Building and Running Atlassian's Data Lake - A Retrospective

Overview

Gain insights into Atlassian's journey of building and managing their data lake infrastructure, exploring challenges, solutions, and key learnings from their real-world implementation.

Syllabus

• Introduction to Data Lakes
Definition and purpose of data lakes
Differences between data lakes and data warehouses
Use cases for data lakes
• Atlassian's Data Lake Journey
Motivation for building a data lake
Initial goals and project scope
Overview of the data lake architecture
• Infrastructure and Technology Stack
Key technologies used in Atlassian's data lake
Integrating with existing systems
Scalability considerations
• Challenges Faced
Data ingestion and processing issues
Handling diverse data sources
Ensuring data quality and consistency
• Solutions and Best Practices
Strategies for efficient data ingestion
Techniques for maintaining data integrity
Tools and frameworks that played key roles
• Data Governance and Security
Implementing data governance policies
Security measures and compliance
Access control and auditing
• Key Learnings and Insights
Lessons learned from the implementation process
How challenges were overcome
Impact on business processes and decision-making
• Future Directions and Innovations
Evolving the data lake infrastructure
Incorporating advancements in AI and analytics
Planning for future growth
• Conclusion and Retrospective
Reflecting on the journey and outcomes
Final thoughts on the success and impact of the project
Open discussion on potential improvements and innovations

Subjects

Business