`Data comes from a wide range of sources. It can be relational or non-relational, the connectivity can be unstable, and there are also many other issues when data has to be extracted from data sources. This is why developers, statisticians, and data scientists should never entirely believe in the quality of the source data. This chapter explains the techniques for data transformation and cleansing using Transact-SQL (T-SQL) language.
The following topics will be covered in this chapter:
- The need for data transformation: This section presents the main goal of data transformation for data science purposes and, using examples, also provides several cases of what could happen to incoming data.
- Database architectures for data transformations: Data transformations can vary from very simple to very complex. That's why it's necessary...
