Introduction to Data Warehousing & Architecture and Design

10 Hours

Beginner

10 Modules

LE 3,999.00
LE 6,600.00
LE 3,999.00
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Introduction to Data Warehousing & Architecture and Design Data warehousing is a critical component of modern data management strategies, focusing on the systematic collection, storage, and analysis of vast amounts of organizational data. At its core, a data warehouse serves as a centralized repository where data from diverse sources—such as transactional databases, external systems, and historical records—is aggregated to support data analysis and decision-making. Key Components of Data Warehousing: ETL (Extract, Transform, Load): Data is extracted from various sources, transformed to ensure quality, and then loaded into the warehouse. This process allows consistent, cleansed, and integrated data to be stored. Data Marts: These are specialized subsets of data warehouses tailored to specific business functions (e.g., sales, finance) to enhance query performance for department-specific needs. OLAP (Online Analytical Processing): A technology supporting complex queries and analytical reporting, OLAP enables users to perform multi-dimensional analysis for insights into trends, patterns, and KPIs. Data Warehouse Architecture and Design: The architecture of a data warehouse is designed to ensure efficient data flow, scalability, and ease of access. Key layers include: Data Source Layer: Data is collected from internal and external sources, such as relational databases, flat files, APIs, and other operational systems. Staging Layer: This temporary area stores raw data, where ETL processing occurs. Data Storage Layer: Data is organized in the warehouse using structures such as star and snowflake schemas, which facilitate quick querying and reporting. Presentation Layer: This layer provides data access to end-users through BI tools, dashboards, and reporting systems for analysis and visualization. Data Warehousing Benefits: Improved Decision Making: Data warehousing consolidates data to provide a comprehensive view, helping organizations make data-driven decisions. Historical Analysis: It stores historical data, allowing users to track changes over time and perform trend analysis. Performance Optimization: Data warehousing is optimized for query performance, making it easier to retrieve data compared to transactional systems. Data warehousing and its architecture form the backbone of advanced analytics and BI, offering structured and reliable insights that enable businesses to drive growth and innovation.
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151Days 8Hours 23Minutes 0Seconds
Vendor: Cloud Learning Academy
Categories: Data Warehouse (DWH),
Product Type: DWH Course
    • Course Outline
    • What you will learn
    • Audience profile
    Topics: What is a Data Warehouse? Key concepts, differences between a Data Warehouse and traditional databases. Objectives: Develop a foundational understanding of what data warehousing entails and its significance.
    Topics: Importance of data warehousing in BI, use cases, benefits. Objectives: Learn how data warehouses support BI and real-world applications.
    Topics: Introduction to ETL (Extract, Transform, Load), core concepts. Objectives: Understand ETL’s role in consolidating data from different sources.
    Topics: Data Warehouse Architecture, OLTP vs. OLAP, Three-tier architecture (data source, integration, and presentation layers). Objectives: Learn to distinguish different system architectures and their application in data warehousing.
    Topics: Comparison of data lakes and data warehouses, when to use each. Objectives: Gain insights into data storage solutions and select the best fit for specific scenarios
    Topics: Overview of Data Warehouse architectures, differences in architectures. Objectives: Gain an understanding of the structural frameworks for designing data warehouses.
    Topics: Kimball methodology (dimensional modeling), Inmon methodology (normalized relational model), hybrid approaches. Objectives: Compare and contrast the methodologies, understand when to apply each.
    Topics: Range, list, and hash partitioning techniques, indexing types, and their importance in performance. Objectives: Learn partitioning techniques to optimize data retrieval and improve storage.
    Topics: Indexing strategies, performance-enhancing techniques. Objectives: Understand best practices for indexing and how they impact data query speeds and storage.
    Topics: Columnar storage, row-based storage, storage optimization techniques. Objectives: Understand different storage formats and how to optimize for different data types and uses.
    Upon completing these modules, you will be able to: Define and distinguish data warehousing and traditional databases. Explain the importance of data warehouses in supporting business intelligence. Describe the core elements of ETL processes and how they enable data integration. Understand key architectural frameworks, including OLTP and OLAP. Evaluate the differences between data lakes and data warehouses and select the best storage solution for a given scenario.
    This module series is designed for: Data analysts and data engineers seeking foundational knowledge of data warehousing. Business intelligence professionals looking to understand data warehousing’s role in BI. IT professionals and decision-makers interested in leveraging data storage solutions effectively.

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