Introduction

Data warehousing is a critical component in managing and analyzing vast amounts of organizational data for better decision-making. A well-designed data warehouse integrates data from multiple sources, enabling organizations to perform complex queries and analyses in real time. This course provides participants with the knowledge and practical skills to design, implement, and manage data warehousing solutions. Participants will learn about the key concepts, best practices, and advanced techniques in building scalable and efficient data warehouses.

By the end of this course, participants will be able to design, implement, and optimize data warehousing solutions using the latest technologies and industry best practices.

Objectives

By the end of this course, participants will:

• Understand the fundamental concepts of data warehousing, including ETL, OLAP, and OLTP systems.
• Learn how to design data warehousing architectures and data models.
• Gain hands-on experience in implementing ETL (Extract, Transform, Load) processes.
• Understand how to use data warehousing tools like SQL Server, Amazon Redshift, Snowflake, and Google BigQuery.
• Develop skills in optimizing and managing large-scale data warehouses.
• Learn how to implement data security and data governance in data warehousing environments.
• Understand the role of data warehousing in business intelligence and analytics.

Who Should Attend?

This course is ideal for:

• Data engineers, architects, and analysts who want to deepen their knowledge of data warehousing.
• IT professionals looking to implement and manage data warehousing solutions in their organizations.
• Business intelligence developers and analysts who want to leverage data warehousing for reporting and decision-making.
• Anyone interested in learning how to create and optimize scalable data warehousing solutions.

Day 1: Introduction to Data Warehousing

Morning Session: Data Warehousing Basics

• Introduction to data warehousing: Definition, components, and architecture.
• Key concepts: Data mart, OLAP, OLTP, and Data Lakes.
• Data warehousing processes: ETL (Extract, Transform, Load), ELT (Extract, Load, Transform).
• Understanding data sources and integration: Structured, semi-structured, and unstructured data.
• Hands-on: Explore the basic architecture of a data warehouse and key components.

Afternoon Session: Data Warehousing Architecture

• Data warehouse layers: Staging layer, Data Integration layer, Data Presentation layer.
• Different types of data warehousing architectures: Kimball vs. Inmon approach.
• Data warehousing platforms: On-premise vs. cloud-based solutions.
• Understanding star schema, snowflake schema, and fact and dimension tables.
• Hands-on: Create a basic star schema and implement it in a simple data warehouse.

Day 2: ETL Processes and Data Integration

Morning Session: Introduction to ETL

• Overview of ETL processes: Extract, Transform, and Load.
• Tools and technologies for ETL: Apache NiFi, Talend, Informatica, and SSIS.
• Best practices for ETL development and managing data flows.
• Automating ETL processes for large datasets.
• Hands-on: Set up an ETL pipeline using SQL Server Integration Services (SSIS) or Talend.

Afternoon Session: Data Transformation and Integration

• Data cleansing and quality: Handling missing values, data standardization, and validation.
• Data integration strategies: Merging, joining, and aggregating data from different sources.
• Data loading strategies: Batch vs. real-time data loading.
• Hands-on: Implement data transformation techniques (filtering, sorting, and aggregation) in an ETL pipeline.

Day 3: Data Warehousing Solutions in the Cloud

Morning Session: Cloud Data Warehousing Overview

• Introduction to cloud-based data warehousing platforms: Amazon Redshift, Snowflake, Google BigQuery.
• Cloud vs. on-premise data warehousing: Benefits and challenges.
• Data storage and management in the cloud: Scalable storage solutions, cost management.
• Cloud-specific features: Auto-scaling, security, and data governance in the cloud.
• Hands-on: Set up a basic data warehouse on Amazon Redshift or Google BigQuery.

Afternoon Session: Data Warehousing with Snowflake and Google BigQuery

• Advanced features of Snowflake: Data sharing, scaling, and security.
• Introduction to Google BigQuery: Querying large datasets using SQL and integrating with other GCP services.
• Data partitioning, clustering, and optimization techniques in the cloud.
• Hands-on: Implement a data warehouse solution using Snowflake or Google BigQuery for real-time data analysis.

Day 4: Data Governance, Security, and Best Practices

Morning Session: Data Governance in Data Warehousing

• Understanding the importance of data governance: Data quality, privacy, and compliance.
• Implementing data lineage and metadata management.
• Ensuring data consistency and accuracy across sources.
• Data retention policies and archival strategies.
• Hands-on: Set up data governance practices and metadata tracking in a cloud-based data warehouse.

Afternoon Session: Data Security and Optimization

• Securing data warehouses: Access controls, encryption, and auditing.
• Best practices for managing user access: Role-based access control (RBAC).
• Performance optimization: Indexing, query optimization, and partitioning strategies.
• Hands-on: Implement basic security measures and optimize data queries in a data warehouse.

Day 5: Data Warehousing for Business Intelligence and Analytics

Morning Session: Data Warehousing and Business Intelligence (BI)

• The role of data warehousing in business intelligence: Reporting, dashboards, and analytics.
• Integration with BI tools: Power BI, Tableau, Qlik.
• Querying data warehouses for advanced analytics: Using SQL for complex analytics queries.
• Hands-on: Integrate a data warehouse with Power BI or Tableau for data visualization.

Afternoon Session: Advanced Data Warehousing Topics and Case Study

• Real-time data warehousing: Stream processing and near-real-time analytics.
• Best practices for managing large-scale data warehouses.
• Case study: Building a complete data warehouse solution for an organization.
• Group discussion: Challenges and considerations in implementing large-scale data warehousing solutions.
• Hands-on: Build and present a final case study solution incorporating data warehousing, ETL processes, cloud technologies, and BI tools.

Materials and Tools:

• Required tools: SQL Server, Snowflake, Amazon Redshift, Google BigQuery, Power BI, Tableau, Python, Talend, Apache NiFi
• Sample datasets: Sales data, customer data, financial data, and operational data.
• Recommended resources: Online guides and tutorials for cloud data warehousing platforms and ETL tools.