Atlas Analytics Platform

The Problem: Data That Arrives Too Late

Atlas Corp had no shortage of data. They had a shortage of timely data. Their existing data warehouse ran nightly batch jobs. Business leaders made pricing and inventory decisions on information that was 24-48 hours old.

In a competitive market, 24-hour-old data is not data — it's history.

Architecture

Real-Time Ingestion with Kafka

We instrumented all 7 source systems to emit events to Apache Kafka topics. Every transaction, every inventory change, every customer action generates an event within milliseconds.

Kafka topics are organized by domain (orders, inventory, customers, pricing) and consumed by multiple downstream systems without coupling the producers to consumers.

ClickHouse for Analytics

Traditional databases are optimized for transactional workloads. ClickHouse is optimized for analytical queries — aggregations, time-series analysis, and columnar scans across billions of rows.

We migrated Atlas's analytical queries from PostgreSQL to ClickHouse and saw:

• 120x improvement on complex aggregation queries
• Sub-second response on queries that previously took 4+ minutes
• Linear scaling as data volume grows

The Self-Serve Layer

Business users shouldn't need SQL to answer questions about their business. We built a semantic layer that translates business concepts ("show me revenue by region for the last 30 days") into optimized ClickHouse queries.

The interface combines pre-built dashboard templates with ad-hoc exploration — analysts get the flexibility they need, business users get the simplicity they require.

Results

• Sub-second query latency across 500M records
• Real-time data with average 800ms end-to-end latency from transaction to dashboard
• 87% reduction in time spent on manual reporting
• $12M in monthly decisions now informed by the platform
• 340 daily active users across the business within 60 days of launch