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Building a Lakehouse for Data Science at DoorDash

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Type

  • Session

フォーマット

  • Hybrid

Track

  • データレイク、データウェアハウス、データレイクハウス

業種

  • 小売・消費財

Difficulty

  • Beginner

Room

  • Moscone South | Level 2 | 202

Duration

  • 35 min
Download session slides

概要

DoorDash was using a data warehouse but found that they needed more data transparency, lower costs, and the ability to handle streaming data as well as batch data. With an engineering team rooted in big data backgrounds at Uber and LinkedIn, they moved to a Lakehouse architecture intuitively, without knowing about the term. In this session, learn more about how they arrived at that architecture, the process of making the move, and the results they have seen. While addressing both data analysts and data scientists from their lakehouse, this session will focus on their machine learning operations, and how their efficiencies are enabling them to tackle more advanced use cases such as NLP and image classification.

Session Speakers

Hien Luu

シニアエンジニアリングマネージャー

DoorDash

Brian Dirking

Sr. Director Partner Marketing

Databricks

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