Carro

Explore Project

Data pipeline engineering

Core Capacities

Real-time, AI-ready infrastructure

Project Focus

2025

Year

Overview

Decoupling Analytics from Production

Decoupling Analytics from Production

Decoupling Analytics from Production

Carro is a Series B platform that helps e-commerce brands grow through cross-store selling, brand collaborations, and new distribution channels. As the platform scaled, its production database was doing double duty — powering live customer operations and internal analytics simultaneously. The combined load created performance bottlenecks and blocked any path toward the machine learning and AI capabilities Carro's roadmap required.

Carro is a Series B platform that helps e-commerce brands grow through cross-store selling, brand collaborations, and new distribution channels. As the platform scaled, its production database was doing double duty — powering live customer operations and internal analytics simultaneously. The combined load created performance bottlenecks and blocked any path toward the machine learning and AI capabilities Carro's roadmap required.

Carro is a Series B platform that helps e-commerce brands grow through cross-store selling, brand collaborations, and new distribution channels. As the platform scaled, its production database was doing double duty — powering live customer operations and internal analytics simultaneously. The combined load created performance bottlenecks and blocked any path toward the machine learning and AI capabilities Carro's roadmap required.

1

The Challenge

The Challenge

The Challenge

Running analytics and live operations against the same database was a ticking clock. As merchant volume grew, query performance degraded and the data science team had no reliable foundation for AI-driven recommendations or demand intelligence. The company needed to decouple the two environments without disrupting the live platform — and do it in a way that could support advanced AI use cases, not just reporting.

2

The Solution

The Solution

The Solution

Sierra designed and built a fully decoupled streaming data pipeline using Kafka Change Data Capture, replicating production data in real time with zero impact on live operations. Before building anything, Sierra's engineers investigated and reverse-engineered the existing database schemas to make sure the new architecture reflected how the data actually behaved — not how it was assumed to behave. The pipeline fed into a dedicated Databricks analytics environment structured across Bronze, Silver, and Gold layers, from raw ingestion to business-ready outputs.

3

Implementation Highlights

Implementation Highlights

Implementation Highlights

• Conducted investigatory analysis and reverse-engineered existing database schemas before a line of architecture was committed. • Built real-time CDC ingestion using Confluent Kafka, eliminating analytics load from the production system entirely. • Defined the full infrastructure in Terraform, making deployments reproducible and reconfigurable as the business evolves. • Structured data across a medallion architecture optimized for machine learning, recommendation engines, and downstream AI workflows.

4

Results

Results

Results

Jobs that previously took an hour now run in minutes. More significantly, the new foundation unlocked capabilities that had not been feasible before — machine learning models, recommendation engines, and AI-driven demand intelligence built on clean, real-time data. Carro now has the infrastructure to pursue AI-powered merchant experiences without putting operational reliability at risk.

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Let's Build Together.

Let's Build Together.

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