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Stripe Data Architecture

Introduction

Stripe is a leading financial technology company that offers a comprehensive suite of products to manage payments and financial operations for businesses of all sizes. The company processes billions of transactions annually, supporting millions of merchants worldwide. As Stripe continues to grow, it faces increasing complexity in managing its data across various platforms, including transactional systems (OLTP), analytical systems (OLAP), and non-relational databases (NoSQL).

Stripe is looking to build a cutting-edge data infrastructure that seamlessly integrates these different types of data systems to support both operational excellence and advanced analytics.

The goal is to create a unified, scalable data architecture :

  • that can handle high volumes of transactional data, with high expectations for latency, consistency and speed;

  • support complex analytical queries, ad-hoc analysis and real-time insights.

  • manage unstructured data thought a NoSQL database for advanced use cases like fraud detection, customer behavior analysis and real-time recommendations. It has to be integrated with relational databases.

This architectture needs to be compliant and Secure.

Overview

This documentation describes the end-to-end data architecture for Stripe, integrating OLTP, OLAP, and NoSQL systems to support:

  • High-volume transaction processing (OLTP).
  • Advanced analytics (OLAP).
  • Flexible unstructured data management (NoSQL).
  • Real-time machine learning for fraud detection and personalization.
  • OLTP: Transactional backbone with ACID compliance and real-time replication.
  • OLAP: Analytical engine for complex queries and aggregations.
  • NoSQL and ML: Flexible storage for logs, user interactions and ML features, ML integration
  • Data Integration & ML: Unified pipeline, streaming and batch data between OLTP and OLAP/NoSQL systems
  • Cross-Cutting Concerns: Security, regulatoty compliance and monitoring concerns.