At the intersection of fashion and technology, Rent the Runway (RTR) has built a sophisticated data infrastructure that powers their designer clothing rental service. In a recent tech talk at the Prefect and dbt joint event, Vishal Kella, Staff Engineer at Rent the Runway, provided a fascinating glimpse into how the company orchestrates its data operations using Prefect and dbt.
Rent the Runway's business model presents unique data challenges. Beyond the customer-facing rental platform, the company manages complex warehouse operations, marketing initiatives, and financial data streams. Their data engineering team handles ingestion and processing of everything from inventory tracking to customer behavior analysis, requiring a robust and flexible data stack.
At the heart of RTR's data architecture lies a powerful combination of tools:
The company follows an ELT (Extract, Load, Transform) paradigm, with Prefect and dbt serving as the backbone of their data operations.
What makes RTR's implementation particularly interesting is how they've integrated Prefect and dbt. The data engineering team has created a parameterized Prefect flow that serves as their universal dbt runner. This flow handles:
The scale of their operation is impressive: approximately 30-40 dbt projects containing around 1,800 models, all orchestrated through Prefect.
One elegant example of their Prefect-dbt integration involves ingesting data from Google Sheets. Business stakeholders can input data into spreadsheets, which Prefect automatically ingests into Snowflake. dbt then applies quality tests using the dbt-expectations package before the data flows into downstream models.
Another notable implementation monitors their membership services data. The team uses tagged critical tests in dbt, executed through Prefect, to ensure data quality. When issues arise, the system automatically notifies relevant teams via Slack and email, creating a proactive data quality monitoring system.
RTR is currently migrating from Prefect 1 to Prefect 2, while simultaneously upgrading their dbt implementation from version 1.4 to 1.8. This transition has brought several benefits:
The team is focused on several key areas for improvement:
For teams looking to implement a similar stack, Kella offers valuable advice:
Rent the Runway's implementation of Prefect and dbt showcases how modern data tools can be combined to handle complex business requirements. Their approach demonstrates that successful data infrastructure isn't just about choosing the right tools—it's about thoughtfully integrating them to create reliable, maintainable, and scalable data operations.
By balancing custom solutions with standardized tools, RTR has built a data stack that can handle everything from simple spreadsheet ingestion to complex data transformations, all while maintaining data quality and observability. As they continue to evolve their stack, their experience offers valuable lessons for other organizations looking to scale their data operations.
