What is the data warehouse? First, it should be known that data warehouse is not a program or product. The data warehouse is an architecture that is a medium. The data warehouse is a repository for collectors and historical data in an understandable and easily accessible structure after receiving, cleaning and replacing data from different operational systems, call centers and similar sources. In other words, data warehouse is a relational database designed to be used for querying and analysis rather than database movement. In general, it may include historical information from motion data, as well as information from other sources. With the workload of database movement, the analysis distinguishes the burden from each other, allowing the information gathered from different sources to be organized more easily. As I mentioned above, the information to be transferred to the data warehouse passes through a number of operations before being transferred to the data warehouse. The data passes through the ETL process before entering the data warehouse. In this way, depending on how to use the given data, the desired format is inserted. Solutions So what is this ETL? First, let’s look at the opening of the ETL. ETL; Extract: Receiving data from the source system, Transform: The data have to go through certain transformations in order to be appropriate for our production. That is to say, cleaning and improving the quality of a certain kind, Load: means that the data is loaded into the target system. ETL in brief; the data is retrieved from the source system, changed accordingly, and loaded into the data warehouse. Another data quality method is ELT. ELT (Extract Load Transform) is; the data is again taken from the source system, but this time the transform is performed after loading into the system. ELTA With these transactions, Data Cleaning, Data Conforming, It’s called Data Quality. So why is the quality of the data warehouse so important? If the data in the data warehouse were very irregular and were in a situation where it was not possible to operate on the allele, we could get wrong results in our queries. For example; When we want to choose unmarried women over 18, the data in the gender block are female, female, May be entered in shapes. In this case, we will have lost a significant amount of future data when we take our inquiry as a lady.… Read More »