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What would you like to do?
Outline for Airflow Summit CFP



Common problems we face

* Custom code for data source. 1:1 mappings for source to destinations
* ETLs bottleneck, not fast as biz landscape changes are
* Depletion o f trust. Wrong decisions made, bad data in production
* Data pipelines blamed, wrong data produced by source
* Bugs in data transformations reaching production
* Debugging Pipelines slow
* Engineer Analyst hand-off painful
* No documentation of assumptions of our data



  • Tap - stream of records from a source
  • Target - data loading script. load it into a file, API or database.
  • Unix inspired
  • Any combination
  • Best practices are shipped
  • Code example

Great Expectations

  • Framework for specifying assumptions of datasaet
  • Works with pipelines: batch
  • Out of Box expectations
  • Code Example
  • Features explanation


  • SQL first transformation tool
  • Built for analysts
  • Testing and docs/catalog by dbt
  • Code example


  • Does what it is does bestL Orchestrate
  • Provide operators to invoke the above without pain
  • Future : declarative dag genration
  • Yaml Dag - Dag Factory
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