Data

Newest Article

AI & ML Data Machine Learning Transforming MLOps at DoorDash with Machine Learning Workbench

DoorDash builds an internal ML Workbench by taking a user-centered approach to solve for productivity and velocity of our Data, ML, AI teams.

Archit Jha

Nachiket Paranjape

10 Minute Read

Backend Data Mobile Web

Leveraging Flink to Detect User Sessions and Engage DoorDash Consumers with Real-Time Notifications

Doordash optimizes real-time notifications with the frontend events by leveraging streaming processing.

Chen Yang

Fan Zhang 9 Minute Read

Backend Data General

How DoorDash Standardized and Improved Microservices Caching

DoorDash's in-house multi-layer cache, used to unlock our high-performance services across DoorDash

Lev Neiman

Jason Fan 12 Minute Read

Backend Data

Addressing the Challenges of Sample Ratio Mismatch in A/B Testing

Solutions for dealing with sample ratio mismatch.

Stas Sajin

Michael Zhou

Krishna Gourishetti 19 Minute Read

Data

Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

Learn how DoorDash build a metrics layer to enable consistent metrics and democratized decision making for experimentation

Arun Balasubramani 23 Minute Read

Data

Using CockroachDB to Reduce Feature Store Costs by 75%

While building our ML feature store we learned that a mix of databases yields significant gains in efficiency and operational simplicity.

Brian Seo

Kunal Shah 12 Minute Read

Data

Balancing Velocity and Confidence in Experimentation

Figuring out how to balance our experimentation speed with the necessary controls to maintain trust is never easy. Learn DoorDash's approach.

Stas Sajin 17 Minute Read

Data

Adapted Switch-back Testing to Quantify Incrementality for App Marketplace Search Ads

Learn how DoorDash developed a switchback testing method to perform incrementality testing on an app marketplace ad platform

Kanhua Pan

Yingying Chen 11 Minute Read

Data Machine Learning

Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly

Data preparation, represents The vast majority of work in developing machine learning models, learn how to make things easier

Kornel Csernai

Devjit Chakravarti 10 Minute Read

Data

Building Scalable Real Time Event Processing with Kafka and Flink

Learn how DoorDash build a platform to process billions of events from different data sources, quickly, consistently and reliably

Allen Wang 19 Minute Read

Data General

Building a Source of Truth for an Inventory with Disparate Data Sources

Learn how DoorDash crowdsources data from a variety of sources to help predict realtime inventory for our new connivence and grocery product

Anubhav Kushwaha 9 Minute Read

Data

Using Back-Door Adjustment Causal Analysis to Measure Pre-Post Effects

When A/B testing is not recommended we can still quickly implement a new feature and measure its effects in a data-driven way.

Sharon Cui 9 Minute Read

Data Machine Learning

Meet Dash-AB — The Statistics Engine of Experimentation at DoorDash

Learn how DoorDash was able to test uniformly according to established best practices and reuse complex statistical methods with Dash AB

Caixia Huang

Yixin Tang 12 Minute Read

Newest Article

Leveraging Flink to Detect User Sessions and Engage DoorDash Consumers with Real-Time Notifications

How DoorDash Standardized and Improved Microservices Caching

Addressing the Challenges of Sample Ratio Mismatch in A/B Testing

Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

Using CockroachDB to Reduce Feature Store Costs by 75%

Balancing Velocity and Confidence in Experimentation

Adapted Switch-back Testing to Quantify Incrementality for App Marketplace Search Ads

Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly

Building Scalable Real Time Event Processing with Kafka and Flink￼

Building a Source of Truth for an Inventory with Disparate Data Sources

Using Back-Door Adjustment Causal Analysis to Measure Pre-Post Effects

Meet Dash-AB — The Statistics Engine of Experimentation at DoorDash

Building Scalable Real Time Event Processing with Kafka and Flink