15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Architects
- Data Engineers
- Database Administrators
- Business Intelligence Developers
- Data Analysts
- System Architects
- Anyone needing data warehousing and data lake design skills
Session Objectives
- Understand the fundamentals of data warehousing and data lake design.
- Master dimensional modeling and schema design for data warehouses.
- •tilize data lake architectures for flexible data storage and processing.
- Implement ETL/ELT processes for data integration and transformation.
- Design and build efficient data storage solutions for analytics.
- Optimize data storage for performance, scalability, and cost-effectiveness.
- Troubleshoot and address common challenges in data warehousing and data lake design.
- Implement data governance and data quality management in data storage.
- Integrate data warehousing and data lakes with real-world analytics platforms.
- Understand how to handle large datasets and distributed storage.
- Explore advanced data storage techniques (e.g., data virtualization, data mesh).
- Apply real world use cases for data warehousing and data lake design.
- Leverage data storage tools and frameworks for efficient implementation
About the Course
Bridge raw storage and high-speed reporting with our Data Warehousing and Data Lake Design Training Course. Gain practical experience designing flexible warehouse schemas, optimizing lake repositories, and structuring resilient data integration pipelines. Learn how to minimize storage overhead, prevent data swamps, and build high-performance analytical environments that scale effortlessly with growing data volumes.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of data warehousing and data lake design.
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Subtopic 1.2:
Overview of dimensional modeling, data lake architectures, and ETL processes.
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Subtopic 1.3:
Setting up a data storage design environment.
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Subtopic 1.4:
Introduction to data storage tools and frameworks.
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Subtopic 1.5:
Best practices for data storage design.
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Subtopic 2.1:
Mastering dimensional modeling and schema design for data warehouses.
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Subtopic 2.2:
Utilizing star schema and snowflake schema design.
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Subtopic 2.3:
Designing and building fact and dimension tables.
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Subtopic 2.4:
Optimizing schema design for query performance.
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Subtopic 2.5:
Best practices for dimensional modeling.
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Subtopic 3.1:
Utilizing data lake architectures for flexible data storage and processing.
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Subtopic 3.2:
Implementing data lake design patterns (e.g., bronze, silver, gold layers).
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Subtopic 3.3:
Designing and building data lake storage solutions.
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Subtopic 3.4:
Optimizing data lake architectures for scalability.
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Subtopic 3.5:
Best practices for data lake architectures.
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Subtopic 4.1:
Implementing ETL/ELT processes for data integration and transformation.
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Subtopic 4.2:
Utilizing data integration tools and techniques.
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Subtopic 4.3:
Designing and building data pipelines for data warehousing and data lakes.
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Subtopic 4.4:
Optimizing ETL/ELT processes for data quality.
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Subtopic 4.5:
Best practices for ETL/ELT.
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Subtopic 5.1:
Designing and building efficient data storage solutions for analytics.
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Subtopic 5.2:
Utilizing data storage technologies (e.g., cloud storage, data warehousing appliances).
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Subtopic 5.3:
Implementing data partitioning and indexing strategies.
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Subtopic 5.4:
Optimizing data storage for specific analytics workloads.
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Subtopic 5.5:
Best practices for data storage solutions.
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Subtopic 6.1:
Optimizing data storage for performance, scalability, and cost-effectiveness.
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Subtopic 6.2:
Utilizing performance tuning and monitoring tools.
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Subtopic 6.3:
Implementing data compression and storage optimization techniques.
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Subtopic 6.4:
Designing scalable data storage architectures.
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Subtopic 6.5:
Best practices for optimization.
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Subtopic 7.1:
Debugging common challenges in data warehousing and data lake design.
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Subtopic 7.2:
Analyzing data storage performance and errors.
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Subtopic 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Subtopic 7.4:
Resolving common data storage issues.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data governance and data quality management in data storage.
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Subtopic 8.2:
Utilizing data quality checks and validation techniques.
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Subtopic 8.3:
Designing and building data governance policies.
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Subtopic 8.4:
Optimizing data storage for data integrity.
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Subtopic 8.5:
Best practices for governance.
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Subtopic 9.1:
Integrating data warehousing and data lakes with real-world analytics platforms.
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Subtopic 9.2:
Utilizing BI tools and data visualization platforms.
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Subtopic 9.3:
Implementing data access and security measures.
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Subtopic 9.4:
Optimizing integration for data-driven insights.
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Subtopic 9.5:
Best practices for integration.
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Subtopic 10.1:
Understanding how to handle large datasets and distributed storage.
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Subtopic 10.2:
Utilizing distributed storage systems (e.g., Hadoop, Spark).
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Subtopic 10.3:
Implementing data partitioning and parallel processing.
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Subtopic 10.4:
Designing scalable data storage solutions for big data.
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Subtopic 10.5:
Best practices for large datasets.
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Subtopic 11.1:
Exploring advanced data storage techniques (data virtualization, data mesh).
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Subtopic 11.2:
Utilizing data virtualization for data integration.
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Subtopic 11.3:
Implementing data mesh architectures for decentralized data ownership.
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Subtopic 11.4:
Designing and building advanced data storage solutions.
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Subtopic 11.5:
Optimizing advanced techniques for specific applications.
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Subtopic 11.6:
Best practices for advanced techniques.
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Subtopic 12.1:
Implementing data warehousing for business intelligence and reporting.
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Subtopic 12.2:
Utilizing data lakes for data science and machine learning.
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Subtopic 12.3:
Implementing data storage solutions for real-time analytics.
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Subtopic 12.4:
Utilizing data storage for customer data platforms.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing data storage tools and frameworks (Snowflake, Databricks, AWS Redshift).
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Subtopic 13.2:
Implementing data storage solutions with specific tools.
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Subtopic 13.3:
Designing and building data storage pipelines.
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Subtopic 13.4:
Optimizing tool usage for efficient implementation.
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Subtopic 13.5:
Best practices for tool implementation.
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Subtopic 14.1:
Implementing data storage performance monitoring.
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Subtopic 14.2:
Utilizing data storage metrics and monitoring tools.
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Subtopic 14.3:
Designing and building performance dashboards.
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Subtopic 14.4:
Optimizing monitoring for real-time insights.
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Subtopic 14.5:
Best practices for monitoring.
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Subtopic 15.1:
Emerging trends in data storage design.
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Subtopic 15.2:
Utilizing AI for data storage optimization.
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Subtopic 15.3:
Implementing data storage in cloud-native environments.
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Subtopic 15.4:
Best practices for future applications.
