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( 10 Students )
( 10 Students )
DataOps Simplified
Learn how to streamline your data workflows, automate pipelines, and improve collaboration across the data lifecycle with DataOps principles and tools
Course Duration: 10 Hours
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DataOps Simplified – Online Course
DataOps Simplified is a comprehensive, self-paced online course designed to demystify DataOps and make it approachable for data professionals, analysts, engineers, and IT managers. This course serves as your definitive guide to understanding, implementing, and optimizing DataOps practices to create high-quality, efficient, and agile data pipelines in modern data environments.
About the Course
Course Introduction
As businesses increasingly rely on data-driven decision-making, the demand for streamlined, automated, and collaborative data management processes has surged. DataOps, short for Data Operations, addresses this need by bringing DevOps-style agility to the world of data engineering and analytics.
As businesses increasingly rely on data-driven decision-making, the demand for streamlined, automated, and collaborative data management processes has surged. DataOps, short for Data Operations, addresses this need by bringing DevOps-style agility to the world of data engineering and analytics.
What is DataOps Simplified?
"DataOps Simplified" offers a step-by-step introduction to the DataOps methodology—an integrated approach to managing the data lifecycle, from ingestion and transformation to analytics and governance. This course explores foundational concepts such as pipeline automation, continuous integration and delivery (CI/CD) for data, environment management, quality control, testing, monitoring, and cross-functional collaboration.
"DataOps Simplified" offers a step-by-step introduction to the DataOps methodology—an integrated approach to managing the data lifecycle, from ingestion and transformation to analytics and governance. This course explores foundational concepts such as pipeline automation, continuous integration and delivery (CI/CD) for data, environment management, quality control, testing, monitoring, and cross-functional collaboration.
You’ll not only gain theoretical insights but also hands-on experience with modern tools such as Apache Airflow, dbt, Great Expectations, Jenkins, Git, and Kubernetes—all tailored for DataOps implementation.
How to Use This Course
This course is structured for learners at all levels. Whether you're a beginner with basic data knowledge or a professional looking to implement agile data pipelines, the course is designed to guide you through the principles and tools needed for a successful DataOps journey. To maximize learning:
This course is structured for learners at all levels. Whether you're a beginner with basic data knowledge or a professional looking to implement agile data pipelines, the course is designed to guide you through the principles and tools needed for a successful DataOps journey. To maximize learning:
- Start from the basics and build your way up to complex DataOps orchestration.
- Practice using real datasets and open-source tools to simulate real-world scenarios.
- Implement mini-projects after each module to reinforce learning and build a DataOps portfolio.
- Use documentation and tool guides introduced throughout the course to develop self-sufficiency.
- Engage with exercises, quizzes, and code labs embedded in each section.
By the end of this course, you'll understand how to bridge the gap between data engineering and data consumption while promoting automation, collaboration, and data reliability.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Explain the principles and lifecycle of DataOps.
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Set up automated and version-controlled data pipelines using CI/CD.
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Implement data quality testing and monitoring practices.
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Use tools like Apache Airflow, dbt, and Great Expectations effectively.
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Design modular, reusable, and observable data workflows.
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Apply agile practices to data engineering and analytics workflows.
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Integrate GitOps and DevOps with data pipeline development.
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Enable cross-functional collaboration between data engineers, analysts, and business users.
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Automate end-to-end deployment of analytics infrastructure.
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Create a resilient and scalable DataOps architecture using cloud-native tools.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to DataOps
- What is DataOps?
- History and evolution of DataOps
- Comparing DataOps, DevOps, MLOps
Module 2: DataOps Principles & Lifecycle
- Core principles of DataOps
- DataOps vs traditional data pipelines
- Stages of the DataOps lifecycle
Module 3: Source Control & CI/CD for Data
- Git and GitOps basics
- Version control for SQL, models, and data pipelines
- CI/CD pipelines with Jenkins and GitHub Actions
Module 4: Orchestrating Workflows with Apache Airflow
- DAGs and task dependencies
- Scheduling and retries
- Best practices for Airflow DAG design
Module 5: Data Transformation using dbt
- Writing modular SQL transformations
- Testing and documentation in dbt
- dbt Cloud vs dbt Core
Module 6: Data Quality & Testing
- Importance of data validation
- Great Expectations: setup and configuration
- Defining and automating tests
Module 7: Monitoring and Observability
- Key metrics for data pipeline monitoring
- Alerting and logging practices
- Using tools like Prometheus and Grafana
Module 8: Containerization and Deployment
- Docker basics for data pipelines
- Kubernetes for orchestration
- Deploying Airflow and dbt on Kubernetes
Module 9: DataOps in the Cloud
- Using AWS/GCP/Azure for DataOps pipelines
- Serverless and cloud-native services
- Cost optimization and scaling
Module 10: Agile Collaboration in Data Teams
- Feedback loops and sprint cycles
- Role of Product Owners, Analysts, and Engineers
- Creating shared documentation and dashboards
Modules 11–15: Hands-on Projects
- Sales Analytics Pipeline with dbt + Airflow
- Real-time Stock Data Pipeline with Kafka + Airflow
- Marketing Funnel with dbt + Great Expectations
- CI/CD for Data Pipelines with Jenkins
- End-to-End DataOps System on Kubernetes
Module 16: DataOps Interview Questions & Answers
Certification Back to Top
Upon successful completion of the DataOps Simplified course, learners will receive an industry-recognized Certificate of Completion from Uplatz. This certificate validates your proficiency in implementing modern DataOps practices and working with tools such as Apache Airflow, dbt, Great Expectations, and Jenkins. Whether you're applying for a Data Engineer, DevOps Engineer, or DataOps Specialist role, this certification helps demonstrate your practical, tool-based expertise alongside your understanding of agile principles and pipeline management. It acts as proof of both your theoretical knowledge and your applied skills, offering a competitive edge in job interviews and consulting roles.
Career & Jobs Back to Top
DataOps is one of the most in-demand skill sets in the data and analytics job market today. As organizations increasingly look to automate, scale, and improve the reliability of their data infrastructure, professionals skilled in DataOps enjoy lucrative career prospects.
By completing this course, you’ll be prepared for roles such as:
- DataOps Engineer
- Data Engineer
- Analytics Engineer
- DevOps Engineer (Data Focus)
- Cloud Data Platform Engineer
- Data Quality Analyst
Job opportunities exist across industries such as finance, e-commerce, healthcare, telecom, and logistics. You can work in startups that need rapid pipeline development, large enterprises with complex data environments, or consulting firms offering data solutions. Freelance and remote opportunities are also abundant for DataOps professionals. With expertise in DataOps, you can accelerate your career growth and become a key player in delivering data-driven innovation.
Interview Questions Back to Top
1. What is DataOps and how does it benefit organizations?
DataOps is an agile, process-oriented methodology for developing and delivering data pipelines. It enhances collaboration, automation, and reliability, reducing the time to insight and improving data quality.
DataOps is an agile, process-oriented methodology for developing and delivering data pipelines. It enhances collaboration, automation, and reliability, reducing the time to insight and improving data quality.
2. How is DataOps different from DevOps?
While DevOps focuses on software delivery, DataOps applies similar principles to the data lifecycle, emphasizing data validation, pipeline automation, and cross-functional collaboration.
While DevOps focuses on software delivery, DataOps applies similar principles to the data lifecycle, emphasizing data validation, pipeline automation, and cross-functional collaboration.
3. What tools are commonly used in DataOps?
Common tools include Apache Airflow for orchestration, dbt for transformation, Great Expectations for data testing, Jenkins for CI/CD, and Git for version control.
Common tools include Apache Airflow for orchestration, dbt for transformation, Great Expectations for data testing, Jenkins for CI/CD, and Git for version control.
4. How does CI/CD apply to data pipelines?
CI/CD enables automated testing, validation, and deployment of data workflows—ensuring faster, more reliable releases and reducing human errors.
CI/CD enables automated testing, validation, and deployment of data workflows—ensuring faster, more reliable releases and reducing human errors.
5. What is the role of Apache Airflow in DataOps?
Apache Airflow manages and schedules workflows as DAGs, enabling the orchestration of complex data pipelines with dependencies, retries, and alerts.
Apache Airflow manages and schedules workflows as DAGs, enabling the orchestration of complex data pipelines with dependencies, retries, and alerts.
6. What are the key components of a DataOps pipeline?
Components include data ingestion, transformation, testing, monitoring, deployment, and version control—all automated and managed collaboratively.
Components include data ingestion, transformation, testing, monitoring, deployment, and version control—all automated and managed collaboratively.
7. How does dbt help in DataOps practices?
dbt enables modular SQL transformation, testing, and documentation. It encourages version control and automated deployment of analytics code.
dbt enables modular SQL transformation, testing, and documentation. It encourages version control and automated deployment of analytics code.
8. Why is data testing important in DataOps?
Data testing ensures the accuracy, completeness, and consistency of data flowing through pipelines, preventing bad data from reaching analytics layers.
Data testing ensures the accuracy, completeness, and consistency of data flowing through pipelines, preventing bad data from reaching analytics layers.
9. How do you monitor a DataOps pipeline?
Using tools like Prometheus, Grafana, or Airflow logs, you monitor task status, data freshness, failures, and anomalies across the pipeline.
Using tools like Prometheus, Grafana, or Airflow logs, you monitor task status, data freshness, failures, and anomalies across the pipeline.
10. What challenges can arise in implementing DataOps?
Common challenges include tool integration, team silos, cultural resistance to change, lack of observability, and insufficient data quality practices.
Common challenges include tool integration, team silos, cultural resistance to change, lack of observability, and insufficient data quality practices.
Course Quiz Back to Top
FAQs
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