Datalenses

Introduction

Built a cost-efficient public on-chain analytics platform across 5+ Cosmos chains, used by protocols and institutional investors for reliable decision-making, with 100+ custom metrics and p99 dashboard queries <150 ms. Powered by BigQuery and DBT, scheduled pipelines, incremental builds, and strategic caching to reduce compute costs by ~80% while maintaining data accuracy.

The Challenge

The Cosmos ecosystem needed accessible analytics, but real-time processing was prohibitively expensive. The challenge was to deliver comprehensive metrics across 5 major chains while keeping infrastructure sustainable. We had to balance data freshness with efficiency, serving both retail users and institutional investors who required accurate historical analysis.

Solution & Approach

We built a cost-optimized analytics platform based on scheduled batch processing:

Data Pipelines

• Contributed to ingestion pipelines processing data from 5+ Cosmos chains.
• Implemented DBT transformations on scheduled runs to reduce compute costs.
• Designed BigQuery schemas optimizing performance versus storage.
• Set up data quality checks to ensure reliable decisions.

Cost-Optimized Architecture

• Scheduled batch processing instead of real-time streams (80% cost reduction).
• Strategic caching layers to avoid redundant compute.
• Incremental DBT models that process only new data.
• Query optimizations cutting BigQuery costs while maintaining performance.

Analytics Development

• Assisted with TVL calculations and liquidity metrics.
• Contributed to cross-chain volume aggregation pipelines.
• Supported dashboard development with Next.js and TypeScript.
• Helped implement data exports for institutional clients.

Results & Impact

Datalenses is now a trusted analytics source in the Cosmos ecosystem—providing historical insights, cross-chain metrics, and fast dashboard access while keeping infrastructure cost sustainable through batch processing and incremental data modeling.

Platform Achievements

• Served data to VCs and major investors across the ecosystem
• Processed data from 5+ Cosmos chains reliably
• Maintained sustainable infrastructure costs through batch processing
• Delivered accurate historical data for investment, users, and usage patterns

Technical Contribution

• Stable data pipelines with scheduled DBT transformations.
• Cost-optimized architecture reducing expenses by 80% vs. real-time.
• Reliable data quality enabling institutional decisions.
• Scalable infrastructure ready for additional chains.

The platform continues to serve the Cosmos community with reliable, cost-efficient analytics that institutional investors trust for decision-making.