DataOps on Microsoft Azure Training Course

 DataOps on Microsoft Azure Training Course 

Introduction 

DataOps on Microsoft Azure Training Course is designed to equip professionals with advanced knowledge and practical skills in implementing, managing, and optimizing modern data operations using Microsoft Azure cloud technologies. The course explores DataOps principles, data pipeline automation, cloud data engineering, continuous integration and continuous delivery (CI/CD), data orchestration, data governance, and automated data quality management. Participants will gain practical experience using Azure Data Factory, Azure Databricks, Microsoft Fabric, Azure Synapse Analytics, Azure DevOps, and Azure Data Lake Storage to build scalable, secure, and reliable data platforms. Emphasis is placed on agile data management, workflow automation, infrastructure as code, real-time data processing, metadata management, and end-to-end data lifecycle optimization. 

The training also examines how organizations can leverage DataOps frameworks to accelerate analytics delivery, improve collaboration between data engineers and business teams, and establish reliable data-driven decision-making processes. Through practical exercises and international case studies, participants will explore automated testing, data observability, version control, deployment pipelines, performance monitoring, and cloud-native data architecture. The course incorporates industry best practices and enterprise implementation scenarios to help participants develop scalable DataOps strategies, strengthen operational resilience, reduce data delivery bottlenecks, and maximize the value of Microsoft Azure investments. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand DataOps principles, agile methodologies, and modern data engineering practices.
2. Design scalable DataOps architectures using Microsoft Azure services.
3. Automate data pipelines using Azure Data Factory and Azure Databricks.
4. Implement CI/CD workflows for data engineering and analytics solutions.
5. Apply automated data testing, validation, and quality assurance.
6. Manage data orchestration and workflow automation across cloud environments.
7. Implement version control using Git and Azure DevOps.
8. Apply infrastructure as code and automated deployment strategies.
9. Establish data governance, security, and regulatory compliance frameworks.
10. Implement data observability, monitoring, and performance optimization.
11. Integrate real-time data processing and streaming technologies.
12. Apply metadata management, data lineage, and lifecycle management.
13. Develop enterprise DataOps strategies for continuous improvement and operational efficiency.

Organizational Benefits
 

1. Accelerates enterprise data delivery and analytics implementation.
2. Reduces operational costs through automated data workflows.
3. Improves data accuracy, consistency, and reliability.
4. Strengthens collaboration between development and data engineering teams.
5. Enhances cloud infrastructure scalability and operational resilience.
6. Improves regulatory compliance and enterprise data security.
7. Minimizes pipeline failures through automated testing and monitoring.
8. Supports faster business intelligence and informed decision-making.
9. Optimizes resource utilization across Microsoft Azure environments.
10. Promotes continuous innovation and agile digital transformation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. DataOps engineers and platform specialists.
3. Business intelligence and analytics professionals.
4. Azure administrators and cloud engineers.
5. DevOps engineers and software developers.
6. Database administrators and data integration specialists.
7. IT managers and enterprise data governance professionals.
8. Technical consultants and digital transformation specialists.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of DataOps and Microsoft Azure
 

• DataOps concepts, principles, frameworks, and implementation strategies.
• Agile data engineering, DevOps integration, and collaborative workflows.
• Microsoft Azure architecture and cloud-native data services.
• Data lifecycle management and enterprise data operations.
• DataOps maturity models and organizational adoption.
• Case study: Netflix's data engineering and continuous analytics delivery practices.

Module 2: Azure Data Engineering and Pipeline Development
 

• Designing automated data pipelines using Azure Data Factory.
• Data ingestion, transformation, and integration techniques.
• Implementing Azure Data Lake Storage architectures.
• Batch processing and incremental data loading.
• Pipeline dependencies, triggers, and scheduling.
• Case study: Uber's large-scale data ingestion and processing architecture.

Module 3: DataOps Automation and Orchestration
 

• Automating workflows using Azure Data Factory.
• Data orchestration with Azure Databricks and notebooks.
• Implementing event-driven data processing.
• Managing workflow dependencies and execution scheduling.
• Integrating APIs and external enterprise data sources.
• Case study: Microsoft enterprise data workflow automation practices.

Module 4: CI/CD and Azure DevOps for Data Engineering
 

• Implementing CI/CD pipelines for data solutions.
• Git repositories, branching strategies, and version control.
• Automated deployment across development, testing, and production.
• Managing release pipelines and environment configurations.
• Infrastructure as code using Azure Resource Manager and Bicep.
• Case study: Capital One's cloud DevOps and automated deployment practices.

Module 5: Data Quality, Testing, and Validation
 

• Implementing automated data quality assessment frameworks.
• Data profiling, validation, and anomaly detection.
• Unit testing and integration testing for data pipelines.
• Managing schema changes and data consistency.
• Implementing automated error handling and recovery.
• Case study: Airbnb's data quality and reliability engineering practices.

Module 6: Data Governance, Security, and Compliance
 

• Implementing Azure security and identity management.
• Data governance using Microsoft Purview.
• Metadata management, cataloguing, and data lineage.
• Role-based access control and sensitive data protection.
• Regulatory compliance, auditing, and data retention.
• Case study: HSBC's enterprise data governance and regulatory compliance practices.

Module 7: Data Observability and Performance Optimization
 

• Implementing data monitoring and observability frameworks.
• Azure Monitor, Log Analytics, and application insights.
• Identifying pipeline failures and performance bottlenecks.
• Optimizing storage, compute, and processing resources.
• Implementing automated alerts and incident management.
• Case study: LinkedIn's large-scale data monitoring and operational reliability practices.

Module 8: Advanced DataOps Implementation and Capstone Project
 

• Integrating Microsoft Fabric, Azure Databricks, and Synapse Analytics.
• Implementing real-time analytics and streaming data pipelines.
• Designing scalable enterprise DataOps architectures.
• Developing DataOps implementation roadmaps and KPIs.
• Capstone project: Building an automated Azure data pipeline with CI/CD, quality testing, governance, and monitoring.
• Case study: Walmart's cloud-based data analytics and enterprise data integration practices.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Microsoft Azure cloud environments.
• Hands-on exercises involving data pipelines and workflow automation.
• Group projects and collaborative DataOps architecture development.
• International enterprise case studies and implementation scenarios.
• Capstone projects, technical assessments, and participant feedback.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.