Types of Data Warehouse

Types of Data Warehouse

Types of Data Warehouse are

  • Informatica process
  • Data mining and
  • Analytical process

Informatica process: A data warehouse allows in processing the data and store it. This data will be processed by the way of a query, cross tabs reporting, basic statistical analysis, tables, graphs and charts.

Analytical process: A data warehouse supports all other analytical process of the information which is stored in a data. The data can be analyzed and OLAP operations included in drill up, drill down.

Data mining: It supports with knowledge discovery to find the hidden associations and patterns for building the analytical models, prediction and to perform with classification. All these mining results will be presented for using virtualization tools.

Usage of data warehouse

There are various technologies to support and utilize the data available in a data warehouse. All these technologies will be used for warehouse effectively and used. They gather the data and analyze it to make decisions which are based on information which is presented for warehousing. The information gathered here can be used in the following domains,

Strategies of tuning production

The product strategies will be tuned by changing the product and manage the product by comparing it with the sales half and yearly.

Customer analysis

It is done for analyzing the consumer’s buying preferences, cycles of budget, buying time etc.

Operations analysis: Data warehousing also helps in consumer relationship management and also make environment.

Data warehousing tools and functions

The following are the functions of data warehousing tools and functions,

Data extraction: It also involved by gathering the data information with multiple heterogeneous sources.

Data cleaning: It is involved in finding the errors in a data.

Data transformation: It is also includes in converting the data from legacy format

Data loading: It includes with consolidation, sorting, constructing indices, summarize, partitions etc.

Refreshing: It includes the updating from data sources to warehouse. A data cleaning will be important in all other data warehouse.

Data mart: It contains the sub set of organization with wide data fields which is valuable for the specific groups of people in an organization. In other words, a data mart will be included in all other data marts that are specifically used with a particular group. For example, the marketing data may contain only the related data to consumers, items and sales. Data marts are used to subjects.

A data warehouse includes all the consolidated data with various views. A data warehouse provides OLAP tools. All these tools help interactively and effectively and results in data generalization and data mining. The function of data mining is to cluster, associate, classification and prediction. It is to enhance with interactive mining for knowledge with various levels of abstraction. So that data warehousing become very important for data analysis and with Online Analytical Processing tool.

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