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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

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
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
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

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

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
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

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

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
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

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
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

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