This document discusses applying DevOps principles and practices to data warehousing and analytics. It proposes a cognitive learning service that abstracts data and applications to enable self-service orchestration across the development, operations, and administration lifecycles. Key components include business process managers, metadata handlers, integrators, service abstractors using middleware and orchestration, and infrastructure platforms. The approach aims to streamline processes from definition to delivery for both data and applications.

1. DevOps in Data
Warehousing & Analytics
PERSPECTIVE AND APPROACH
KARTHIKEYAN SELVARAJ • MAY 2018

2. Data Vs Applications
 Define
 IDE
 Process
 Build
 Repo Management
 Triggers
 Integrations
 Bindings
 Test
 Deploy
 Monitor

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. 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. “
”
Why the Crisp Talk … Open
Source Product on its way
Thoughts and Questions ?