Orchestrating Data Workflows Using a Fully Serverless Architecture
Who is this presentation for?Senior Data Engineer at Fundbox
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 a 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 StepFunctions, Docker containers, and Spark to build a live serverless data orchestration platform, focusing on their decision to build a user-freindly yet a powerful and scalable solution. Tomer will further describe AWS StepFunctions state machines, their limitations, and how to overcome them by building custom job scheduling and dependency features. Finally, the talk will illustrate 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 knowledgeA basic understanding of serverless solutions. Familiarity with the challenges introduced by enterprise architectures.
What you'll learn
Tomer is a senior data engineer on the DataOps team at Fundbox, where he helps shape the data platform architecture to drive business goals.
Previously, he was a data engineer at Intel’s advanced analytics group helping to build out the data platform supporting the data storage and analysis needs of Intel® Pharma Analytics Platform, an edge-to-cloud artificial intelligence solution for remote monitoring of patients during clinical trials.
He is incredibly passionate about the power of data. Tomer holds a BSc in software engineering.
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