Automated Data Operations and Orchestration Training Course

 Automated Data Operations and Orchestration Training Course 

Introduction 

Automated Data Operations and Orchestration Training Course is designed to equip professionals with advanced knowledge and practical skills in modern data engineering, workflow automation, and enterprise data management. The course explores automated data pipelines, data orchestration frameworks, workflow scheduling, data integration, Extract, Transform, Load (ETL), Extract, Load, Transform (ELT), real-time data processing, and cloud-based data operations. Participants will develop competencies in managing complex data workflows, automating repetitive processes, optimizing data pipeline performance, and implementing scalable data infrastructure using modern technologies such as Apache Airflow, Prefect, Dagster, Kubernetes, Apache Kafka, and cloud data platforms. 

The training emphasizes hands-on learning, enterprise automation strategies, data reliability engineering, monitoring, and intelligent workflow management. Participants will explore dependency management, automated testing, error handling, pipeline observability, data governance, and continuous integration and continuous deployment (CI/CD) for data systems. Through practical exercises and global industry case studies, learners will understand how organizations leverage automated data operations to improve operational efficiency, accelerate analytics delivery, reduce infrastructure costs, and support data-driven decision-making across complex business environments. 

Course Objectives 

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

1. Design scalable automated data operations architectures.
2. Develop and manage automated data pipelines.
3. Implement enterprise data orchestration frameworks.
4. Apply ETL and ELT automation techniques.
5. Configure workflow scheduling and dependency management.
6. Integrate cloud-native data engineering technologies.
7. Implement real-time data processing workflows.
8. Apply data quality monitoring and validation.
9. Optimize pipeline performance and resource utilization.
10. Implement automated error handling and recovery.
11. Apply CI/CD practices to data engineering.
12. Strengthen data security and governance frameworks.
13. Develop reliable and observable data operations environments.

Organizational Benefits
 

1. Reduced manual data processing and operational costs.
2. Improved enterprise data integration capabilities.
3. Increased productivity through workflow automation.
4. Faster delivery of business intelligence and analytics.
5. Improved data accuracy and consistency.
6. Enhanced scalability of data infrastructure.
7. Reduced pipeline failures and operational disruptions.
8. Stronger data governance and regulatory compliance.
9. Improved collaboration between data engineering teams.
10. Better decision-making through timely data availability.

Target Audiences
 

1. Data engineers and data architects.
2. Database administrators and developers.
3. Business intelligence professionals.
4. Data scientists and analytics specialists.
5. Cloud computing and DevOps engineers.
6. IT infrastructure and operations managers.
7. Enterprise technology consultants.
8. Data platform and systems administrators.

Course Duration: 10 days
 
Course Modules

Module 1: Fundamentals of Automated Data Operations
 

• Principles of data operations automation.
• Modern data engineering architectures.
• Data pipeline lifecycle management.
• Automation strategies and operational workflows.
• Enterprise data operations frameworks.
• Case study: Netflix automated data processing infrastructure.

Module 2: Data Pipeline Architecture and Development
 

• Pipeline architecture and design principles.
• Batch and streaming data pipelines.
• Source-to-destination data integration.
• Pipeline scalability and modular development.
• Data transformation workflows.
• Case study: Uber's large-scale data pipeline architecture.

Module 3: Data Orchestration Frameworks
 

• Introduction to Apache Airflow, Prefect and Dagster.
• Directed Acyclic Graph (DAG) development.
• Workflow orchestration architecture.
• Task execution and dependency configuration.
• Orchestration monitoring and management.
• Case study: Airbnb's adoption of Apache Airflow.

Module 4: ETL and ELT Process Automation
 

• Automated data extraction techniques.
• Data transformation and loading workflows.
• ETL versus ELT architecture.
• Integration with enterprise databases.
• Incremental loading and change data capture.
• Case study: Modern cloud data warehouse automation at Snowflake.

Module 5: Workflow Scheduling and Dependency Management
 

• Automated scheduling strategies.
• Task dependencies and execution sequencing.
• Cron-based workflow scheduling.
• Event-driven workflow execution.
• Parallel processing and task optimization.
• Case study: Spotify's automated data processing workflows.

Module 6: Cloud-Based Data Operations
 

• Cloud data engineering architectures.
• AWS Glue and Azure Data Factory.
• Google Cloud Dataflow and BigQuery.
• Cloud storage and processing integration.
• Infrastructure scalability and cost optimization.
• Case study: Google's cloud-based data processing ecosystem.

Module 7: Real-Time Data Processing and Streaming
 

• Streaming data architecture fundamentals.
• Apache Kafka and event-driven processing.
• Real-time ingestion and transformation.
• Stream processing and event management.
• Monitoring streaming pipeline performance.
• Case study: LinkedIn's large-scale Kafka infrastructure.

Module 8: Data Quality Automation and Validation
 

• Automated data quality assessment.
• Data profiling and anomaly detection.
• Schema validation and consistency checks.
• Great Expectations and data testing.
• Data observability and quality reporting.
• Case study: Monte Carlo data observability implementations.

Module 9: Pipeline Monitoring and Performance Optimization
 

• Pipeline performance monitoring.
• Execution logs and operational metrics.
• Bottleneck identification and resolution.
• Resource allocation and workload optimization.
• Alerting and incident management.
• Case study: Google's monitoring and reliability engineering practices.

Module 10: Automated Error Handling and Recovery
 

• Pipeline failure detection mechanisms.
• Retry policies and recovery strategies.
• Exception handling and fault tolerance.
• Workflow rollback and restart procedures.
• Automated incident response.
• Case study: Amazon's resilient distributed data operations.

Module 11: DevOps and CI/CD for Data Operations
 

• DataOps principles and methodologies.
• CI/CD pipeline implementation.
• Git-based workflow management.
• Automated testing and deployment.
• Version control and release management.
• Case study: GitLab's automated software delivery practices.

Module 12: Containerization and Workflow Deployment
 

• Docker fundamentals for data pipelines.
• Kubernetes orchestration environments.
• Containerized workflow deployment.
• Microservices and distributed processing.
• Infrastructure automation.
• Case study: Spotify's containerized infrastructure practices.

Module 13: Data Security and Governance Automation
 

• Enterprise data security frameworks.
• Identity and access management.
• Data encryption and access controls.
• Automated compliance monitoring.
• Data lineage and audit management.
• Case study: Financial-sector automated data governance.

Module 14: Advanced Automation and Intelligent Operations
 

• AI-powered workflow optimization.
• Machine learning pipeline automation.
• Predictive failure detection.
• Intelligent resource allocation.
• Automated operational decision-making.
• Case study: Google's machine learning operations ecosystem.

Module 15: Enterprise Implementation and Capstone Project
 

• Designing an enterprise automation architecture.
• Developing an end-to-end orchestration pipeline.
• Integrating cloud and on-premises systems.
• Testing, monitoring and deployment.
• Evaluating operational performance and scalability.
• Case study: Building an automated enterprise data platform inspired by Netflix and Uber.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical exercises using modern data orchestration tools.
• Hands-on pipeline development and configuration.
• Group discussions and international industry case studies.
• Real-world enterprise automation simulations.
• Capstone projects, assessments and performance evaluations.

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.