DataOps (Data Operations) is a process-oriented methodology and a set of tools aimed at supporting the logistical needs of data analytics teams through the entire "cradle-to-grave" data lifecycle (from data acquisition to storing, to processing, to retiring obsolete data). The primary DataOps' objective is to shorten the "time-to-insight" cycle compared to the usual expectations of traditional data warehouse environments. While not rooted in any particular technology, where it is fitting, DataOps leverages the toolchains, methods, and ideas of Data Engineering, DevOps, Agile, and Lean Manufacturing.
This training course provides an overview of DataOps: the related concepts, terminology, methodology, and technologies.
Data and business analysts, information architects, and technical managers.
Participants are expected to have a general knowledge of programming and data processing.
Outline for Fundamentals of DataOps Training
Chapter 1. Intro to DataOps
- Problems in the Data & Analytics Industry
- Root Cause: Organizational Complexities
- Solution: What Is DataOps?
Chapter 2. DataOps Production Pipeline
- The Three DataOps Pipelines
- Meta-Orchestrate Tools, Teams & Processes
- Automate Tests for Error Detection
- Types of Tests
- Measure Production Processes, Reflect & Improve
Chapter 3. DataOps Development Pipeline
- Development Lifecycle Complexities
- Data & Analytics Development
- How to Achieve Fast Deployments
- DataOps Deployments: Beyond DevOps
Chapter 4. DataOps Environment Pipeline
- DataOps Environment Challenges
- Environment Management: Components & Use Cases
- Principles of DataOps Environments
Chapter 5. DataOps Implementation
- Lean DataOps Implementation
- Four Phases of Lean DataOps
- Getting started with DataOps