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Fundamentals of DataOps

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...

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$810 USD
Course Code WA3219
Duration 1 day
Available Formats Classroom, Virtual

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.

Who Can Benefit

Data and business analysts, information architects, and technical managers.

Prerequisites

Participants are expected to have a general knowledge of programming and data processing.

Course Details

Outline

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
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