In this course, students will learn the principles and practices of DataOps, a set of techniques and tools used to streamline and optimize the process of collecting, processing, and analyzing data. Students will learn about the different stages of the DataOps lifecycle, including data ingestion, data processing, data analysis, and data delivery, and will gain hands-on experience with tools and techniques used in each stage.

Course Information

Difficulty: Beginner

Categories:

Lesson 1: Introduction to DataOps

  • Overview of DataOps
  • Benefits of DataOps
  • DataOps lifecycle
  • Key concepts in DataOps

Lesson 2: Data Ingestion

  • Data ingestion best practices
  • Data ingestion tools and technologies
  • Data ingestion challenges and solutions

Lesson 3: Data Processing

  • Data processing best practices
  • Data processing tools and technologies
  • Data processing challenges and solutions

Lesson 4: Data Analysis

  • Data analysis best practices
  • Data analysis tools and technologies
  • Data analysis challenges and solutions

Lesson 5: Data Delivery

  • Data delivery best practices
  • Data delivery tools and technologies
  • Data delivery challenges and solutions

Lesson 6: Hands-on Exercises

  • Hands-on exercises on data ingestion, processing, analysis, and delivery
  • Students will work on a real-world data project using DataOps techniques and tools

Lesson 7: Case Studies

  • Case studies of companies that have successfully implemented DataOps
  • Analysis of the challenges faced and solutions implemented

Course Instructor

Ardent XR Ardent XR Author

Introduction to DevOps

Continuous integration

Continuous delivery

Infrastructure as Code

DevOps Metrics and Monitoring

DevOps Culture and Collaboration

DevOps Tools and Pipelines

DevOps in practice