15 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Engineers
- ETL/ELT Developers
- Data Warehouse Developers
- Data Architects
- Database Administrators
- Database Developers
- Data Integration Specialists
- Analytics Engineers
- Business Intelligence Developers
- Data Analysts
- Data Scientists
- Cloud Data Engineers
- Data Platform Engineers
Session Objectives
- Understand the fundamentals of data warehousing and ETL pipelines.
- Design and implement efficient data pipelines for various data sources.
- Master data modeling techniques for data warehousing.
- Utilize industry-standard ETL tools and technologies.
- Optimize ETL pipeline performance for large datasets.
- Implement data quality checks and validation processes.
- Design and build dimensional data models for BI.
- Automate ETL workflows for efficient data loading.
- Troubleshoot and debug ETL pipeline
- Implement data security and access control in data warehousing.
- Integrate data warehousing solutions with BI tools.
- Explore advanced ETL patterns and techniques.
About the Course
This comprehensive training course equips participants with the knowledge and practical skills required to design, develop, manage, and optimize data warehouses and ETL pipelines. It focuses on how organizations can integrate data from multiple sources, transform it into reliable and consistent information, and load it into centralized data warehouse environments for reporting, business intelligence, and analytics.Participants will explore data warehouse architecture, dimensional modeling, schema design, ETL processes, data extraction, transformation and cleansing, data loading, data quality, pipeline scheduling, error handling, monitoring, and performance optimization. The course also introduces modern approaches to ETL/ELT, cloud data warehouses, data lakes, and automated data pipelines.Through practical exercises and real-world scenarios, participants will learn how to build reliable data integration workflows, troubleshoot pipeline failures, improve data quality, optimize warehouse performance, and establish scalable data platforms that support strategic decision-making.
Curriculum & Topics
15 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of data warehousing and ETL.
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Workshop 1.2:
Understanding the role of ETL pipelines in BI.
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Workshop 1.3:
Overview of different data warehousing architectures.
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Workshop 1.4:
Setting up a development environment.
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Workshop 1.5:
Introduction to ETL tools.
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Workshop 2.1:
Concepts of dimensional data modeling.
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Workshop 2.2:
Designing star and snowflake schemas.
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Workshop 2.3:
Implementing fact and dimension tables.
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Workshop 2.4:
Utilizing surrogate keys and slowly changing dimensions.
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Workshop 2.5:
Best practices for data modeling.
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Workshop 3.1:
Designing ETL pipelines for various data sources.
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Workshop 3.2:
Implementing data extraction, transformation, and loading.
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Workshop 3.3:
Utilizing ETL design patterns.
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Workshop 3.4:
Implementing data quality checks and validation.
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Workshop 3.5:
Best practices for ETL development.
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Workshop 4.1:
Utilizing industry-standard ETL tools (e.g., Talend, Informatica, DataStage).
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Workshop 4.2:
Implementing data transformations using SQL and scripting.
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Workshop 4.3:
Utilizing cloud-based ETL services (AWS Glue, Azure Data Factory).
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Workshop 4.4:
Integrating ETL tools with databases and data lakes.
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Workshop 4.5:
Best practices for tool selection.
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Workshop 5.1:
Optimizing ETL pipeline performance for large datasets.
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Workshop 5.2:
Tuning ETL processes for efficiency.
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Workshop 5.3:
Implementing parallel processing and partitioning.
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Workshop 5.4:
Utilizing indexing and caching strategies.
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Workshop 5.5:
Performance monitoring and analysis.
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Workshop 6.1:
Implementing data quality checks and validation processes.
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Workshop 6.2:
Utilizing data profiling and cleansing techniques.
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Workshop 6.3:
Implementing data reconciliation and auditing.
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Workshop 6.4:
Handling data errors and exceptions.
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Workshop 6.5:
Best practices for data quality management.
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Workshop 7.1:
Designing dimensional data models for BI.
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Workshop 7.2:
Implementing star and snowflake schemas for reporting.
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Workshop 7.3:
Utilizing OLAP cubes and data aggregation.
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Workshop 7.4:
Integrating dimensional models with BI tools.
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Workshop 7.5:
Best practices for BI data modeling.
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Workshop 8.1:
Automating ETL workflows for efficient data loading.
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Workshop 8.2:
Utilizing workflow management tools (e.g., Apache Airflow, Control-M).
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Workshop 8.3:
Implementing scheduling and dependency management.
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Workshop 8.4:
Automating data quality checks and notifications.
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Workshop 8.5:
Best practices for workflow automation.
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Workshop 9.1:
Troubleshooting and debugging ETL pipeline issues.
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Workshop 9.2:
Analyzing ETL logs and error messages.
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Workshop 9.3:
Utilizing debugging tools and techniques.
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Workshop 9.4:
Identifying and resolving performance bottlenecks.
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Workshop 9.5:
Best practices for ETL troubleshooting.
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Workshop 10.1:
Implementing data security in data warehousing.
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Workshop 10.2:
Utilizing authentication and authorization.
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Workshop 10.3:
Implementing data encryption and masking.
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Workshop 10.4:
Auditing and compliance in data warehousing.
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Workshop 10.5:
Best practices for data security.
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Workshop 11.1:
Integrating data warehousing solutions with BI tools (e.g., Tableau, Power BI).
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Workshop 11.2:
Utilizing data visualization and reporting techniques.
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Workshop 11.3:
Implementing data analysis and dashboards.
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Workshop 11.4:
Best practices for BI integration.
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Workshop 12.1:
Implementing change data capture (CDC).
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Workshop 12.2:
Utilizing data virtualization and data federation.
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Workshop 12.3:
Implementing data streaming for real-time ETL.
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Workshop 12.4:
Utilizing advanced ETL transformations.
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Workshop 12.5:
Best practices for advanced ETL.
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Workshop 13.1:
Deploying data warehousing solutions on cloud platforms.
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Workshop 13.2:
Utilizing cloud-based data warehousing services (e.g., Redshift, BigQuery).
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Workshop 13.3:
Implementing cloud-based ETL pipelines.
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Workshop 13.4:
Best practices for cloud data warehousing.
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Workshop 14.1:
Implementing data governance policies in data warehousing.
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Workshop 14.2:
Utilizing metadata management tools.
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Workshop 14.3:
Implementing data lineage and data dictionary.
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Workshop 14.4:
Best practices for data governance.
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Workshop 15.1:
Emerging trends in data warehousing and ETL.
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Workshop 15.2:
Utilizing AI and machine learning in ETL processes.
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Workshop 15.3:
Implementing data mesh architectures.
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Workshop 15.4:
Best practices for future data warehousing.
