.

Deliver a competitive advantage in speed and responsiveness compared to rivals.

If your current database is slowing down your analytics, we can help. From designing ETL pipelines to building real-time, scalable analytics platforms, our team combines expertise in full-stack development, DevOps, and advanced data engineering. Let’s discuss how we can accelerate your analytics performance.

📩 Contact us today for a free consultation or demo.