Data Platform Reliability Engineering Training Course

 Data Platform Reliability Engineering Training Course 

Introduction 

Data Platform Reliability Engineering Training Course provides comprehensive knowledge and practical skills for designing, implementing, monitoring, and maintaining reliable, scalable, and resilient modern data platforms. The course explores Site Reliability Engineering (SRE), DataOps, data infrastructure management, cloud-native architectures, automated data pipeline monitoring, fault tolerance, disaster recovery, and performance optimization. Participants will learn how to apply reliability engineering principles to enterprise data ecosystems, including data warehouses, data lakes, lakehouse architectures, streaming platforms, and distributed data processing environments. Emphasis is placed on data availability, data quality, service-level objectives (SLOs), service-level indicators (SLIs), service-level agreements (SLAs), and automated incident management. 

The training also examines modern technologies and industry practices for improving data platform observability, operational efficiency, security, and business continuity. Through practical exercises and global case studies, participants will explore how organizations use predictive monitoring, infrastructure automation, capacity planning, continuous integration and continuous delivery (CI/CD), and reliability testing to minimize downtime and improve data service performance. Examples from Amazon, Netflix, Google, and financial institutions will demonstrate how reliability engineering supports mission-critical data operations. Participants will develop actionable strategies for building dependable data platforms that support artificial intelligence, machine learning, business intelligence, advanced analytics, and enterprise digital transformation. 

Course Objectives 

• Understand data platform reliability engineering principles and SRE frameworks.
• Develop measurable SLIs, SLOs, and SLAs for data services.
• Implement DataOps and automated reliability management practices.
• Improve data pipeline availability, scalability, and performance.
• Apply observability, monitoring, and alerting technologies.
• Strengthen data quality management and validation processes.
• Implement fault tolerance and automated failure recovery.
• Design effective backup and disaster recovery strategies.
• Optimize cloud infrastructure and distributed data systems.
• Apply CI/CD automation to data engineering workflows.
• Strengthen data platform security and operational governance.
• Conduct incident management and root cause analysis.
• Develop proactive reliability improvement and capacity planning strategies.

Organizational Benefits
 

• Reduced data platform downtime and operational disruptions.
• Improved enterprise data availability and accessibility.
• Increased productivity through automated DataOps workflows.
• Lower infrastructure maintenance and operational costs.
• Enhanced data quality and business intelligence accuracy.
• Stronger cybersecurity and operational resilience.
• Improved disaster recovery and business continuity.
• Better scalability for enterprise analytics workloads.
• Faster incident detection and resolution.
• Increased confidence in data-driven decision-making.

Target Audiences
 

• Data engineers and platform engineers.
• Site Reliability Engineers (SREs).
• Data architects and enterprise architects.
• Cloud infrastructure and DevOps engineers.
• Data operations and DataOps professionals.
• Database administrators and system administrators.
• IT operations and technology managers.
• Business intelligence and analytics specialists.

Course Duration: 5 days

Course Modules

Module 1: Foundations of Data Platform Reliability Engineering
 

• Reliability engineering principles and SRE fundamentals.
• Modern data platform architectures and operational requirements.
• DataOps integration and reliability engineering frameworks.
• Reliability maturity models and performance benchmarks.
• Global case study: Google's SRE approach to large-scale infrastructure reliability.
• Data platform availability, scalability, and resilience assessment.

Module 2: Service-Level Management and Reliability Metrics
 

• Defining SLIs, SLOs, and SLAs for data platforms.
• Error budgets and reliability performance management.
• Data availability, latency, freshness, and throughput measurements.
• Reliability dashboards and operational performance reporting.
• Global case study: Netflix service monitoring and reliability measurement.
• Establishing enterprise reliability targets and reporting frameworks.

Module 3: Data Platform Observability and Monitoring
 

• Data pipeline monitoring and real-time observability.
• Metrics, logs, traces, and distributed system monitoring.
• Anomaly detection and automated alert management.
• Data quality monitoring and pipeline health checks.
• Global case study: Uber's data observability and large-scale data infrastructure practices.
• Implementing proactive monitoring and troubleshooting frameworks.

Module 4: Data Pipeline Resilience and Fault Tolerance
 

• Designing fault-tolerant data ingestion and processing systems.
• Managing pipeline failures, retries, and recovery mechanisms.
• Stream processing reliability and distributed data systems.
• Handling data duplication, consistency, and processing errors.
• Global case study: Amazon's distributed infrastructure resilience practices.
• Implementing automated pipeline recovery strategies.

Module 5: Cloud Infrastructure Reliability and Automation
 

• Cloud-native data platform reliability architecture.
• Infrastructure as Code and configuration management.
• Kubernetes orchestration and container reliability.
• Automated provisioning, scaling, and resource optimization.
• Global case study: Spotify's cloud infrastructure and engineering automation practices.
• Implementing reliable infrastructure deployment workflows.

Module 6: Incident Management and Disaster Recovery
 

• Incident response planning and escalation procedures.
• Root cause analysis and post-incident reviews.
• Backup, replication, and disaster recovery planning.
• Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs).
• Global case study: Financial institutions implementing disaster recovery for mission-critical data.
• Developing business continuity and operational recovery strategies.

Module 7: Data Platform Security and Governance
 

• Data infrastructure security and access management.
• Encryption, identity management, and access controls.
• Data governance, compliance, and auditability.
• Security monitoring and vulnerability management.
• Global case study: Microsoft Azure enterprise security and governance practices.
• Integrating security and compliance into reliability engineering.

Module 8: Reliability Testing and Continuous Improvement
 

• Reliability testing and automated validation frameworks.
• Chaos engineering and controlled failure simulation.
• CI/CD pipelines and automated deployment testing.
• Capacity planning and predictive performance optimization.
• Global case study: Netflix Chaos Engineering and resilience testing.
• Developing continuous reliability improvement roadmaps.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using modern data engineering technologies.
• Hands-on reliability monitoring and troubleshooting exercises.
• Group assignments involving real-world data platform scenarios.
• Global case study analysis and industry best-practice discussions.
• Practical assessments, knowledge-sharing sessions, and action planning.

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.