• what does your company do?

  • Tell me about your background. What did you specialise in? Where did you work before? What did you do there?

    • developer advocate, previously engineer + data engineering

  • standard ways of using it

  • what problems does GE solve?

    • data quality
      • wrong data (keyboard mistakes)
      • wrong assumptions (taxi fares always negative?)
      • data drift (assumptions had been correct, but they changed)
    • GE is for monitoring data quality + documenting it
    • GE is specifically human-in-the-loop
    • competitors:
      • montecarlo
      • self-rolling
      • anomalo
      • bigeye

  • process of launching cloud version

  • trying to make it easier to use

  • some sort of research adoption

  • city of prague + some government office in brazil adopted GE

  • what role will cloud play?

    • cloud will manage validation results / expectation suites / data source
    • connect data sources + point and click

  • what problems do companies that use great expectations tend to have?

    • initial setup is the biggest challenge
    • a lot of people find errors in their data within a week

  • do you see a lot of non-engineering-heavy companies adopt GE?

    • รก la netcheck, research
    • are you interested in that?

  • GE is only relevant for evolving datasets, right?

    • nope: GE is relevant everytime you communicate about data

  • are there plans on making GE easier to use?

    • there was discussion: https://discuss.greatexpectations.io/t/a-super-simple-alternative-introduction-to-great-expectations/27/6
    • that was in 2020, nothing seems to have changed
      • actually, a few things happened:
        • v3 API
        • made it easier to add custom expectations
        • very successful hackathon

  • can you tell me some typical datasets that are used with GE?

  • who should I talk to next?