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DevOps in Analytics and Data Warehousing

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  1. 1. DevOps in Data Warehousing & Analytics PERSPECTIVE AND APPROACH KARTHIKEYAN SELVARAJ • MAY 2018
  2. 2. Data Vs Applications  Define  IDE  Process  Build  Repo Management  Triggers  Integrations  Bindings  Test  Deploy  Monitor
  3. 3. Data DevOps Design & Approach Cognitive Learning Service Abstraction Data Application Lineage Metadata Managers Orchestration  Management Systems  Process Integration  Workflow Orchestration  Learning Integration  Process Recommendations  Application Proposal  Lineage  Data  Application  Abstractions  Service  Delivery  Self-Service Orchestration  Development  Operations  Administration  Pre-Live  Batch Mode  Live Mode
  4. 4. Ecosystem Business Process Managers – Rally, Jira, Confluence Metadat a Handlers Integrators and Validators Service Abstractors – Middleware and Orchestration Application Core – DW, Informatica, MicroStrategy, Tableau… Infrastructure – OpenStack, GCP
  5. 5. “ ” Why the Crisp Talk … Open Source Product on its way Thoughts and Questions ?

DevOps in Analytics and Data Warehousing

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