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We're here to help you find itThe Oracle Database 19c: Data Warehousing Concepts course provides a comprehensive insight into the world of data warehousing, focusing on the evolution and management of information. It is designed to help learners understand the architecture and processes that underpin effective data warehouses.
Module 1 sets the stage with a course overview, delineating what students can expect to learn.
In Module 2, learners delve into the Evolution of Information Management and data warehousing, exploring the requirements for business intelligence and how they have shaped modern data practices.
Module 3 provides an overview of data warehouse and Multidimensional Model Concepts, detailing various architectures and the components of the data warehousing process.
Module 4 covers the different types of models - business, logical, dimensional, and physical - and discusses primary key and foreign key relationships, as well as the importance of determining Granularity in Data.
Module 5 introduces the concepts of extracting, transforming, and loading data (ETL), which are critical for building and maintaining a data warehouse.
Finally, Module 6 offers an introduction to various data warehousing platforms and tools that can be utilized in conjunction with Oracle Database 19c.
Overall, this course equips learners with the knowledge to design, implement, and maintain an efficient data warehouse, a skill that is highly valuable in the era of big data and analytics.
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You can request classroom training in any city on any date by Requesting More Information
To ensure that participants can maximize their learning experience and successfully undertake the Oracle Database 19c: Data Warehousing Concepts course, the following are the minimum required prerequisites:
It is not necessary for participants to have advanced IT skills or extensive experience with Oracle Database technologies prior to taking this course. The curriculum is designed to introduce and build upon each concept methodically, making the course suitable for individuals who meet the above prerequisites.
Oracle Database 19c: Data Warehousing Concepts course is designed for IT professionals focusing on data management and analytics.
Target audience for the Oracle Database 19c: Data Warehousing Concepts course:
Introduction: This Oracle Database 19c: Data Warehousing Concepts course provides a comprehensive understanding of data warehousing, its evolution, architecture, modeling, and the tools and platforms used for efficient data management.
Learning Objectives and Outcomes:
Data warehousing is a technology used to consolidate data from different sources into a central repository to enable better decision-making, reporting, and data analysis. It involves collecting, cleansing, and storing large volumes of information in a structured format. Organizations can then use this data to conduct complex queries and generate reports efficiently. Oracle Data Warehousing provides a robust solution for managing these vast data repositories, optimizing data retrieval, and ensuring data integrity, thus supporting strategic business decisions and operations.
The evolution of Information Management encompasses the progressive development of methods and technologies for efficiently collecting, storing, and analyzing data to inform business decisions. Historically, this began with manual record-keeping, evolved through the eras of mainframe computers and database management systems, to today's sophisticated digital databases and data warehousing solutions, including platforms like Oracle Data Warehousing. These advancements enable organizations to handle vast amounts of structured and unstructured data, derive insights through advanced analytics, and support strategic operations and decision-making processes in a data-driven landscape.
Multidimensional model concepts relate to how data is organized and structured in a way that allows for complex analyses and quick retrieval. In essence, data is stored in multi-dimensional arrays, resembling a cube. Each dimension represents a different aspect of the data, such as time, location, or product categories, enabling efficient processing and analysis for tasks like forecasting and trend analysis. This model is particularly useful in scenarios like Oracle data warehousing, where handling large volumes of data from various sources needs to be swift and efficient for business intelligence.
Granularity in data refers to the level of detail or precision contained in the pieces of data stored in a database. In the context of Oracle data warehousing, granularity is significant because it affects both the storage size and the speed of data retrieval. Finer granularity, meaning more detailed data, provides deeper insights but requires more storage and processing power. Coarser granularity, with less detail, improves performance and reduces storage requirements but may limit the depth of analysis you can perform. The choice of granularity level impacts how effectively data can be analyzed and utilized in decision-making processes.
Data warehousing platforms are specialized systems used to capture, store, and analyze large volumes of data. These platforms work as central repositories where data from different sources is consolidated, enabling businesses to perform complex queries and analysis. Tools associated with these platforms help in managing data, ensuring it is cleaned, transformed, and readily available for business intelligence tasks. Oracle Data Warehousing is a popular choice, providing robust solutions to handle large-scale data operations efficiently, supporting decision-making processes by providing comprehensive data insights.
Oracle Database 19c: Data Warehousing Concepts course is designed for IT professionals focusing on data management and analytics.
Target audience for the Oracle Database 19c: Data Warehousing Concepts course:
Introduction: This Oracle Database 19c: Data Warehousing Concepts course provides a comprehensive understanding of data warehousing, its evolution, architecture, modeling, and the tools and platforms used for efficient data management.
Learning Objectives and Outcomes:
