Carvana Enhances Customer Experience with Databricks DLT and Streaming
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Consumer Goods
- Retail
Applicable Functions
- Maintenance
- Quality Assurance
Use Cases
- Real-Time Location System (RTLS)
- Visual Quality Detection
Services
- Testing & Certification
About The Customer
Carvana is an online used car retailer based in Arizona. Founded in 2012, the company’s mission is to change how people buy and sell cars by offering an intuitive and convenient online car buying, selling and financing experience. Data is key to helping Carvana achieve that mission. Carvana developed its Next Generation Communication Platform (NGCP) to help car buyers and sellers enjoy a seamless car shopping experience. NGCP engineers and product teams built the data platform from the ground up by researching and prototyping new technologies and working as a team to deploy new features and services to production.
The Challenge
Carvana, an online used car retailer, developed its Next Generation Communication Platform (NGCP) to provide a seamless car shopping experience. However, the NGCP team faced several challenges related to scale, data quality, and high data warehouse costs. The team initially streamed its conversation and AI data into Google BigQuery, which limited how data engineers could partition and optimize query tables. Data quality was another challenge, with engineers needing to dedupe in the pipeline, but distinct calls on large data frames were slow and caused recomputation on the entire data set. The team also faced data availability challenges, with no process to automatically pick up experiment data as campaigns were configured and run. Maintenance and transparency were another challenge, as a single repo contained both the ETL and business logic. Finally, the data sets produced often contained too many files to be shipped to data warehouses via the Spark Connector, creating a data export bottleneck.
The Solution
Carvana turned to the Databricks Lakehouse Platform, which includes Delta Live Tables and Databricks SQL Serverless, to overcome these challenges. The platform enabled the NGCP team to manage the complex dependencies and scale of its data pipelines. The team now uses Delta Live Tables (DLT) as a single-entry point for streaming and batch jobs, dependency orchestration, data quality, and error handling. This allows them to build scalable and testable pipelines under a data medallion architecture with simple and declarative syntax. DLT also provided several technical improvements, including data lineage visibility and real-time alerts for delayed events. At the data warehouse level, Carvana uses Databricks SQL Serverless and Delta Lake, which improve speed for real-time analytics use cases where Carvana data engineers need accurate data with very low latency.
Operational Impact
Quantitative Benefit
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