Complete DataOps Mastery: Build Automated Data Pipelines

Name: Complete DataOps Mastery: Build Automated Data Pipelines
Rating: 4.1 (9 reviews)

Master DataOps fundamentals, automation tools, and CI/CD practices to design robust data pipelines, implement quality

Created byMeta Brains

Last updated 8/2025

English

What you'll learn

Master DataOps principles and implement automated data pipeline architectures for organizational data workflows
Design and deploy CI/CD processes for data projects using industry-standard tools like Apache Airflow, Kafka, and Flink
Establish comprehensive data governance frameworks including quality checks, monitoring, and compliance with GDPR/HIPAA standards
Build cross-functional collaboration strategies and agile methodologies for effective DataOps team management
Implement version control systems for both data and code using Git, DVC, and automated metadata management practices
Configure advanced monitoring and observability solutions using Prometheus, Grafana, and real-time alerting systems
Develop cloud-native DataOps solutions across AWS, Azure, and GCP platforms with scalable architecture design
Execute end-to-end capstone project creating automated data quality monitoring pipeline for e-commerce applications

Course content

10 sections • 42 lectures • 5h 25m total length

What is DataOps?8:22
Role of DataOps in Data-Driven Organizations8:52
Challenges in Traditional Data Management7:58
Core Principles and Benefits of DataOps11:12
Mini Project: Identify key pain points in a provided dataset pipeline7:46

The DataOps Lifecycle: Overview and Stages8:17
Batch vs. Real-Time Data Pipelines18:50
Integration of DevOps Practices into Data Workflows7:28
Integrate DevOps principles into data workflows to automate data pipelines, enable continuous integration and delivery, and boost collaboration, quality, and scalability.
Automating Data Lineage and Metadata Management7:48
Mini Project: Build a basic batch pipeline for structured data5:35

Overview of Essential DataOps Tools8:05
Version Control for Data and Code (Git, DVC)8:22
Track code, datasets, configurations, and metadata with Git and DVC to build reproducible, scalable dataops pipelines.
Real-Time Data Processing with Kafka and Flink6:35
Master real time data processing with Kafka and Flink to build end-to-end streaming workflows, enabling low latency analytics, stateful processing, and insights for fraud detection and stock market analysis.
Visualization and Monitoring with Prometheus and Grafana8:57
Monitor DataOps pipelines with Prometheus and Grafana to collect real-time metrics, visualize dashboards, and set alerts that prevent silent failures and improve pipeline reliability.

Agile and Iterative Approaches in DataOps8:56
Managing Team Roles and Responsibilities10:26
Communication Tools and Techniques for DataOps Teams9:16
Cross-Functional Collaboration Between Teams8:43
Align cross-functional data ops by defining roles for data engineers, data scientists, DevOps, and business analysts, syncing goals, using collaborative tools, and holding regular check-ins to deliver automated pipelines.
Simulate collaboration between "team members" to plan a data workflow12:51
Simulate cross-functional collaboration to plan a data workflow in a dataops environment, defining roles, assigning tasks, and aligning the pipeline with business goals.

Summary of Key Learning Outcomes3:13
Future Trends and Opportunities in DataOps3:27
Discover the future of dataops by uniting dataops with ml ops, embracing cloud native and serverless architectures, enabling real-time ai-driven automation, and prioritizing privacy with gdpr and ccpA.
Recommendations for Continued Learning and Skill Development4:05
Advance dataops by hands-on cloud work with AWS, Azure, and Google Cloud, mastering data pipelines, automation with Airflow and Terraform, and MLOps with Kubeflow, MLflow, and Seldon.
Closing Remarks1:45

Requirements

Basic understanding of databases and SQL fundamentals for data manipulation and querying
Familiarity with command line interface and basic programming concepts (any language background helpful)
Access to computer with internet connection for hands-on exercises and cloud platform trials
No prior DataOps or DevOps experience required - course designed for beginners to intermediate learners
Willingness to learn new tools and technologies through guided practical exercises

Description

Disclosure: This course contains the use of artificial intelligence.

Transform your data management approach with this comprehensive DataOps course designed for data professionals, engineers, and analysts ready to revolutionize their workflows. DataOps is the game-changing methodology that bridges the gap between data teams and operational excellence, combining DevOps principles with data-specific practices to create reliable, scalable, and automated data pipelines.

In this hands-on course, you'll master the complete DataOps lifecycle from foundational concepts to advanced implementation strategies. You'll learn to design and build automated data pipelines using industry-standard tools like Apache Airflow, Kafka, and Flink, while implementing robust monitoring and observability practices with Prometheus and Grafana.

The course covers essential DataOps components including continuous integration and deployment (CI/CD) for data workflows, version control for both data and code using Git and DVC, and real-time data processing techniques. You'll dive deep into data governance, quality assurance, and compliance frameworks while mastering collaboration strategies that enable cross-functional teams to work seamlessly together.

Through practical mini-projects and a comprehensive capstone project, you'll implement an end-to-end DataOps pipeline for e-commerce data quality monitoring. You'll also explore cutting-edge topics like MLOps integration, cloud-native solutions across AWS, Azure, and GCP, and advanced observability techniques for complex data environments.

Whether you're looking to optimize existing data operations or build DataOps capabilities from scratch, this course provides the practical skills, strategic insights, and hands-on experience needed to drive organizational transformation and deliver reliable, high-quality data solutions at scale.

Who this course is for:

Data engineers seeking to modernize workflows and implement automated pipeline management systems
Database administrators wanting to adopt DevOps practices and improve data reliability and governance
Business analysts and data scientists looking to streamline data preparation and quality assurance processes
IT professionals transitioning into data roles who need comprehensive DataOps methodology training
Software developers interested in expanding skills into data operations and pipeline automation
Team leads and managers responsible for building data-driven organizational capabilities and cross-functional collaboration

Complete DataOps Mastery: Build Automated Data Pipelines

What you'll learn

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Course content

Introduction to DataOps5 lectures • 44min

Building Blocks of DataOps5 lectures • 48min

DataOps Framework and Tools4 lectures • 34min

DataOps Strategy for Organizations3 lectures • 32min

Hands-On DataOps Tools and Implementation4 lectures • 32min

Data Governance and Quality in DataOps4 lectures • 32min

Collaboration and Teamwork in DataOps5 lectures • 50min

Capstone Project4 lectures • 20min

Advanced Topics in DataOps4 lectures • 21min

Conclusion4 lectures • 13min

Requirements

Description

Who this course is for: