Connecting to Data and Performing ETL Transformations ETL (Extract, Transform, Load) is a data integration process that prepares raw data for analysis by...
ETL (Extract, Transform, Load) is a data integration process that prepares raw data for analysis by cleaning, transforming, and loading it into a data warehouse or analysis tool.
Connecting to data sources involves establishing a secure connection between your local machine and the data source (e.g., a database, CSV file, API).
Examples:
Connecting to an SQL database using SQL Server or PostgreSQL drivers.
Reading data from a CSV file using OpenCV library in Python.
Connecting to a API using a dedicated REST client library.
Transforming data involves cleaning, filtering, and organizing data according to the desired analysis requirements.
Examples:
Removing irrelevant columns and rows.
Converting date formats.
Grouping data by specific criteria.
Performing calculations on selected attributes.
Loading transformed data into a data warehouse or analysis tool involves saving it in a format compatible with the tool (e.g., CSV, Excel, JSON).
Examples:
Saving transformed data in an Excel workbook using Power Query in Power BI Desktop.
Exporting data from Power BI Desktop to a CSV file.
Loading data into a Google BigQuery table using Power BI Desktop.
Connecting to data and performing ETL transformations allows you to gain insights from diverse data sources, prepare data for analysis, and integrate it into your data pipeline for further analysis and reporting