15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Business Intelligence (BI) Developers
- Analytics Engineers
- Data Scientists working with large-scale data pipelines
- Cloud Engineers and Cloud Data Specialists
- DevOps and Platform Engineers supporting data workflows
- Software Engineers involved in data integration
- Data Warehouse and Data Lake Developers
- IT Managers and Technology Managers
- Data Platform and Infrastructure Teams
- Data Governance and Data Management Professionals
- Business Intelligence and Analytics Managers
- Project Managers overseeing data engineering or integration projects
- Professionals transitioning into modern data engineering roles
Session Objectives
- Understand the fundamentals of ETL/ELT pipeline development.
- Master data extraction techniques from various data sources.
- Utilize data transformation logic for data cleansing and enrichment.
- Implement data loading strategies for data warehousing and data lakes.
- Design and build efficient ETL/ELT pipelines for data integration.
- Optimize data pipelines for performance, scalability, and maintainability.
- Troubleshoot and address common challenges in ETL/ELT pipelines.
- Implement data quality checks and validation in ETL/ELT processes.
- Integrate ETL/ELT pipelines with real-world analytics platforms.
- Understand how to handle data security and compliance in data integration.
- Explore advanced ETL/ELT techniques (e.g., change data capture, real-time data integration).
- Apply real world use cases for ETL/ELT pipeline development.
- Leverage ETL/ELT tools and frameworks for efficient pipeline development.
About the Course
This practical and advanced training course provides participants with the knowledge and skills required to design, develop, automate, and manage modern ETL and ELT data pipelines for reliable enterprise data integration. It explores how organizations can efficiently extract data from diverse sources, transform and validate it, and load it into data warehouses, data lakes, lakehouses, and analytics platforms.Participants will examine modern data integration architectures, pipeline design principles, workflow orchestration, dependency management, data quality, error handling, monitoring, scheduling, and performance optimization. The course also addresses the growing role of cloud platforms, APIs, real-time data sources, automation, and scalable data engineering practices in modern ETL/ELT environments.Through practical exercises and real-world scenarios, participants will learn how to build robust and maintainable pipelines, automate data workflows, troubleshoot pipeline failures, and establish effective operational controls. The course is suitable for organizations seeking to modernize their data integration capabilities and build scalable data engineering environments.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of ETL/ELT pipeline development.
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Workshop 1.2:
Overview of data extraction, transformation, and loading processes.
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Workshop 1.3:
Setting up an ETL/ELT development environment.
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Workshop 1.4:
Introduction to ETL/ELT tools and frameworks.
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Workshop 1.5:
Best practices for ETL/ELT pipelines.
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Workshop 2.1:
Mastering data extraction techniques from various data sources.
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Workshop 2.2:
Utilizing database connectors, APIs, and file formats.
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Workshop 2.3:
Designing and building data extraction scripts.
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Workshop 2.4:
Optimizing extraction for performance and data integrity.
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Workshop 2.5:
Best practices for data extraction.
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Workshop 3.1:
Utilizing data transformation logic for data cleansing and enrichment.
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Workshop 3.2:
Implementing data cleansing, normalization, and aggregation.
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Workshop 3.3:
Designing and building data transformation workflows.
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Workshop 3.4:
Optimizing transformations for data quality.
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Workshop 3.5:
Best practices for data transformation.
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Workshop 4.1:
Implementing data loading strategies for data warehousing and data lakes.
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Workshop 4.2:
Utilizing batch loading, incremental loading, and real-time loading.
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Workshop 4.3:
Designing and building data loading scripts.
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Workshop 4.4:
Optimizing loading for data integrity and performance.
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Workshop 4.5:
Best practices for data loading.
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Workshop 5.1:
Designing and building efficient ETL/ELT pipelines for data integration.
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Workshop 5.2:
Implementing data pipeline orchestration and scheduling.
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Workshop 5.3:
Utilizing modular and reusable pipeline components.
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Workshop 5.4:
Optimizing pipelines for specific data integration tasks.
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Workshop 5.5:
Best practices for pipeline design.
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Workshop 6.1:
Optimizing data pipelines for performance, scalability, and maintainability.
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Workshop 6.2:
Utilizing performance tuning and monitoring tools.
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Workshop 6.3:
Implementing data partitioning and caching strategies.
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Workshop 6.4:
Designing scalable pipeline architectures.
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Workshop 6.5:
Best practices for pipeline optimization.
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Workshop 7.1:
Debugging common challenges in ETL/ELT pipelines.
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Workshop 7.2:
Analyzing pipeline performance and errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common data integration issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data quality checks and validation in ETL/ELT processes.
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Workshop 8.2:
Utilizing data validation libraries and techniques.
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Workshop 8.3:
Designing and building data quality monitoring systems.
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Workshop 8.4:
Optimizing validation for data integrity.
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Workshop 8.5:
Best practices for data quality.
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Workshop 9.1:
Integrating ETL/ELT pipelines with real-world analytics platforms.
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Workshop 9.2:
Utilizing BI tools and data visualization platforms.
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Workshop 9.3:
Implementing data access and security measures.
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Workshop 9.4:
Optimizing integration for data-driven insights.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle data security and compliance in data integration.
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Workshop 10.2:
Utilizing data encryption and access control.
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Workshop 10.3:
Designing and building compliant data pipelines.
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Workshop 10.4:
Optimizing data handling for regulatory requirements.
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Workshop 10.5:
Best practices for security.
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Workshop 11.1:
Exploring advanced ETL/ELT techniques (change data capture, real-time data integration).
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Workshop 11.2:
Utilizing change data capture (CDC) for incremental updates.
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Workshop 11.3:
Implementing real-time data integration with streaming platforms.
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Workshop 11.4:
Designing and building advanced data integration 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 techniques.
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Workshop 12.1:
Implementing ETL/ELT for data warehousing and business intelligence.
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Workshop 12.2:
Utilizing ETL/ELT for data migration and integration.
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Workshop 12.3:
Implementing ETL/ELT for real-time data analytics.
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Workshop 12.4:
Utilizing ETL/ELT for customer data platforms.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing ETL/ELT tools and frameworks (Apache Airflow, Talend, Informatica).
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Workshop 13.2:
Implementing data pipelines with specific tools.
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Workshop 13.3:
Designing and building automated data 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 pipeline performance monitoring.
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Workshop 14.2:
Utilizing logging and monitoring tools.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in ETL/ELT development.
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Workshop 15.2:
Utilizing AI for data pipeline automation.
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Workshop 15.3:
Implementing ETL/ELT in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
