15 Topics
10 Days
74 Sessions
Programme Overview
Training Description
Who Should Attend
- Data Engineers
- Data Architects
- Data Warehouse Developers
- Data Lake Engineers
- Database Administrators
- Database Developers
- Analytics Engineers
- Business Intelligence Professionals
- Data Scientists
- Machine Learning Engineers
- Cloud Data Engineers
- Data Platform Engineers
Session Objectives
- Understand the fundamentals of data warehousing and data lakes.
- Master dimensional modeling and schema design for data warehouses.
- Utilize data lake architectures for flexible data storage.
- Implement data ingestion and ETL processes for data warehousing and lakes.
- Design and build efficient data storage solutions for analytics.
- Optimize data storage for query performance and scalability.
- Troubleshoot and address common data warehousing and data lakes challenges.
- Implement data governance and security for data storage systems.
- Integrate data warehouses and data lakes with real-world applications.
- Understand how to handle data quality and metadata management.
- Explore advanced data warehousing and data lakes techniques (e.g., cloud-based data warehouses, data lakehouses).
- Apply real world use cases for data warehousing and data lakes.
- Leverage data storage tools and platforms for efficient implementation.
About the Course
This advanced training course provides participants with the knowledge and practical skills required to design, develop, manage, and optimize modern data warehouses and data lakes for scalable enterprise analytics. It examines how organizations can build robust data storage environments capable of handling large volumes of structured, semi-structured, and unstructured data.Participants will explore data warehouse and data lake architectures, dimensional modeling, schema design, data ingestion, data integration, storage optimization, metadata management, data governance, and analytical workloads. The course also introduces modern lakehouse architectures and examines how they combine the flexibility of data lakes with the reliability and performance of traditional data warehouses.
Through practical exercises and real-world scenarios, participants will learn how to select appropriate storage architectures, design efficient schemas, optimize analytical queries, manage data quality, and build scalable platforms that support business intelligence, advanced analytics, artificial intelligence, and machine learning.
Curriculum & Topics
15 Topics | 74 Sessions
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Workshop 1.1:
Fundamentals of data warehousing and data lakes.
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Workshop 1.2:
Overview of dimensional modeling and data lake architectures.
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Workshop 1.3:
Setting up a data storage development environment.
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Workshop 1.4:
Introduction to data storage tools and platforms.
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Workshop 1.5:
Best practices for data warehousing and data lakes.
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Workshop 2.1:
Implementing dimensional modeling for data warehouses.
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Workshop 2.2:
Utilizing star and snowflake schemas for data organization.
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Workshop 2.3:
Designing and building data warehouse schemas.
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Workshop 2.4:
Optimizing schemas for query performance.
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Workshop 2.5:
Best practices for schema design.
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Workshop 3.1:
Implementing data lake architectures for flexible storage.
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Workshop 3.2:
Utilizing object storage and file formats (Parquet, ORC).
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Workshop 3.3:
Designing and building data lake storage solutions.
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Workshop 3.4:
Optimizing data lakes for data exploration and analysis.
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Workshop 3.5:
Best practices for data lakes.
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Workshop 4.1:
Implementing data ingestion for data warehousing and lakes.
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Workshop 4.2:
Utilizing ETL tools and data integration techniques.
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Workshop 4.3:
Designing and building data ingestion pipelines.
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Workshop 4.4:
Optimizing ETL processes for data loading and transformation.
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Workshop 4.5:
Best practices for data ingestion.
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Workshop 5.1:
Designing efficient data storage solutions for analytics.
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Workshop 5.2:
Implementing data partitioning and clustering.
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Workshop 5.3:
Utilizing data compression and indexing.
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Workshop 5.4:
Optimizing data storage for query performance.
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Workshop 5.5:
Best practices for solution design.
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Workshop 6.1:
Optimizing data storage for query performance and scalability.
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Workshop 6.2:
Utilizing query optimization techniques.
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Workshop 6.3:
Implementing data caching and indexing.
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Workshop 6.4:
Designing scalable data storage systems.
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Workshop 6.5:
Best practices for performance and scalability.
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Workshop 7.1:
Debugging common data warehousing and data lakes issues.
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Workshop 7.2:
Analyzing storage 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 storage challenges.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and security for data storage systems.
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Workshop 8.2:
Utilizing access control and data encryption.
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Workshop 8.3:
Designing and building secure data storage pipelines.
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Workshop 8.4:
Optimizing data handling for compliance.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating data warehouses and data lakes with real-world applications.
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Workshop 9.2:
Utilizing APIs and data connectors.
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Workshop 9.3:
Implementing data storage for various analytics 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 metadata management tools and techniques.
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Workshop 10.3:
Designing and building data quality pipelines.
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Workshop 10.4:
Optimizing data handling for accuracy and consistency.
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Workshop 10.5:
Best practices for data quality.
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Workshop 11.1:
Implementing cloud-based data warehouses (Snowflake, Redshift).
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Workshop 11.2:
Utilizing data lakehouses for unified data management.
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Workshop 11.3:
Designing and building advanced data storage 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 warehousing for business intelligence reporting.
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Workshop 12.2:
Utilizing data lakes for data science and machine learning.
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Workshop 12.3:
Implementing data storage for customer analytics.
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Workshop 12.4:
Utilizing data storage for financial data analysis.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing Snowflake and Amazon Redshift for data warehousing.
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Workshop 13.2:
Implementing data lakes with Apache Iceberg and Delta Lake.
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Workshop 13.3:
Designing and building data storage solutions with tools.
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Workshop 13.4:
Optimizing tool usage for efficient implementation.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing performance tuning for data warehouses and data lakes.
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Workshop 14.2:
Utilizing query optimization and indexing techniques.
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Workshop 14.3:
Designing and building optimized storage systems.
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Workshop 14.4:
Optimizing storage performance and resource utilization.
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Workshop 14.5:
Best practices for performance tuning.
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Workshop 15.1:
Emerging trends in data warehousing and data lakes.
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Workshop 15.2:
Utilizing AI for data storage management and optimization.
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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 storage.
