Data Observability Training Course: Monitor Data Pipeline Health

Introduction

Ensure the reliability of your data infrastructure with our Data Observability Training Course. This program is designed to equip you with the essential skills to implement tooling to monitor the health of data pipelines, enabling you to proactively identify and resolve data quality issues. In today's data-driven world, mastering data observability is crucial for organizations seeking to maintain trust in their data and ensure operational efficiency. Our data observability training course offers hands-on experience and expert guidance, empowering you to build robust monitoring systems for your data pipelines.

This monitor data pipeline health training delves into the core concepts of data observability, covering topics such as data quality monitoring, anomaly detection, and data lineage tracking. You'll gain expertise in using industry-standard tools and techniques to implement tooling to monitor the health of data pipelines, meeting the demands of modern data-intensive organizations. Whether you're a data engineer, data architect, or data quality analyst, this Data Observability course will empower you to build and maintain reliable data systems.

Target Audience:

• Data Engineers
• Data Architects
• Data Quality Analysts
• Data Scientists
• DevOps Engineers
• System Administrators
• Anyone needing data observability skills

Course Objectives:

• Understand the fundamentals of data observability.
• Master data quality monitoring and validation techniques.
• Utilize anomaly detection and alerting systems for data pipelines.
• Implement data lineage and metadata tracking.
• Design and build automated data observability dashboards.
• Optimize data observability for real-time monitoring.
• Troubleshoot and address common issues in data pipeline monitoring.
• Implement data governance and compliance within observability frameworks.
• Integrate data observability with various data platforms and tools.
• Understand how to handle large-scale data monitoring.
• Explore advanced data observability patterns (e.g., automated root cause analysis).
• Apply real world use cases for data observability.
• Leverage data observability tools and frameworks for efficient monitoring.

Duration

10 Days

Course content

Module 1: Introduction to Data Observability

• Fundamentals of data observability.
• Overview of data quality monitoring, anomaly detection, and data lineage.
• Setting up a data observability development environment.
• Introduction to data observability tools and best practices.
• Best practices for data observability.

Module 2: Data Quality Monitoring and Validation

• Mastering data quality monitoring and validation techniques.
• Utilizing data profiling and schema validation.
• Implementing data quality checks and rules.
• Designing and building data quality monitoring pipelines.
• Best practices for data quality.

Module 3: Anomaly Detection and Alerting

• Utilizing anomaly detection and alerting systems for data pipelines.
• Implementing statistical anomaly detection.
• Designing and building alerting mechanisms.
• Optimizing alerting for real-time notifications.
• Best practices for anomaly detection.

Module 4: Data Lineage and Metadata Tracking

• Implementing data lineage and metadata tracking.
• Utilizing metadata repositories and data catalogs.
• Designing and building data lineage graphs.
• Optimizing metadata for data discovery.
• Best practices for data lineage.

Module 5: Automated Observability Dashboards

• Designing and building automated data observability dashboards.
• Utilizing visualization tools and metrics.
• Implementing real-time monitoring dashboards.
• Optimizing dashboards for actionable insights.
• Best practices for dashboards.

Module 6: Real-Time Monitoring Optimization

• Optimizing data observability for real-time monitoring.
• Utilizing stream processing and event-driven architectures.
• Implementing real-time data quality checks.
• Designing scalable monitoring systems.
• Best practices for real-time.

Module 7: Troubleshooting Monitoring Issues

• Troubleshooting and addressing common issues in data pipeline monitoring.
• Analyzing monitoring logs and alerts.
• Utilizing problem-solving techniques for resolution.
• Resolving common monitoring errors.
• Best practices for troubleshooting.

Module 8: Data Governance and Compliance

• Implementing data governance and compliance within observability frameworks.
• Utilizing data access control and audit logs.
• Designing and building compliance monitoring.
• Optimizing observability for regulatory requirements.
• Best practices for governance.

Module 9: Integration with Data Platforms

• Integrating data observability with various data platforms and tools.
• Utilizing APIs and data connectors.
• Implementing observability for cloud-native data platforms.
• Optimizing integration for data monitoring.
• Best practices for integration.

Module 10: Large-Scale Data Monitoring

• Understanding how to handle large-scale data monitoring.
• Utilizing distributed monitoring systems.
• Implementing data partitioning and aggregation.
• Designing scalable observability architectures.
• Best practices for large scale data.

Module 11: Advanced Observability Patterns

• Exploring advanced data observability patterns (automated root cause analysis).
• Utilizing machine learning for anomaly detection.
• Implementing automated root cause analysis.
• Designing and building advanced observability solutions.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced patterns.

Module 12: Real-World Use Cases

• Implementing data observability for e-commerce data pipelines.
• Utilizing data observability for financial transaction monitoring.
• Implementing data observability for IoT data streams.
• Utilizing data observability for healthcare data quality.
• Best practices for real-world applications.

Module 13: Observability Tools Implementation

• Utilizing data observability tools and frameworks (Great Expectations, Monte Carlo).
• Implementing observability with specific tools.
• Designing and building automated monitoring workflows.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Observability Performance Monitoring

• Implementing observability performance monitoring.
• Utilizing monitoring metrics and logs.
• Designing and building performance dashboards.
• Optimizing monitoring for observability system health.
• Best practices for monitoring.

Module 15: Future Trends in Data Observability

• Emerging trends in data observability.
• Utilizing AI for automated anomaly detection.
• Implementing observability for data mesh architectures.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.