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DevOps in Analytics and Data Warehousing
Submitted by karthikeyan selvaraj (@kaarthikeyapreyan) on Wednesday, 21 March 2018
Technical level: Intermediate
Products & Data DevOps - Handling them in different Angles
How did we Solve our Problem in a space that grows over 80 TB a month?
The Ecosystem that encouraged the platform evolution
Diversity in the two realms - Data and Application
The Design that solves over 200 + Analytics and Data warehousing requests in a day
- Service Abstractions
- Cognitive Leaning
- Application Lineage
- Service Handlers
- Metadata Managers
Supporting Ecosystems Applicaiton and Tools
Fundamental exposure to Warehousing applications is desired.
Open source Enthusiasist doing software development for over a decade, Loves to spend all day on building open source products. Currently Leading the DevX Space for Data Technology in PayPal. Experince in infecting people with ideas in PyCon and Scipy conferences.