0.11.0 - Prepare for 1.0.0

• Junit5
• Spark 3.4.0
• Minimize default interpreters
  • Exclude Shell interpreter to minimize security issues by default
  • Download interpreters easily
    • Could use Github release for individual interpreters
• Fluent docker support
• JDK 11

OUTDATED

• Enterprise ready
  • Authentication 
    • Shiro authentication ZEPPELIN-548
  • Authorization 
  • • Notebook authorization PR-681
  • Security
  • Multi-tenancy
  • • user impersonation
  • Stability
  • • Better memory management for notebook
  • Job scheduler
  • High availability and Disaster Recovery
  • Monitoring (exposing JMX metrics)
• Usability Improvement
  • UX improvement
    • Folder structure for notebook
  • Better Table data support
    • Download data as csv, etc PR-725, PR-714, PR-6, PR-89
    • Featureful table data display (pagenation, filter, sort etc)
• Pluggability ZEPPELIN-533
  • Pluggable visualization
  • Dynamic Interpreter, notebook, visualization loading
  • Repository and registry for pluggable components
• Improve documentation
  
  • Improve contents and readability
  • more tutorials, examples
• Interpreter
  • Generic JDBC Interpreter
  • (spark)R Interpreter
  • Cluster manager for interpreter (Proposal)
  • more interpreters
  • Developer support (including easier debugging)
• Notebook storage
  • Versioning ZEPPELIN-540
  • more notebook storages (github push/pull)
• Visualization
  • More visualizations PR-152, PR-728, PR-336, PR-321
  • Customize graph (show/hide label, color, etc)

0.7.0 - Enterprise-ready

• Enterprise support
  • multi user support (ZEPPELIN-1337)
    • Impersonation
  • Job management
  • Monitoring support (e.g. JMX)
• Interpreter
  • Improve JDBC / Python interpreter
  • New Interpreters
• Front end performance improvement
• Pluggable visualization

1. Job management
   • New menu 'Job'. Which is displaying all job status, job histories. Display scheduled job information at a glance.
2. Better Python support
   • Better integration with libraries such as matplotlib. Better python repl integration (like auto completion, etc)
3. R language support
   • Implementation of sparkR interpreter
4. Output streaming
   • Output stream to the front-end side.
5. Pluggable visualization
   • Visualization can be pluggable
6. Pivot in a backend-side
   • Pivot runs in backend-side so large dataset can be transformed in backend-side before transfer to the front-end side.
7. Folder structure for notebook
   • Let's organize notebooks
8. Bring AngularDisplay feature from 'experimental' to 'beta'

0.5.0 - First release in Apache incubator