Data Warehouse Performance
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Data Warehouse Performance
Reduce operating and maintenance costs while substantially improving the performance of new and existing data warehouses and data marts Data Warehouse Performance This book tells you what you need to know to design, build, and manage data warehouses and data marts for optimum performance. Written by an all-star team of data warehouse pioneers and innovators-including Bill Inmon, "the father of the data warehouse," and Ken Rudin, one of the leading experts on performance-the book describes the layers of a high-performance data warehouse environment and guides the reader through their implementation and management. It also supplies proven techniques for supercharging the performance of existing environments. Crucial topics covered include: * Mitigating the impact of dormant data on performance * Data cleansing and implementation techniques * Implementing platform components like data marts to support scalability * Database design, sizing, and optimization techniques, including star schema and indexing * Hardware assessment, selection, and sizing * The role of monitors in balancing workload and assessing performance * Creating a service management contract to meet user expectations
Simple Strategies to Improve Data Warehouse Performance
Data warehouse management is fast becoming one of the most popular and important topics in industries today. For business executives, it promises significant competitive advantage for their companies, while presenting the information system managers a way to overcome the obstructions in providing business information to managers and other users. Here the company is going through the problem of inefficient performance of its data warehouse. To find an appropriate solution to this problem we first try to understand the data warehouse concept and its basic architecture, followed by an in depth study of the company data warehouse and the various issues affecting it. We propose and evaluate a set of solutions including classification of suppliers, implementing corporate commodity classification and coding system, obtaining level three spend details for PCard purchases, etc. The experimental results show considerable improvement in the data quality and the data warehouse performance. We further support these recommendations by evaluating the return on investment for improved quality data. Lastly, we discuss the future scope and other possible improvement techniques for obtaining better results.
Data Warehouse Essentials
Author: Virversity Online Courses
language: en
Publisher: eBookIt.com
Release Date: 2025-03-26
Data Warehouse Essentials is a comprehensive course designed to empower individuals with a robust understanding of data warehousing concepts, architecture, and best practices. Whether you're an aspiring data professional or looking to improve your data management skills, this course provides the essential knowledge needed to design, implement, and optimize a data warehouse effectively. Master the Fundamentals of Data Warehousing Understand core data warehousing concepts and their importance in modern data management. Gain insights into key architectural components and design considerations. Learn data modeling techniques, including ER models and star schemas. Explore the essentials of ETL processes and integrating multiple data sources. Discover popular data warehousing tools and technologies. Develop skills for querying, reporting, and optimizing data warehouse performance. Ensure security, compliance, and governance in data warehousing. Identify the relationship between big data and data warehouses. Comprehensive Guide to Data Warehousing Concepts and Techniques This course begins with an introduction to data warehousing, providing an overview of its fundamental concepts, highlighting the significant impact they have in today's data-driven decision-making processes. Students will delve into the architecture of data warehouses, examining key components and the various design considerations crucial for creating efficient systems. As learners progress, they will explore data modeling techniques, including Entity-Relationship (ER) models and star schemas, which are integral for organizing and structuring data within a warehouse. The course further covers essential ETL (Extract, Transform, Load) procedures necessary for data integration and cleansing, ensuring a smooth and accurate data flow into the warehouse. The practical skills taught extend to the use of popular data warehousing tools and technologies, aiding students in navigating the current technology landscape. Additionally, the course addresses the critical areas of querying and reporting, providing techniques for effective data analysis and visualization. Individuals will gain insights into performance optimization strategies, essential for maintaining the efficiency and speed of data warehouse operations. Furthermore, the course emphasizes security and data governance, critical for ensuring that data warehouses meet compliance requirements and remain secure. Finally, participants will explore the dynamic interaction between big data and data warehousing, equipping them with the knowledge to handle modern data complexities effectively. By the end of the course, students will transform their data management capabilities, becoming proficient in designing and managing robust data warehouses that support strategic business objectives.