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Fundamentals of DataOps

This course introduces you to DataOps basics, including its origins, components, real-life applications, and ways to implement it. Skills Gained Understand the fundamentals of DataOps and how people...

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

This course introduces you to DataOps basics, including its origins, components, real-life applications, and ways to implement it.

Skills Gained

  • Understand the fundamentals of DataOps and how people collaborate to deliver data for specific purposes.
  • Learn the three standard DataOps pipelines (Production, Development, and Environment) and how to orchestrate the necessary teams, tools, and processes
  • Test, measure, and iteratively improve DataOps production pipelines
  • Structure the development pipeline to fit the development lifecycle and use it to achieve fast deployments
  • Build environment pipelines, manage components, and adapt the environment to different use cases
  • Apply Lean DataOps to improve your organization’s data operations

Who Can Benefit

  • Data and Business Analysts
  • Information Architects
  • Technical Managers


General knowledge of programming and data processing.

Course Details


DataOps Introduction

  • Data Analytics On the Run
  • Impediments to the Data Analytics Cycle Time
  • Finding a Solution ...
  • What is DataOps?
  • Agile Development ...
  • DevOps
  • The DataOps Technology and Methodology Stack
  • The DataOps and Data Science Relationship
  • DataOps Relationships with Other Data Management Disciplines and Concerns
  • Standing Up a DataOps Practice
  • The Lean Manufacturing Methodology
  • Statistical Process Control
  • What is Six Sigma?
  • DataOps Enterprise Data Technologies
  • The DataOps Manifesto
  • Problems that DataOps Solves
  • DataOps Leadership Principles

The DataOps Problem Domain

  • Connecting to the Digital Realm ...
  • Data is King
  • Actionable Insights
  • Snowflake Environments
  • Data Observability
  • Cloud Resource Monitoring Dashboards
  • Fragmented Data Sources
  • Data Formats
  • Interoperable Data
  • The Data-Related Roles
  • What is Data Engineering
  • The Typical Data Analytics (Machine Learning) Pipeline
  • IT Systems' Woes
  • Types of Architecture
  • How to Lead with Data (the "Fidelity Way" *)
  • How to Lead with Data: Ownership
  • How to Lead with Data: Shared Environment Security Controls
  • How to Lead with Data: the Current Trends
  • DataOps Functional Architecture
  • Key Components of a DataOps Platform
  • Automation
  • Maintenance
  • DataOps Data Pipelines
  • Building Pipelines: Aggregating System DAGs
  • Distributed Data Flow Challenges
  • Promoting Teamwork
  • The Tragedy of the (Unmanaged) Commons
  • Tests in Data Analytics
  • Test Types
  • The Netflix Simian Army Test Suite
  • Input Data "Irregularities"
  • Dealing with Missing Data in Python

DataOps Technology and Tools

  • Data Storage System Types
  • The CAP Theorem
  • The CAP Triangle - Which Storage System to Choose
  • Mechanisms to Guarantee a Single CAP Property
  • Data Physics (a.k.a Distributed Data Economics)
  • Hadoop: Example of Collocating Data and Computation
  • An Example of Hive DDL
  • Efficient Storage with Columnar Formats
  • Example: AWS Athena Storage and Processing Cost Savings
  • Example: Converting the CSV Data Format into Parquet Using HiveQL CTAS Statement
  • The Cloud: Value Proposition
  • Lessons from the Field
  • Design for System Resiliency
  • How eBay Preempts Possible Database Corruption
  • Cloud Data Services
  • The Cloud Strategy
  • Virtualization
  • Virtualization Benefits
  • What is Docker
  • What is Kubernetes
  • Computing Services in the Cloud
  • Get Educated ...
  • "Good/Not so Good" Use Cases for the Cloud
  • Infrastructure as Code (IaC)
  • Example of Provisioning and Running a PostgreSQL Database in Docker
  • IoC Systems and Tools
  • Workflow (Pipeline) Orchestration Systems
  • Example of a Workflow Orchestration System: Apache NiFi
  • NiFi Processor Types
  • Building a Simple Data Flow in the NiFi Designer
  • An Annotated Example of Using scikit-learn Python Machine Learning (ML) Pipeline Class
  • Version Control Systems
  • Branching and Merging Visually
  • Some Popular Version Control Systems
  • Overview of DataOps Tools and Services

IT Governance

  • IT Governance
  • Data Governance
  • Controlling the Decision-Making Process
  • Enterprise IT Governance Models
  • Key Artifacts
  • Agile IT
  • Types of System Requirements
  • Scoping Requirements
  • Requirements Gathering ...
  • Data Governance Overview
  • Data Governance Roles and Responsibilities
  • Roles and Responsibilities in DataOps
  • Example of Assigning Responsibilities (AWS Shared Responsibility Model)
  • Example of a Governance-Enabling Service
  • Governance Best Practices
  • Governance Gotchas
  • The Goldilocks Principle

Lab Exercises

  • Lab 1. Data Availability and Consistency
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