Data warehousing is a process used to collect and manage data from multiple sources into a centralized repository to drive actionable business insights. With all your data in one place, it becomes simpler to perform analysis and reporting at different aggregate levels.
Data warehousing is the process of consolidating all the organizational data into one common database. On the other hand, data analytics is all about analyzing the raw data and driving conclusions from the information gained. The concepts are interrelated but different.
The following steps are involved in the process of data warehousing: Extraction of data – A large amount of data is gathered from various sources. Cleaning of data – Once the data is compiled, it goes through a cleaning process. The data is scanned for errors, and any error found is either corrected or excluded.
There are four major
processes that contribute to a
data warehouse − Extract and load the data. Cleaning and transforming the data. Backup and archive the data. Managing queries and directing them to the appropriate data sources.Data is ingested from multiple sources, then cleansed and transformed for other applications to use in a process called
extract, transform, and load (ETL). The bottom tier is also where data is stored and optimized, which leads to faster query times and better performance overall.The following steps are involved in the process of data warehousing:
Extraction of data – A large amount of data is gathered from various sources. Cleaning of data – Once the data is compiled, it goes through a cleaning process. The data is scanned for errors, and any error found is either corrected or excluded.
The data warehouse process is a multi-step process that involves the following steps:
- Data Extraction: The first step in the data warehouse process is to extract data from various sources such as transactional systems, spreadsheets, and flat files.
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