In olden days, the data is collected from different places and aggregated in one place. From that place, people used to get the data and process. This is good when the data is in the form of small, Why data marts play a major role in today’s life, but it’s not good if the data is in the form of large accounts for a single query it needs to fetch entire data warehouse which the lot the time if the record is at the last and the same time, if another user request for another record nearer to it, the data warehouse needs to fetch that required record too. And if multiple queries happened in a single instance, it needs to fetch that record too. At this situation, the query execution time will be delayed when compared to know the execution of the single query. To overcome this problem, data marts were introduced.
Data Mart :
Data mart: It is a part of a data warehouse where data is subjected to the particular group (or) department which is a subject-oriented database and is also known as High-performance Query structures. Through this organization have multiple data marts related to different departments like Sales, operation, HR and so on. A data mart is usually organized as a one- dimension data model as a star scheme (OLAP cube).These data marts are often controlled by a single department within an organization.
A data mart can be a standalone entity, which can be designed directly by extracting data from OLTP (or) can be linked with the central repository of the data warehouse, where it gets the data from OLTP System. With the emergence of this data mart, corporate people were able to sustain in maintaining the data as it is difficult in maintaining the data warehouses. Data marts appeared as important along with rapid growth of data warehouses.These also share the many of the data warehouse features where the scope is much smaller where the size of the data mart is about 10 gigabytes and the size of the data warehouse is about 100 GB.
Building up data mart is essential in an effective manner in order to get them quickly in a short span of time. The query efficiency depends upon the how the Data mart was built. It basically uses two types of approaches . One approach is to builds several data marts with a view for integration into the ware house and the other approach is to build the infrastructure for corporate data ware house while at the same time building the one (or) more data marts to satisfy the immediate business needs
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Bottom up – approach :
In this model , data mart is created first by extracting from the OLTP systems , and then the created data marts were integrated to gather so as to create the centralized data ware house .These data marts were built logically so as to enable the integration between data marts. This data mart is integrated with one other with in a logical enterprise dimension model. The major agenda of this approach is to deliver business value by deploying dimensional data marts as quick as possible . Its greatest advantage is to create user friendly , flexible data structure using star schema and dimensional models .
Top –Down Approach :
In this approach , the centralized , data ware house is designed first by extracting the data from the OLTP where all the data marts gets the data from the data ware house . In this data warehouse . In this approach , data ware house holds the atomic (or) transaction which is extracted from one (or) more source system and integrated with in a normalized enterprise data model . From there data is summarized , dimensionless and distributed to one (Or) more data marts which is dependent of the data ware house as the data is derived from data ware house .
Because of the above approaches and effectiveness in maintaining the data , data marts plays a major role in today’s life
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