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Making Data Science Work with Dataops Practices
Agenda● Big Data is “dead”● Data Culture● Data Management● Agile Data Management
Big Data is dead.
Making analytics products is a teamwork now.
Data Democratization requires Data Culture
3 types of data culture
Helps making decisions
Data & Context are starting points for making a decision
Data helps strategic goals
Data culture is about
Data culture is about
Data culture is about
We need to manage the data
Why Data Management● Data Quality management => no “garbage in”
● Data Stewards institute
● DWH and infrastructure development
● Collaboration with business users => data becomes an asset
● Creates and develops standards of interacting with data
DAMA DMBOK & Classic methodology
Agile Data Management
DataOps Manifesto
http://dataopsmanifesto.org
Whether referred to as data science, data engineering, data management, big data, business intelligence, or the like, through our
work we have come to value in analytics:
Individuals and interactions over processes and tools
Working analytics over comprehensive documentation
Customer collaboration over contract negotiation
Experimentation, iteration, and feedback over extensive upfront design
Cross-functional ownership of operations over siloed responsibilities
Dataops New Roles
Data Librarian
Data Journalist
Data Analyst
Data Engineer
Data Steward
Data Architect
DataOps practices
● Data Platform unifies the data
● Data Governance
● Self-service
● Testing & Code Review
● CI/CD & Containerization
● Different Environments & Git
● Collaboration & Knowledge Sharing
About meLead Big Data architect in MTS
● HSE Big Data programme grad● 5+ years in DWH dev&ops● 2 years in Big Data
Thank you!
