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Introduction data warehouse
Data warehouse is a relational database management system (RDBMS) construct to meet the requirement of transaction processing system. It can be loosely described as any centralized data repository which can be queried for business benefits. It is a database that store information oriented to satisfy decision making requests. It is a group decision support technology, targets to enabling the knowledge worker to make superior and higher decisions. Data warehousing assignments is quite challenging for most students. That’s why our expert writers and researchers are available in Assignmentsguru team to help you with your data warehouse assignment.
Data warehouse environment contains an extraction, transport, and loading solution, an online analytical processing (OLAP) engine, customer analysis tools, and other applications that handle the process of gathering information and delivering it to business users. Data warehouse is not used for daily operations and transaction processing but used for making decisions.
Data warehouse can be viewed as a data system with following attributes:
- It supports a relatively small number of clients with relatively long interactions.
- It includes current and historical data to provide a historical perspective of information.
- Its usage is read-intensive.
- It contains a few large tables.
- It is database designed for investigative tasks, using data from various application.
Benefits of a data warehouse
Data warehouse offer the overarching and unique benefit of allowing organizations to analyse large amounts of variant data and extract significant value from it as well as to keep historical record.
A well-designed data warehouse will perform queries very quickly, deliver high data throughput, and provide flexibility for end user to reduce the volume of data for closer examination to meet variety of demands-whether at high level or at very fine, detailed level.
Data warehouse is servers as the functional foundation for middleware BI environments that provide end user with reports, dashboard, and other interfaces.
Data warehouse analyses change overtime. Once the data is in warehouse its stable and doesn’t change. It creates constituency among different data types from desperate sources. They can analyse data about particular subject or functional area.
Characteristics of data warehouse
A data warehouse target on the modelling and analysis of data for decision makers. Therefore, data warehouses typically provide concise and straightforward view around a particular subject, such as customer product, or sales instead of global organization ongoing operations. This is done by excluding data that are not useful concerning the subject and including all data needed by the user to understand the subject.
A data warehouse integrates various heterogeneous data sources like RDBMS, flat files, and online transaction records. It requires performing data cleaning and integration during data warehousing to ensure consistency in naming conventions attributes types among different data sources.
Historical information is kept in data warehouse. For example, one can retrieve files from 3 months, 6 months, 12 months, or even previous data from a data warehouse. This variation with transaction system where often only the most current files kept.
The data warehouse is a physical separate data storage, which is transformed from the source operational. RDBMS. The operational updates of data do not occur in the Data warehouse, i.e., update, insert, and delete operations are not performed. It usually requires only two procedures in data accessing: Initial loading of data and access of data. Therefore, the data warehouse requires transaction processing, recovery, and concurrency capabilities, which allows concurrent capabilities.
Data warehouse architecture.
The architecture of data warehouse is determined by the organization’s specific needs. Those architecture include.
- Simple- All data warehouse shares basic design in which metadata summary data, and raw data are stored within central repository of the warehouse. The repository is fed by data sources on one end and access by end user for analysis, reporting and mining on the rather end.
- Hub and spoke- adding data marts between the central repository and end user allow an organization to customize its data warehouse to serve various lines of business. When the data is ready for use. It is moved to the appropriate data mart.
- Sandboxes are private, secure, safe areas that allows companies to quickly and informally explore new data sets or way of analyzing data without having to conform to or comply with the formal rules and protocol of the Data warehouse
- Simple with strategic area. Operational data must be cleaned and processed before being put in the warehouse although this can be done programmatically Many data warehouse add staging area data before it enters the warehouse, to simplify data preparations.
Designing a data warehouse
When an organization sets out to design a data warehouse. It should start by defining specific business requirements, agreeing on the scope, and drafting a conceptual design. The businesses organisation will then develop both logical and physical design for the data warehouse. The logical design involves the relationships between the objects, and the physical design involves the best way to store and retrieve the objects. The physical design also incorporates transportation, backup, and recovery processes
Any data warehouse design must address the following:
- Specific data content
- Relationships within and between groups of data
- The systems environment that will support the data warehouse
- The types of data transformation required
- Data refresh frequency.
A key factor in design is the needs of the end user. Most end users are interested in performing analysis and looking at data in aggregate. Instead of individual transactions. However., often end users don’t really know what they want until specific need arises enabling the planning process should include enough exploration to anticipate needs. Lastly the data warehouse design should allow room for expansion and evolution to keep pace with the evolving needs of end users.
Need for data warehouse
- Store historical data: data warehouse is required to store the time variable data from the past. This input is made to be used for various purposes.
- Make strategic decisions. Some strategies may be depending upon the data in the data warehouse. So, data warehouse contributes to making strategic decisions.
- For data consistency and quality: Bringing the data from different sources at commonplace, the user can effectively undertake to bring the uniformity and consistency in data.
- High response time: data warehouse has to be ready form somewhat unexpected loads and types of queries, which demands a significant degree flexibility and quick response.
Why choose oracle Autonomous data warehouse over snowflake.
- Automation-The only data warehouse fully automates database administration.
- Ease of use. The autonomous data warehouse solution is simpler to deploy and manage with built-in capabilities that remove the need for additional standalone services
- Data security – we provide stronger built-in security protocols that protects your data against cyber threats.
- Data governance- Our data warehouse platform makes it seamless for organization to manage data sovereignty.
- Cost of solution- our modern data warehouse and enhanced feature have similar costs to similar workload requirements.
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