Data

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Backend Data How We Applied Client-Side Caching to Improve Feature Store Performance by 70%

Learn about which caching libraries we considered, the analysis of our system and how we were able to use experiments to validate our approach.

Kornel Csernai

18 Minute Read

Data

3 Principles for Building an ML Platform That Will Sustain Hypergrowth

Learn about the principles and big bets that enabled to scale and maintain our ML platform which supports our data users and data scientists

Hien Luu 16 Minute Read

Data

Making Applications Compatible with Postgres Tables BigInt Update

Ensuring ID's don't get exceeded is an evolving challenge. Learn how DoorDash made our tables were compatible with our new Bigint upgrade

Maggie Fang

Amiraj Dhawan 10 Minute Read

Data Machine Learning

Introducing Fabricator: A Declarative Feature Engineering Framework

Learn how our Fabricator infrastructure integrations for ML features were automated and continuously deployed to generate 500 unique features and 100+ billion daily feature values

Kunal Shah 13 Minute Read

Data

How to Run Apache Airflow on Kubernetes at Scale

Learn how DoorDash managed to make its data orchestration more scalable and reliable with Kubernentes and Airflow

Akshat Nair 10 Minute Read

Data

The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%

In a data driven world a company's experiment capacity directly impacts its development velocity. Learn what DoorDash did to boost testing 1000%

Sifeng Lin

Yixin Tang 18 Minute Read

Backend Data

Building Faster Indexing with Apache Kafka and Elasticsearch

DoorDash describes how it built a faster search index using open source projects.

Satish Saley

Danial Asif

Siddharth Kumar 13 Minute Read

Data

Overcoming Rapid Growth Challenges for Datasets in Snowflake

To meet additional SLA's of DoorDash's rapidly growing team without increasing compute we had to rely on a variety of ETL optimizations.

Andrew Huynh

Ashwini Manjunath 10 Minute Read

Data Machine Learning

Maintaining Machine Learning Model Accuracy Through Monitoring

Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.

Swaroop Chitlur

Kornel Csernai 18 Minute Read

Data Machine Learning

Building Riviera: A Declarative Real-Time Feature Engineering Framework

In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...

Allen Wang

Kunal Shah 17 Minute Read

Data Machine Learning

How to Drive Effective Data Science Communication with Cross-Functional Teams

Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...

James Williams

Lokesh Bisht 11 Minute Read

Data Machine Learning

Running Experiments with Google Adwords for Campaign Optimization

Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...