15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Engineers
- Data Platform Engineers
- Analytics Engineers
- Data Architects
- Big Data Engineers
- Cloud Data Engineers
- Data Quality Professionals
- Data Governance Specialists
- Data Operations Professionals
- Database Administrators
- Data Warehouse Engineers
- Data Lake and Lakehouse Engineers
- DevOps Engineers
Session 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.
About the Course
This advanced training course equips participants with the knowledge and practical skills required to monitor, diagnose, and maintain the reliability, quality, and performance of modern data pipelines and platforms. It focuses on data observability as a proactive approach to identifying data incidents, pipeline failures, quality issues, unexpected changes, and infrastructure problems before they significantly affect business operations.
Participants will explore the key dimensions of data observability, including freshness, accuracy, completeness, consistency, validity, and availability. The course covers pipeline monitoring, data quality checks, anomaly detection, lineage, metadata, dependency tracking, alerting, incident management, root-cause analysis, and service-level objectives (SLOs) for data systems.Through practical exercises and real-world scenarios, participants will learn how to establish data health metrics, monitor end-to-end pipelines, detect anomalies, troubleshoot failures, identify upstream causes of downstream issues, and build reliable data operations. The course also addresses observability across data lakes, data warehouses, lakehouses, cloud platforms, streaming systems, and modern analytics environments.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data observability.
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Workshop 1.2:
Overview of data quality monitoring, anomaly detection, and data lineage.
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Workshop 1.3:
Setting up a data observability development environment.
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Workshop 1.4:
Introduction to data observability tools and best practices.
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Workshop 1.5:
Best practices for data observability.
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Workshop 2.1:
Mastering data quality monitoring and validation techniques.
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Workshop 2.2:
Utilizing data profiling and schema validation.
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Workshop 2.3:
Implementing data quality checks and rules.
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Workshop 2.4:
Designing and building data quality monitoring pipelines.
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Workshop 2.5:
Best practices for data quality.
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Workshop 3.1:
Utilizing anomaly detection and alerting systems for data pipelines.
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Workshop 3.2:
Implementing statistical anomaly detection.
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Workshop 3.3:
Designing and building alerting mechanisms.
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Workshop 3.4:
Optimizing alerting for real-time notifications.
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Workshop 3.5:
Best practices for anomaly detection.
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Workshop 4.1:
Implementing data lineage and metadata tracking.
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Workshop 4.2:
Utilizing metadata repositories and data catalogs.
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Workshop 4.3:
Designing and building data lineage graphs.
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Workshop 4.4:
Optimizing metadata for data discovery.
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Workshop 4.5:
Best practices for data lineage.
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Workshop 5.1:
Designing and building automated data observability dashboards.
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Workshop 5.2:
Utilizing visualization tools and metrics.
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Workshop 5.3:
Implementing real-time monitoring dashboards.
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Workshop 5.4:
Optimizing dashboards for actionable insights.
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Workshop 5.5:
Best practices for dashboards.
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Workshop 6.1:
Optimizing data observability for real-time monitoring.
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Workshop 6.2:
Utilizing stream processing and event-driven architectures.
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Workshop 6.3:
Implementing real-time data quality checks.
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Workshop 6.4:
Designing scalable monitoring systems.
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Workshop 6.5:
Best practices for real-time.
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Workshop 7.1:
Troubleshooting and addressing common issues in data pipeline monitoring.
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Workshop 7.2:
Analyzing monitoring logs and alerts.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common monitoring errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and compliance within observability frameworks.
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Workshop 8.2:
Utilizing data access control and audit logs.
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Workshop 8.3:
Designing and building compliance monitoring.
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Workshop 8.4:
Optimizing observability for regulatory requirements.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating data observability with various data platforms and tools.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing observability for cloud-native data platforms.
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Workshop 9.4:
Optimizing integration for data monitoring.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle large-scale data monitoring.
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Workshop 10.2:
Utilizing distributed monitoring systems.
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Workshop 10.3:
Implementing data partitioning and aggregation.
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Workshop 10.4:
Designing scalable observability architectures.
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Workshop 10.5:
Best practices for large scale data.
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Workshop 11.1:
Exploring advanced data observability patterns (automated root cause analysis).
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Workshop 11.2:
Utilizing machine learning for anomaly detection.
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Workshop 11.3:
Implementing automated root cause analysis.
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Workshop 11.4:
Designing and building advanced observability solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced patterns.
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Workshop 12.1:
Implementing data observability for e-commerce data pipelines.
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Workshop 12.2:
Utilizing data observability for financial transaction monitoring.
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Workshop 12.3:
Implementing data observability for IoT data streams.
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Workshop 12.4:
Utilizing data observability for healthcare data quality.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing data observability tools and frameworks (Great Expectations, Monte Carlo).
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Workshop 13.2:
Implementing observability with specific tools.
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Workshop 13.3:
Designing and building automated monitoring workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing observability performance monitoring.
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Workshop 14.2:
Utilizing monitoring metrics and logs.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for observability system health.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in data observability.
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Workshop 15.2:
Utilizing AI for automated anomaly detection.
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Workshop 15.3:
Implementing observability for data mesh architectures.
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Workshop 15.4:
Best practices for future applications.
