Improving ad hoc and production workflows at Stitch Fix

Description

Stitch Fix aspires to help you find the style that you will love. Data, the backbone of the business, is used to help with styling recommendations, demand modeling, user acquisition, and merchandise planning and also to influence business decisions throughout the organization. These decisions are backed by algorithms and data collected and interpreted based on client preferences. Neelesh Srinivas Salian offers an overview of the compute infrastructure used by the data science team at Stitch Fix, covering the architecture, tools within the larger ecosystem, and the challenges that the team overcame along the way.

Apache Spark plays an important role in Stitch Fix’s data platform, and the company’s data scientists use Spark for their ETL and Presto for their ad hoc queries. The goal for the team running the compute infrastructure is to understand and make the data scientists’ lives easier, particularly in terms of usability of Spark, by building tools that expedite the process of getting started with Spark and transitioning from an ad hoc to a production workflow. The compute infrastructure is a part of the data platform that is responsible for all the needs of data scientists as Stitch Fix.

Neelesh shares Stitch Fix’s journey, exploring its ad hoc and production infrastructure and detailing its in-house tools and how they work in synergy with open source frameworks in a cloud environment. Neelesh also discusses the additional improvements to the infrastructure that help persist information for future use and optimization and explains how the implementation of Amazon’s EMR FS has helped make it easier to read from the S3 source.