What is the use of OLAP
Being a part of BI, OLAP technology, developed to enable fast extraction of requisite data from the data warehouse, is populated from OLTP databases (CRM, ERP, AR, AP etc.) through ETL tools. Online Analytical Processing systems, offered today by multiple BI vendors, deliver great opportunities for data mining, potential trends analysis, and reporting. Beyond that, OLAP makes it available to carry out data modeling, perform business planning and forecasting, adopt budgeting and draw up financial reports. Owing to the system a user is able to conduct ad hoc analysis and apply ‘what if’ scenarios. All of that will bring up the insights to run a successful business.
OLAP represents required information though MDX queries. In order to provide rapid complex query processing and data pre-calculation, OLAP must be flexible, persistent and robust. These features of the system rely on the way the data has been transferred from the primary database and thereafter organized in a multidimensional structure.
In this article, we are going to go into detail on the subject of ETL in data mining, its main aspects, pitfalls and use.
To set up an OLAP system one can’t dispense with ETL tools, which as the name implies include three phases of data management: extract, transform and load. ETL tools aim to facilitate the process of data administration including the procedure of its retrieval from the source database, its conversion and loading the arising information into the OLAP system.
The first stage of ETL is the retrieval of the data from the primary sources which can be hosted on different servers. Commonly this part is considered to be the most significant as it has a straightforward impact on further processes connected with data. It involves data validation and conversion into a unified format suitable for subsequent transformation.
During this ETL phase, certain rules are being imposed on the data retrieved to make it ready for further loading. One more feature applied here is cleansing to meet the requirements of a future data warehouse.
The last step of ETL is to deliver and locate data in the final OLAP warehouse. Sometimes this stage can take quite a long time depending on the needs of a user and the warehouse they exploit. Some warehouses rewrite and update the information, replacing it with aggregated one. Meanwhile, others append new information to the
existing data to maintain the history of data changes if there’s such a default setup.
The volume of the data increases faster and faster with the time thereby affecting the load of a user’s whole system, whether it is a plant, an enterprise or a company. Pliable and meaningful data management is one of the crucial points to keep up with the trends. ETL data processing focuses on providing paramount methods for data transfer and the way these processes are run has a huge impact on a user’s system performance, reliability, and promptness.
Although it is possible to extract data out of nearly any source like Oracle, SQL Server, SAP, Access, Excel and others, there is a hidden rock to remember finishing data migration successfully. The pitfall may occur from data warehouse schema design.
As the data can be forthcoming from absolutely various databases, views and tables all the information may range in its residence, configuration, and host servers. That is why we have to refer ETL for seamless data migration. Common ETL tools will provide a user with several beneficial key features among which there are environment control, load balancing, integrated source control, task execution analysis, file and database error capture and others. Those controls are up to help implement some inconsistent, null and void data into a new system. Furthermore, ETL in data mining gives an opportunity to determine and decant bugs which could be manual mistakes or data corruptions. When transferred, all migrated data will remain coherent, due, and integrated. It will be kept in a conformable and relevant format in the data warehouse. Afterwards, a user can smoothly amend the source system without compromising data accuracy.
As we have already mentioned ETL serves as an imminent intermediary process to provide data migration into an OLAP system. The purpose of these tools is to ensure data consistency without loss and error. Good news lies in the fact that ETL tools have been developed for end users dealing with business, therefore they don’t have to seek the assistance of the technical specialists in I.T., or be good at SQL scripting.