Data Warehouse Architecture. Differentiate Data Warehouse and Data Mart.

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written 7.8 years ago by

aartisahitya • 160

modified 7.8 years ago by

ravinaisrani • 90

Data Warehuse Architecture:

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The data has been selected from various sources and then integrate and store the data in a single and particular format.
Data warehousescontain current detailed data, historical detailed data, lightly and highly summarized data, and metadata.
Current and historical data are voluminous because they are stored at the highest level of detail.
Lightly and highly summarized data are necessary to save processing time when users request them and are readily accessible.
Metadataare “data about data”. It is important for designing, constructing, retrieving, and controlling the warehouse data.
Technical metadatainclude where the data come from, how the data were changed, how the data are organized, how the data are stored, who owns the data, who is responsible for the data and how to contact them, who can access the data , and the date of last update.
Business metadatainclude what data are available, where the data are, what the data mean, how to access the data, predefined reports and queries, and how current the data are.

Characteristic	Data warehouse	Data Mart
Data Scope	data warehouses save all kinds of data related to system	data marts just store specific subject information, becoming much more focused on these functionalities.
Size	Based on the previous premise we can say that a data warehouse is usually much bigger than data marts, because it keeps a lot more data.	Data Mart is small in size
Integration	a data warehouse usually integrates several sources of data in order to feed its database and the system’s needs	a data mart has a lot less integration to do, since its data is very specific.
Creation	Creating a data warehouse is way more difficult and time consuming than building a data mart. Building all the structure a relationships between data its a long and very important step. Plus you need to think and analyse how you will integrate all of your information sources	Since data marts are smaller and subject oriented, these actions tend to be much simpler.
Management	the management of data warehouses is far more complex than data marts. For the same reasons stated above, it is obvious that when you have a lot more data, relationships, processes to manage, it becomes a harder task.	less management
Cost	To build and mantain a data warehouse you need significantly more physical resources like servers, disk space, memory and cpu. Due to the complexity of the systems, a data warehouse requires more time to build and operate. So, since time is money, we can easilly reach to our conclusion.	in terms of cost, data marts are cheaper than data warehouse
Performance	The performance of a system always depends on how it is built, the infrastruture which supports it, the processes, the number of users, etc. However, due to some previous conclusions, is safe to say that usually a data warehouse is less performance because of the inherited complexity.	The performance of a system always depends on how it is built, the infrastruture which supports it, the processes, the number of users, etc. However, due to some previous conclusions, is safe to say that usually a data mart is more performance

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