Day 10. ETL development by an example

First 35 minutes of the video are a descriptive introduction to the ETL/ELT process. You can freely skip this part if you understand meaning raw data import process.

ETL stands for Extract, Transform, Load, which is a data integration process used to move data from various sources, transform it into a format that can be easily analyzed, and load it into a destination database or data warehouse.

The extract phase involves retrieving data from various sources, such as databases, files, or web services.

The transform phase involves cleaning, filtering, and formatting the data so that it can be used effectively.

The load phase involves loading the transformed data into a target database or data warehouse, where it can be analyzed and used for business intelligence or other purposes.

Second part of the video is an introduction to data profiling. The speaker tells us about common conceptions and first steps used for data profiling. We take a first look at staging tables and data import query for our's case staging table.

Staging tables in SQL are temporary storage areas used to hold data before it is loaded into the final destination tables. These tables are often used to cleanse and transform data, ensuring that it is consistent, accurate, and ready for analysis. Staging tables can also be used to merge data from different sources or perform data validation checks before the data is loaded into the final destination tables. Once the data has been processed in the staging tables, it can then be loaded into the final tables using SQL commands such as INSERT, UPDATE, or DELETE. Overall, staging tables play a crucial role in the ETL (Extract, Transform, Load) process of data warehousing and help ensure that data is of high quality and suitable for analysis.