In today’s data-driven world, there is a significant advantage to being able to support a polyglot data environment in which different storage and processing technologies are used to handle various needs.
Category Archives: Data
How DoorDash achieves fast travel estimates
Online ordering and delivery have become an increasingly popular lifestyle.
How DoorDash is pushing experimentation boundaries with interleaving designs
We’ve traditionally relied on A/B testing at DoorDash to guide our decisions.
Safeguarding app health and consumer experience with metric-aware rollouts
As part of our ongoing efforts to enhance product development while safeguarding app health and the consumer experience, we are introducing metric-aware rollouts for experiments.
Sharpening the Blur: Removing dilution to maximize experiment power
When it comes to reducing variance in experiments, the spotlight often falls on sophisticated methods like CUPED (Controlled Experiments Using Pre-Experiment Data).
API-First Approach to Kafka Topic Creation
DoorDash’s Engineering teams revamped Kafka Topic creation by replacing a Terraform/Atlantis based approach with an in-house API, Infra Service.
Transforming MLOps at DoorDash with Machine Learning Workbench
It is amusing for a human being to write an article about artificial intelligence in a time when AI systems, powered by machine learning (ML), are generating their own blog posts.
Leveraging Flink to Detect User Sessions and Engage DoorDash Consumers with Real-Time Notifications
At Doordash, we value every chance to boost order conversions in the app.
How DoorDash Standardized and Improved Microservices Caching
As DoorDash’s microservices architecture has grown, so too has the volume of interservice traffic.
Addressing the Challenges of Sample Ratio Mismatch in A/B Testing
Experimentation isn’t just a cornerstone for innovation and sound decision-making; it’s often referred to as the gold standard for problem-solving, thanks in part to its roots in the scientific method.