Scaling and Modernizing Data Platform with Databricks
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This document summarizes Atlassian's adoption of Databricks to manage their growing data pipelines and platforms. It discusses the challenges they faced with their previous architecture around development time, collaboration, and costs. With Databricks, Atlassian was able to build scalable data pipelines using notebooks and connectors, orchestrate workflows with Airflow, and provide self-service analytics and machine learning to teams while reducing infrastructure costs and data engineering dependencies. The key benefits included reduced development time by 30%, decreased infrastructure costs by 60%, and increased adoption of Databricks and self-service across teams.
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Scaling and Modernizing Data Platform with Databricks
- 1. Managing & Scaling Data Pipelines with Databricks Esha Shah Senior Data Engineer ATLASSIAN Go-To-Market Data Engineering Richa Singhal Senior Data Engineer
- 2. Agenda Atlassian Overview Summary Adopting Databricks Data Platform Challenges
- 4. Growth over the last 5 years Data is now 20x times (Multi petabytes) 5x growth in numbers of internal users 5x number of events/day (Billions)
- 5. Atlassian Data Architecture (Before Databricks)
- 6. Key Challenges with Legacy Architecture Development Cross-team dependencies Cluster management Collaboration
- 8. Current Atlassian Data Architecture
- 9. Our Success Story Reduced development time Rapid Development Increased team and project efficiency with simplified sharing and co-authoring Collaboration Were able to support growth while reducing Infrastructure cost Scaling Removed Data engineering dependency for Analytics and Data Science teams Self Service
- 10. Adopting Databricks at Atlassian Building Data Pipelines Orchestration Leveraging Databricks Delta Databricks for Analytics and Data Science
- 12. Data Pipelines with Databricks Data Pipelines using Notebooks Data Pipelines using DB-Connect
- 13. Development using Databricks Notebook AWS Cloud Interactive Cluster Ephemeral Cluster Bitbucket Branch Databricks Workspace Import/ Export Jira Ticket Command Line Databricks Notebook Databricks Cluster
- 14. Multi-stage Envs using Databricks Workspaces Databricks Notebook Databricks Workspace Dev Folder Local/ Development Stage/ Production Bitbucket CICD Pipeline Stg Folder Prod Folder Stg Cluster Prod Cluster
- 15. Bitbucket CICD Pipeline branches: main: - step: name: Check configuration file deployment: test script: - pip install -r requirements.txt - 'yamllint -d "{extends: default, rules: {}" config.yaml' - python databricks_cicd/check_duplicates.py - step: name: Move code to Databricks deployment: production caches: - pip script: - pip install -r requirements.txt - bash databricks_cicd/move_code_to_databricks.sh prod - step: name: Update the job in Databricks script: - pip install -r requirements.txt - python databricks_cicd/configure_job_in_databricks.py
- 16. Development using DB-Connect Library AWS Cloud Interactive Cluster Ephemeral Cluster Bitbucket Branch Local IDE Pull Request /Merge db-connect Jira Ticket Databricks Cluster
- 17. Multi-stage Envs using AWS S3 Local IDE Databricks Cluster Dev Bucket Local/ Development Stage/ Production Bitbucket CICD Pipeline Docker Stg Bucket Prod Bucket Stg Cluster Prod Cluster
- 18. Orchestration
- 19. Orchestration using Airflow Airflow on Kubernetes SparkSubmit Task YODA In-house Data Quality Platform SignalFx Opsgenie On-Call Notebook Task Slack Notification Code on S3 Notebook Databricks Workspace
- 20. Tracking Resource Usage and Cost Job Metadata 'custom_tags': { 'business_unit': 'Data Engineering', 'environment': cluster_env, 'pipeline': 'Team_name', 'user': 'airflow', 'resource_owner': '<resource_owner>', 'service_name': '<service-name>' } Data Lake Ad Hoc Reporting Databricks Job
- 22. Delta Time Travel Merge Auto-optimize
- 23. Databricks for Analytics and Data Science
- 24. Analytics Use Cases Exploratory and root cause analysis Analysis for Strategic Decisions POC for new metrics and business logic Creating and refreshing ad-hoc datasets Team Onboarding Templates
- 25. Big Wins: Analytics Self-service Collaboration
- 26. Data Science Use Cases Exploration, Sizing Feature generation Model training Scoring Experiments Analyzing results Model serving
- 27. Big Wins: Data Science Faster local stack to cloud cycle No infrastructure overhead Increased ML adoption across teams Governance & Tracking
- 28. Summary
- 29. Key Takeaways Delivery time reduced by 30% Decreased infrastructure costs by 60% Databricks used by 50% of all Atlassians Reduced Data team dependencies by more than 70%
- 30. Thank you!
- 31. Feedback Your feedback is important to us Don’t forget to rate and review the sessions