Fundbox is a growing fintech company that provides an automatic underwriting platform based on data and AI. While scheduling a limited number of data workflows is a generally manageable task, scaling to hundreds of data workflows with dependencies and diverse job types requires substantial customized engineering, complexity, and overall expensive resources. Serverless-based architectures offer an alternative to traditional resource management.

Tomer Levi explains how the data engineering team at Fundbox uses AWS Step Functions, Docker containers, and Spark to build a live, serverless data orchestration platform, focusing on the company’s decision to build a friendly, yet powerful and scalable solution. Tomer details AWS Step Functions state machines, their limitations, and how to overcome them by building custom job-scheduling and dependency features. He illustrates how resource bottlenecks were overcome using Docker containers and AWS Fargate. Fundbox’s architecture is scalable and already serves dozens of engineers, BI developers, and data scientists in the company.

Prerequisite knowledge

• A basic understanding of serverless solutions
• Familiarity with the challenges introduced by enterprise architectures

What you'll learn

• Learn how Fundbox used AWS Step Functions, Docker containers, and Elastic Container Service (ECS) Fargate to build a serverless data workflow platform
• Understand key considerations from a data engineering perspective for deploying data workflow jobs

This session is from the 2019 O'Reilly Strata Conference in New York, NY.