Data Warehousing Training
Introduction to Data Warehousing
Learn the fundamentals of data warehousing, including its purpose and benefits. Understand the concepts of data warehousing, data marts, and how they fit into the broader context of business intelligence and analytics.
Data Warehouse Architecture
Explore the architecture of a data warehouse, including its components and layers. Learn about staging, ETL (Extract, Transform, Load) processes, data integration, and data storage.
Data Modeling for Data Warehousing
Study data modeling techniques specific to data warehousing. Learn about star schema, snowflake schema, and galaxy schema. Understand how to design and implement dimensional models to optimize query performance.
ETL Processes and Tools
Discover ETL processes and tools used to extract, transform, and load data into a data warehouse. Learn about popular ETL tools and best practices for designing and managing ETL workflows.
Data Warehousing Concepts and Techniques
Understand key concepts and techniques in data warehousing, such as data normalization, denormalization, data aggregation, and data partitioning. Learn how these concepts impact performance and scalability.
Data Warehouse Implementation
Explore the steps involved in implementing a data warehouse. Learn about project planning, design, development, testing, and deployment. Understand the challenges and best practices for successful implementation.
Data Warehousing for Business Intelligence
Learn how data warehousing supports business intelligence (BI) and analytics. Understand how to leverage data warehouses for reporting, analysis, and decision-making. Explore BI tools and techniques for querying and visualizing data.
Performance Tuning and Optimization
Study techniques for optimizing the performance of a data warehouse. Learn about indexing, query optimization, partitioning strategies, and performance monitoring to ensure efficient data retrieval and processing.
Data Governance and Security
Understand the importance of data governance and security in a data warehouse environment. Learn about data quality, data management practices, and security measures to protect sensitive information.
Case Studies and Practical Exercises
Engage in case studies and practical exercises to apply data warehousing concepts. Work with real-world scenarios to design, implement, and manage data warehouses, and solve common challenges.
Career Development and Certification
Prepare for a career in data warehousing with guidance on professional development, job search strategies, and certification options. Familiarize yourself with industry certifications and exam preparation resources.
Data Warehousing Syllabus
Introduction to Data Warehousing
- Overview of data warehousing concepts and architecture
- Importance of data warehousing in decision support systems
- Evolution of data warehousing technologies
- Role of data warehouses in business intelligence
Data Warehouse Design Principles
- Introduction to dimensional modeling
- Star schema and snowflake schema design
- Fact and dimension tables
- Normalization vs. denormalization in data warehousing
ETL Processes in Data Warehousing
- Introduction to ETL (Extract, Transform, Load) processes
- Data extraction techniques from heterogeneous sources
- Data transformation and cleansing techniques
- Loading data into the data warehouse
Data Warehouse Architecture
- Overview of data warehouse architecture components
- Data mart vs. enterprise data warehouse (EDW)
- Single-tier, two-tier, and three-tier architectures
- Scalability, reliability, and performance considerations
Data Quality and Master Data Management
- Importance of data quality in data warehousing
- Data profiling and data cleansing techniques
- Introduction to master data management (MDM)
- Data governance and stewardship
Data Warehouse Modeling Techniques
- Overview of different modeling techniques (e.g., Inmon vs. Kimball)
- Hybrid approaches to data warehousing
- Bridge tables and slowly changing dimensions (SCDs)
- Real-world case studies and modeling exercises
Advanced ETL Techniques
- Incremental loading and change data capture (CDC)
- Parallel processing and partitioning for ETL performance
- Error handling and logging in ETL processes
- Introduction to data integration tools
Performance Optimization in Data Warehousing
- Indexing strategies for data warehouses
- Query optimization techniques
- Materialized views and aggregate tables
- Partitioning and compression for performance improvement
Data Warehousing in the Cloud
- Overview of cloud-based data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake)
- Benefits and challenges of cloud data warehousing
- Migrating on-premises data warehouses to the cloud
- Hands-on experience with a selected cloud data warehousing platform
Data Visualization and Reporting in Data Warehousing
- Overview of BI tools for data visualization and reporting (e.g., Tableau, Power BI)
- Designing dashboards and reports for data analysis
- Integrating BI tools with data warehouses
- Real-time analytics and streaming data integration
Data Warehousing Security and Compliance
- Security challenges in data warehousing
- Role-based access control (RBAC) and data encryption
- Compliance requirements (e.g., GDPR, HIPAA) for data warehousing
- Auditing and monitoring in data warehousing environments
Capstone Project
- Final project where students apply their knowledge and skills to design and implement a data warehousing solution for a real-world scenario
- Project planning, data modeling, ETL processes, and performance optimization
- Presentation of projects and peer feedback
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details