15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Database Administrators
- BI Developers
- Data Analysts
- Data Architects
- Software Engineers
- DevOps Engineers
- MLOps Engineers
- Data Quality Professionals
- Data Operations Professionals
- IT and Technology Managers
- Professionals transitioning into data engineering
Session Objectives
- Understand the fundamentals of data pipelines and ETL development.
- Master data extraction from various data sources.
- Utilize data transformation techniques for data cleaning and preparation.
- Implement data loading into data warehouses and data lakes.
- Design and build efficient data pipelines for data integration.
- Optimize data pipelines for performance and scalability.
- Troubleshoot and address common data pipeline challenges.
- Implement data pipeline monitoring and logging.
- Integrate data pipelines with real-world data systems.
- Understand how to handle data quality and data governance.
- Explore advanced data pipeline techniques (e.g., streaming data pipelines, serverless ETL).
- Apply real world use cases for data pipelines and ETL.
- Leverage data pipeline tools and platforms for efficient development.
About the Course
This practical and comprehensive training course equips participants with the skills required to design, develop, automate, monitor, and optimize reliable data pipelines and ETL workflows. It focuses on extracting data from diverse sources, transforming and cleansing it, and delivering high-quality data to warehouses, lakes, lakehouses, and analytical platforms.Participants will explore modern ETL and ELT architectures, batch and real-time data processing, pipeline orchestration, data validation, error handling, workflow automation, monitoring, and performance optimization. The course also introduces best practices for building scalable and maintainable pipelines across cloud and on-premises environments.Through hands-on exercises and real-world scenarios, participants will learn how to develop end-to-end data pipelines, automate recurring workflows, manage dependencies, troubleshoot pipeline failures, implement data quality controls, and optimize pipeline performance for enterprise analytics and business intelligence.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of data pipelines and ETL development.
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Workshop 1.2:
Overview of data extraction, transformation, and loading (ETL).
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Workshop 1.3:
Setting up a data pipeline development environment.
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Workshop 1.4:
Introduction to data pipeline tools and platforms.
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Workshop 1.5:
Best practices for data pipelines.
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Workshop 2.1:
Implementing data extraction from relational databases.
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Workshop 2.2:
Utilizing APIs and web scraping for data extraction.
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Workshop 2.3:
Designing and building data extraction pipelines.
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Workshop 2.4:
Optimizing extraction for various data sources.
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Workshop 2.5:
Best practices for data extraction.
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Workshop 3.1:
Implementing data transformation techniques (data cleaning, aggregation).
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Workshop 3.2:
Utilizing data quality checks and validation.
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Workshop 3.3:
Designing and building data transformation pipelines.
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Workshop 3.4:
Optimizing transformations for data preparation.
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Workshop 3.5:
Best practices for data transformation.
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Workshop 4.1:
Implementing data loading into data warehouses and data lakes.
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Workshop 4.2:
Utilizing batch and incremental loading techniques.
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Workshop 4.3:
•Dsigning and building data loading pipelines.
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Workshop 4.4:
Optimizing loading for data storage and retrieval.
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Workshop 4.5:
Best practices for data loading.
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Workshop 5.1:
Designing efficient data pipelines for data integration.
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Workshop 5.2:
Implementing pipeline orchestration and scheduling.
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Workshop 5.3:
Utilizing workflow management tools (Airflow, Prefect).
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Workshop 5.4:
Optimizing pipeline design for performance.
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Workshop 5.5:
Best practices for pipeline design.
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Workshop 6.1:
Optimizing data pipelines for performance and scalability.
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Workshop 6.2:
Utilizing parallel processing and distributed computing.
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Workshop 6.3:
Implementing data partitioning and sharding.
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Workshop 6.4:
Designing scalable data integration solutions.
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Workshop 6.5:
Best practices for pipeline optimization.
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Workshop 7.1:
Debugging common data pipeline issues.
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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 pipeline challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data pipeline monitoring and logging.
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Workshop 8.2:
Utilizing logging and alerting for pipeline health.
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Workshop 8.3:
Designing and building monitoring dashboards.
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Workshop 8.4:
Optimizing monitoring for real-time insights.
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Workshop 8.5:
Best practices for monitoring.
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Workshop 9.1:
Integrating data pipelines with real-world data systems.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing data pipelines for various applications.
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Workshop 9.4:
Optimizing integration for business impact.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Implementing data quality checks and validation.
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Workshop 10.2:
Utilizing data governance frameworks.
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Workshop 10.3:
Designing and building data quality pipelines.
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Workshop 10.4:
Optimizing data handling for compliance.
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Workshop 10.5:
Best practices for data quality.
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Workshop 11.1:
Implementing streaming data pipelines (Kafka, Flink).
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Workshop 11.2:
Utilizing serverless ETL and data pipelines.
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Workshop 11.3:
Designing and building advanced data integration solutions.
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Workshop 11.4:
Optimizing advanced techniques for specific applications.
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Workshop 11.5:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing data pipelines for e-commerce data integration.
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Workshop 12.2:
Utilizing data pipelines for financial data warehousing.
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Workshop 12.3:
Implementing data pipelines for healthcare data analysis.
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Workshop 12.4:
Utilizing data pipelines for marketing data aggregation.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing Apache Airflow for pipeline orchestration.
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Workshop 13.2:
Implementing data pipelines with AWS Glue and Google Cloud Dataflow.
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Workshop 13.3:
Designing and building data pipelines with tools.
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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 data pipeline security measures.
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Workshop 14.2:
Utilizing data encryption and access control.
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Workshop 14.3:
Designing and building secure data pipelines.
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Workshop 14.4:
Optimizing pipelines for performance and security.
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Workshop 14.5:
Best practices for security and performance.
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Workshop 15.1:
Emerging trends in data pipeline development.
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Workshop 15.2:
Utilizing AI for data pipeline automation.
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Workshop 15.3:
Implementing data mesh and data fabric architectures.
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Workshop 15.4:
Best practices for future data pipelines.
