Operational systems vs data warehousing : we started talking about data warehousing concept. If you already have some working experience of Operational data (Production data) , you must be wondering how different will be the Data warehouse data compared to operational Data . So in below tutorial , we will give some basic comparison of Data warehouse data and Operational data.
How is Data warehouse different from Operational data?
The data warehouse is distinctly different from the operational data used and maintained by day-to-day operational systems. Data warehousing is not simply an “access wrapper” for operational data, where data is simply “dumped” into tables for direct access.
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Operational systems vs Data warehousing
The fundamental difference between operational systems and data warehousing systems is that operational systems are designed to support transaction processing whereas data warehousing systems are designed to support on-line analytical processing (or OLAP, for short).
Based on this fundamental difference, data usage patterns associated with operational systems are significantly different than usage patterns associated with data warehousing systems. As a result, data warehousing systems are designed and optimized using methodologies that drastically differ from that of operational systems.
|OPERATIONAL DATA||DW DATA|
|application oriented||subject oriented|
|detailed summarized||otherwise refined|
|accurate, as of the moment of access||represents values over time, snapshots|
|serves the clerical community||serves the managerial community|
|is be updated frequently||is not updated (Frequently)|
|run repetitively and non reflectively||run heuristically|
|requirements for processing understood before
| requirements for processing not completely
understood before development
|compatible with the Software Development Life
|completely different life cycle|
|performance sensitive (immediate response
required when entering a transaction)
|performance relaxed (immediacy not required)|
|accessed a unit at a time (limited number of data
elements for a single record)
| accessed a set at a time (many records of many data
|transaction driven||analysis driven|
|control of update a major concern in terms of ownership||control of update no issue|
|high availability||relaxed availability|
|managed in its entirety||managed by subsets|
|nonredundancy||redundancy is a fact of life|
|static structure; variable contents||flexible structure|
|small amount of data used in a process||large amount of data used in a process|
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So in above small tutorial , we have gone through the same basic difference between data warehouse data and operational data.