Data Observability and Reliability for Research Systems Training Course

Data Observability and Reliability for Research Systems Training Course

Introduction

In today’s data-driven world, ensuring data observability and reliability has become essential for research systems that depend on timely, accurate, and high-quality insights. Data Observability and Reliability for Research Systems Training Course is designed to equip professionals, researchers, and data engineers with cutting-edge strategies, best practices, and hands-on techniques for achieving end-to-end data observability and reliability. With the increasing complexity of research ecosystems, particularly in academia, healthcare, finance, and climate science, the ability to detect, resolve, and prevent data anomalies in real-time is a competitive advantage. Keywords like data lineage, real-time monitoring, data health, and automated alerting are reshaping how modern systems uphold trust and reproducibility in research.

This course will help participants integrate intelligent monitoring tools, deploy scalable observability frameworks, and understand root cause analysis in large-scale research environments. Through eight robust modules, learners will explore topics such as data pipelines, SLAs/SLOs, AI-driven anomaly detection, and metadata governance. The course is ideal for professionals seeking to develop data resilience, enhance system visibility, and adopt proactive strategies that reduce downtime and prevent data failure. Each module includes real-world case studies to help bridge theory and application.

Course Objectives

1. Understand the foundations of data observability in research systems
2. Explore key principles of data reliability engineering
3. Identify and monitor data quality metrics using modern tools
4. Implement automated alerting systems for real-time anomaly detection
5. Analyze data pipeline health through lineage tracking
6. Create robust data contracts to align teams and ensure compliance
7. Optimize SLA/SLO adherence and failure recovery strategies
8. Employ AI and ML models to predict and prevent data failure
9. Leverage metadata management to support governance and transparency
10. Improve data testing through synthetic and historical validation
11. Understand observability architectures for scalable data systems
12. Use open-source observability tools like Monte Carlo, Great Expectations, and OpenLineage
13. Apply observability to research reproducibility and audit readiness

Target Audiences

1. Research Data Scientists
2. Academic Researchers and Faculty
3. Data Engineers and Analysts
4. Research Software Developers
5. Health Informatics Professionals
6. Government Research Agencies
7. Environmental and Climate Scientists
8. Graduate Students in Data Science and Research Methodology

Course Duration: 5 days

Course Modules

Module 1: Introduction to Data Observability for Research

• Overview of data observability frameworks
• Importance in modern research ecosystems
• Types of data failures and impacts
• Key tools for observability
• Intro to data quality metrics
• Case Study: Real-time observability in COVID-19 data tracking

Module 2: Designing Reliable Data Pipelines

• Components of a resilient pipeline
• Pipeline versioning and monitoring
• Error handling and auto-recovery
• Integration with research databases
• Scheduling and orchestration with Airflow
• Case Study: Genomics research pipeline optimization

Module 3: Data Quality Monitoring and Alerting

• Setting up real-time data monitoring
• Alert thresholds and SLAs/SLOs
• Event correlation and response workflows
• Dashboard design and visualization
• Integrating ML for alert reduction
• Case Study: Anomaly detection in satellite climate data

Module 4: Data Lineage and Metadata Management

• Tracking transformations and dependencies
• Importance of metadata in reproducibility
• Implementing OpenLineage and Amundsen
• Data cataloguing best practices
• Governance and compliance tracking
• Case Study: Academic publishing audit using metadata lineage

Module 5: AI/ML in Observability Systems

• Machine learning for anomaly detection
• Predictive maintenance of pipelines
• Behavior-based anomaly scoring
• Unsupervised vs. supervised learning in observability
• Integrating AI models into alerts
• Case Study: Predictive alert system in agricultural research

Module 6: Building Data Contracts and SLA Management

• What are data contracts?
• Aligning producers and consumers
• Defining and enforcing SLAs
• SLA breach handling procedures
• Using contracts to prevent schema drift
• Case Study: Health research SLA success through contracts

Module 7: Open-Source Tools for Observability

• Overview of Monte Carlo, Great Expectations
• Setup and deployment strategies
• Tool comparison and selection matrix
• Automation through GitOps
• Open-source governance challenges
• Case Study: Academic collaboration using Great Expectations

Module 8: Observability for Reproducible Research

• Linking observability with research integrity
• Data versioning and snapshotting
• Publishing standards and reproducibility
• Audit trails for regulatory submission
• Sustaining data health for longitudinal studies
• Case Study: Long-term ecological study with reproducible observability pipeline

Training Methodology

• Instructor-led virtual and in-person sessions
• Interactive labs with hands-on tool usage
• Real-world case discussions
• Group activities and scenario simulations
• Evaluation through quizzes and final project
• Peer collaboration and feedback loops

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.