Companies today are continually seeking better data analytics in their operations to fully harness the power of big data. One current trend in the world of business analytics is the use of a data warehouse. A data warehouse is designed to combine and analyze business data from various sources. The data warehouse is central to a business intelligence operation developed for data analysis and reporting. This information hub incorporates technologies and components that promote the strategic use of data.
This type of information warehouse combines big data and makes the information accessible for query and analysis. It is a process of turning data into information and insights and quickly producing it to inform a user’s business decisions. While it might seem like an overwhelming task to combine a host of data sources into one data store, the process might be simpler than you’d think. Let’s take a look at what data warehouses look like and how they can benefit a variety of industries.
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Reasons For A Data Warehouse
If your business decisions rely upon examining data from different sources, at some point you might need to combine your data to make things simpler. For example, you might need to consolidate information from your credit card transactions, financial information from an accounting system, and customer-generated activity data. This is a lot easier to do if your information is located in one central data warehouse location. If you need to separate your analytical data from your transactional data having a central store of data would also be helpful. Finally, you should consider a data warehouse if you want to increase the productivity of your analytical queries. Transactional data could consist of thousands of rows. To speed up the process you would want to create summary tables that combine the data into a more queryable form.
How A Data Warehouse Works
A data warehouse functions as a central repository for information derived from various data sources. Data passes into a data warehouse from the transactional system and other relational databases. The data coming in may be structured, semi-structured, or unstructured. The data is then processed, transformed, and ingested for users to access the processed information through the use of business intelligence tools. Simply put, a data warehouse combines information from different sources into a single database. By consolidating big data in one place, an organization can holistically analyze business operations. Data warehousing also aids with the data mining process. Data warehousing brings to light patterns across the data to better inform decisions and lead to higher sales and profits.
Data Warehouse Components
A typical data warehouse is comprised of four components. The load manager is sometimes called the front-end component. It is responsible for the extraction of data and loading it into the warehouse. This process also includes transformations to prepare the data before loading it into the data warehouse. The warehouse manager is designed to handle the management of the data in the warehouse. It is responsible for data analysis to guarantee consistency, creation of various views, the merging of source data, and storing and backing up data. The query manager is the third component and is also known as the backend component. The function of this component is the directing and execution of queries. The last component of a data warehouse is the end-user access tools. These tools aid with operations such as data reporting, querying, and data mining.
Types Of Data Warehouses
There are three common types of data warehouses to store and analyze data. An enterprise data warehouse (EDW) is a centralized warehouse. It offers a consolidated approach for organizing and representing data. An EDW also provides the ability to classify data by subject and can give access according to those classifications. An operational data store (ODS) is also a central database that offers a snapshot of data from multiple transactional systems. An ODS is refreshed in real-time and it is widely preferred for activities like storing employee records. The third is a data mart. A data mart is actually a subset of a data warehouse. It is a simplistic data warehouse that collects data on a single subject. These are ideal for businesses that focus on sales, finance, or marketing. Data marts are often designed and operated by a lone department within the firm.
An organization’s data warehouse is maintained separately from the operational database. Not so much a product, however, the data warehouse is an environment that evolves with the data. It provides users with current and historical data insights to offer decision-support information. Since this kind of data is difficult to access or analyze in a traditional operational data store, data warehouses have become a common tool for business intelligence.
