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Dataops - The WAT, The Pain, The How

Dataops - The WAT, The Pain, The How

A high-level introduction to DataOps and how to deploy or nail it within real organisations.


Xavier Bruhiere

August 22, 2019

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  1. DataOps The WAT The Pain The How

  2. Hey hi, I’m Xavier Bruhiere VP Data engineering @ Lazada

    And I AM HIRING Data and full stack engineers Shameless Plug
  3. 01. Terms, scope, context Agenda 02. Where it hurts 03.

  4. Building ETL What I’ve been told

  5. Building ETL What I do

  6. - Glossary, scope, definitions 01 Have you met DataOps

  7. “DevOps is the combination of cultural philosophies, practices, and tools

    that increases an organization’s ability to deliver applications and services at high velocity: evolving and improving products at a faster pace than organizations using traditional software development and infrastructure management processes.” - AWS:*What is DevOps?* DevOps
  8. “A collaborative data manager practice, really focused on improving communication,

    integration, and automation of data flow between managers and consumers of data within an organization,” - Gartner DataOps
  9. Orchestrate, monitor Cycle Velocity Sensitive information DataOps Long lasting Slow

    changing Stateful People Massive Same goals Data != Software Analytics iterations ML / Operations
  10. -Cost and pain of DevOps for Data 02 Diagnosis

  11. - Time to delivery and Reliability - Fast iterations -

    Safe iterations - Correctness - Cost down - Insight value Goal
  12. - Reproducibility? - Local sandbox of a warehouse? Staging? -

    Mocking source data - Complex, stateful DAGs But… About tooling About testing
  13. - QA Testing with non-tech-savy people - Gap between who

    request and own the domain, and who performs - Various backgrounds and expectations But… About security About people - Access management at the column level - GDPR - Data leakage - Anonymization
  14. |”Violent advices and slides ahead” | - me 03 Counter-measures

  15. Efficiency Automate data collection – capture changes

  16. Efficiency Functional data engineering - Maxime Beauchemin

  17. Safety DO TESTS – Start small, build up. - Maxime

  18. Quality Develop Data Governance

  19. Quality Statistical Monitoring

  20. People // Business Engineer shouldn’t write ETL - Jeff Magnuson

  21. 01. Ignore the buzz, capture automations to escape chaos Takeaway

    02. Listen to people 03. Hunt relentlessly bottlenecks and risks 05. Have faith 02-bis. No heroism – self-organizing team
  22. Questions?

  23. None
  24. Thanks!

  25. None