Introduction

In the pursuit of reliable and actionable business intelligence, the robust foundation of Data Warehousing and ETL (Extract, Transform, Load) is absolutely critical, serving as the backbone for consolidating disparate data sources into a unified, clean, and structured repository optimized for analytical queries and reporting. Without a well-designed data warehouse and efficient ETL processes, BI initiatives often struggle with data inconsistency, poor performance, and a lack of trust in insights, hindering data-driven decision-making. This training course is meticulously designed to equip data engineers, BI developers, data architects, database administrators, and IT professionals with cutting-edge knowledge and practical skills in understanding data warehousing concepts, mastering various ETL methodologies and tools, designing dimensional models, implementing data quality and governance best practices, exploring cloud-based data warehousing solutions, and optimizing data pipelines for performance and scalability. Participants will gain a comprehensive understanding of how to build and maintain the essential data infrastructure that powers effective business intelligence, ensuring data integrity, accessibility, and reliability for strategic insights.

Duration

10 days

Target Audience

• Data Engineers
• BI Developers
• Data Architects
• Database Administrators (DBAs)
• IT Professionals (Data Focus)
• Data Analysts (seeking deeper technical understanding)
• ETL Developers
• Data Governance Professionals
• Cloud Data Engineers
• Solution Architects

Objectives

• Understand the fundamental concepts and architecture of data warehousing.
• Master various ETL (Extract, Transform, Load) methodologies and best practices.
• Learn to design dimensional data models (star schema, snowflake schema).
• Develop proficiency in data extraction from diverse source systems.
• Understand data transformation techniques for cleansing, integration, and aggregation.
• Learn about data loading strategies for efficiency and performance.
• Explore data quality management and data governance in the context of ETL.
• Develop skills in using common ETL tools (e.g., SSIS, Talend, cloud-native services).
• Understand the role of metadata management and data lineage.
• Learn about data warehousing in the cloud (e.g., Snowflake, BigQuery, Azure Synapse).
• Formulate strategies for optimizing ETL processes and data warehouse performance.

Course Content

Module 1. Introduction to Business Intelligence and Data Warehousing

• Defining Business Intelligence (BI): Role of data in decision-making
• What is a Data Warehouse (DW)?: Characteristics, purpose, and benefits
• Comparison of OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)
• Evolution of data warehousing: From traditional DW to data lakes and data lakehouses
• Key components of a data warehousing architecture

Module 2. Data Warehousing Architecture and Design Principles

• Kimball vs. Inmon Approaches: Bottom-up vs. Top-down design
• Dimensional Modeling: Introduction to facts and dimensions
• Star Schema Design: Fact tables, dimension tables
• Snowflake Schema Design: Normalization of dimensions
• Data Marts: Purpose and design

Module 3. Data Modeling for Data Warehouses

• Fact Tables: Transactional, periodic snapshot, accumulating snapshot facts
• Dimension Tables: Conformed dimensions, slowly changing dimensions (SCD Type 1, 2, 3)
• Surrogate Keys vs. Natural Keys
• Granularity and aggregation strategies
• Role of business requirements in data model design

Module 4. Introduction to ETL (Extract, Transform, Load)

• ETL Process Overview: Stages and their importance
• ETL vs. ELT: Understanding the differences and modern trends
• Importance of ETL in data quality and consistency
• ETL challenges: Data volume, variety, velocity, veracity
• Overview of common ETL tools

Module 5. Data Extraction Techniques

• Connecting to Source Systems: Databases (relational, NoSQL), flat files, APIs, cloud services
• Full Load vs. Incremental Load strategies
• Change Data Capture (CDC) mechanisms
• Data replication and streaming data extraction
• Handling heterogeneous data sources

Module 6. Data Transformation: Cleansing and Standardization

• Data Cleansing: Handling missing values, duplicates, inconsistencies
• Data Standardization: Formatting, unit conversion, data type enforcement
• Data Parsing and Pattern Matching
• Data Validation and Error Handling
• Data quality rules and profiling

Module 7. Data Transformation: Integration and Aggregation

• Data Integration: Merging, joining, unioning disparate datasets
• Data Aggregation: Summarizing data for analytical purposes
• Creating calculated columns and measures
• Implementing business rules and logic during transformation
• Data lineage and traceability

Module 8. Data Loading Strategies

• Initial Load vs. Incremental Load: Strategies for loading data into the DW
• Load Performance Optimization: Batch loading, parallel loading
• Error handling during loading
• Restartability and recovery mechanisms
• Data partitioning and indexing for faster queries

Module 9. Data Quality and Governance in ETL

• Integrating Data Quality into ETL Processes: Proactive and reactive approaches
• Data Governance for ETL: Policies, procedures, roles
• Data ownership and stewardship for ETL pipelines
• Metadata management for ETL processes
• Auditing and logging ETL activities

Module 10. Common ETL Tools: Introduction and Overview

• Microsoft SQL Server Integration Services (SSIS): Overview, control flow, data flow
• Talend Open Studio: Data integration capabilities
• Informatica PowerCenter (conceptual overview)
• Other commercial and open-source ETL tools
• Comparison of tool features and suitability for different scenarios

Module 11. Cloud Data Warehousing Solutions

• Cloud DW Architecture: Scalability, elasticity, managed services
• Snowflake: Architecture, virtual warehouses, data sharing
• Google BigQuery: Serverless, petabyte-scale analytics
• Azure Synapse Analytics: Unified analytics platform
• AWS Redshift: Columnar storage, MPP architecture

Module 12. ETL in the Cloud Environment

• Cloud-Native ETL Services: Azure Data Factory, AWS Glue, Google Cloud Dataflow
• Serverless ETL: Benefits and use cases
• Data ingestion services (e.g., AWS Kinesis, Azure Event Hubs)
• Orchestration of ETL pipelines in the cloud
• Cost optimization for cloud ETL

Module 13. Performance Optimization for Data Warehouses and ETL

• Indexing Strategies: Clustered, non-clustered indexes
• Partitioning: For large tables and historical data
• Materialized Views and Aggregation Tables
• Query Optimization Techniques
• Monitoring and tuning ETL job performance

Module 14. Metadata Management and Data Lineage

• Metadata Types: Technical, business, operational
• Metadata Repositories: Storing and managing metadata
• Data Lineage: Tracking data flow from source to target
• Importance of metadata for data governance and understanding
• Automating metadata capture

Module 15. Future Trends in Data Warehousing and ETL

• Data Lakehouse Architecture: Combining best of data lakes and data warehouses
• Data Mesh: Decentralized data ownership and architecture
• Real-time ETL and Streaming Analytics: Kafka, Spark Streaming
• AI and Machine Learning in ETL: Automated data quality, anomaly detection
• Data Virtualization and its role in modern data architectures.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.