Data warehousing is a modern technology term, popularly used within the data analysis field, which is used to reference a data reporting and analysis system. Data warehouses are considered critical components in business intelligence because it is based on these systems that crucial and strategic business decisions can be made. The systems support both structured queries and unstructured (ad-hoc) queries and are composed of composite data stored in central repositories and made available for querying whenever the need arises. In this day and age, big and crucial decisions are increasingly being made based on large amounts of data. The growth of technology and computing power has enabled organizations to amass large data and exploit this data whenever they want to analyze the market and make decisions that affect both their daily operations and long term operational efforts.
With data warehousing, organizations are encouraged to collect all kinds of operational data from their various points of operations and all this data is stored in repositories. Whenever these organizations need to make decisions that affect their operations they query the data warehouses to obtain the required analyzed data, which they can alter their policies or create new ones to foster their business development efforts. The term data warehousing is not only a concept, but it is also used to refer to various techniques that are used in the collection of the data and in the management of this data.
Data warehouse Concepts
A Data Warehouse (DWH) works in pretty much the same way as a goods store; in this case, a data store. However, a data warehouse is unique as it also includes mechanisms for querying this data, and it also has analysis tools that enable the users to analyze the data useful in providing the relevant results for any queries they may subject to the warehouse.
Data is classified into three types in data warehouses. These classifications include:
- Unstructured data
Data in warehouses originate from different sources that affect an organization’s operations. This includes both the internal organizational operations as well as the external environment conditions and operations that may directly or indirectly affect an organization’s business operations.
The data warehouse then processes the data coming into the system in a way that it can be queried to provide results that are usable for business decision-making. A data warehouse is advantageous for business decision-makers because it enables them to perform data analysis in a holistic way that considers all the aspects that affect the business decision, as well as the ones that will be affected by the decision.
Types of data warehouses
Data warehouses are mainly classified into three types. These include:
- Enterprise Data Warehouse (EDW)
- Operational data store
- Data mart
Enterprise data warehouse (EDW)
This is a centralized warehouse and provides all the concepts that are related to warehousing technology. These include offering a unified approach to all the data affecting an organization, classifying the data into subjects and providing access to these subjects based on the divisions. This allows for efficient query processes that are fast and effective.
Operational Data Store
These are mini-warehouses and includes data stores that are required during normal operations and they provide organizations with information that is accessed on a daily basis and is normally refreshed frequently. An example of information that is frequently accessed during organizations’ operations includes the employees’ storing records.
A data mart, on the other hand, is section of a data warehouse and is a specialized form of a data warehouse that is built to address the needs of a particular department in on organization such as finance and/or sales. Whereas it is considered a subset of the general warehouse, the data mart can collect its own information directly from the sources and use the information for making operational business decisions affecting that department.
Data Warehouse Tools
There are several data warehouse tools that are available in the market. In this article, the three main ones will be listed and described. These include:
- Amazon RedShift
This tool uses an array of enterprise features to ease and speed up data integration within a data warehouse. The tool is also capable of performing complex searches and querying of the data warehouse.
The tool is effective at the optimization of customer experiences as it is built to enhance data warehouse operational efficiency. Oracle, the company building the tool is the leading database company in the world.
This is a simple tool mostly used in the analysis of data and can analyze using standard SQL tools. However, the tool can also allow for the running of complex queries using query optimization techniques.
Data warehouse Vs. Database
While Data warehouses and databases are largely similar, the two technologies also have glaring differences that differentiate one from the other. For instance, the purposes for building these two systems are different. Data warehouses are meant to store large quantities of organizations’ historical information and are meant to allow fast querying of this information for strategic business decision-making.
On the other hand, a database is meant for storage of current information and data that is accessed on an ongoing basis for transactional operations. While databases are optimized for speed of updating records, the data warehouses are optimized for complex query processing as larger quantities of information need to be accessed and interpreted before results are obtained.
Data warehousing is among the most popular and modern computer science topics that learners need to fully understand due to their varied applicability across various fields and organizations. It is imperative, therefore, for learners to seek online computer science help whenever they face difficulties in the many concepts relating to this topic.