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Overview of CDR Views and Computed Fields11/20/2023This document lists the CDR views available to the standard user group of the CDR data warehouse. It also defines computed fields, derived from CDR
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How to fill out building a data warehouse

01
Step 1: Determine the business objectives and requirements for the data warehouse.
02
Step 2: Identify the data sources that will be used to populate the data warehouse.
03
Step 3: Design the schema of the data warehouse, including the tables and relationships between them.
04
Step 4: Extract data from the identified data sources and transform it into a format suitable for the data warehouse.
05
Step 5: Load the transformed data into the data warehouse.
06
Step 6: Implement processes for ongoing data maintenance and cleansing.
07
Step 7: Provide access to the data warehouse through query and reporting tools.
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Step 8: Continuously monitor and optimize the performance of the data warehouse.

Who needs building a data warehouse?

01
Businesses and organizations that deal with large volumes of data.
02
Companies that need to consolidate and integrate data from multiple sources.
03
Organizations that require a centralized and reliable data repository.
04
Enterprises that need to support data analysis, reporting, and decision-making processes.
05
Businesses that want to improve data quality and consistency across different systems.
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Building a data warehouse involves the design, construction, and management of a centralized repository of integrated data from various sources, optimized for query and analysis.
Typically, organizations or businesses that accumulate large amounts of data from different operational systems are required to file building a data warehouse.
Filling out a data warehouse involves defining data sources, extracting and loading data into the warehouse, and organizing the structure for easy access and reporting.
The purpose of building a data warehouse is to enhance data analysis and reporting capabilities, support decision-making processes, and ensure efficient data management.
Information that must be reported includes data sources, data schemas, ETL (extract, transform, load) processes, and performance metrics of the data warehouse.
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