Building a Multi Region Compliant Customer Data Lake at Scale

Summary

The article outlines Atlassian's approach to building a unified customer data lake to address limitations in analytics and data export as their cloud customer base expanded. It details the challenges faced, including the need for instant analytics, secure data export paths, and the strain of analytical workloads on operational databases. The solution involved implementing a logical replication strategy for change data capture (CDC) across various product teams, utilizing a decentralized data mesh architecture. The article further explains the design of a streaming ETL pipeline using Apache Flink and Delta Lake, emphasizing the importance of schema evolution and the collaborative process between product and data modeling teams.

Key Learnings

• 1A unified customer data lake can significantly enhance analytics capabilities and data export processes in multi-tenant environments.
• 2Logical replication for change data capture provides technology independence and stability, allowing product teams to manage their data as a product.
• 3Implementing a streaming ETL pipeline with tools like Apache Flink and Delta Lake enables near real-time data processing and analytics.
• 4The use of Protobuf for event schema ensures backward compatibility and efficient data handling across diverse systems.
• 5Collaborative schema management between product teams and data engineers is crucial for maintaining data integrity and usability in analytics.

Who Should Read This

Senior Data Engineers designing scalable data lakes and analytics solutions in cloud environments

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