Have you ever tried to solve a jigsaw puzzle when the pieces are scattered everywhere and you don't even know if those pieces belong to the same puzzle? That's what it can feel like as a data analyst tasked with extracting insights from data that spread across multiple sources, formats, and structures. Not to worry, there's a way to solve this problem. The Extract, Transform, Load, or ETL process. In this video, you'll build on your knowledge of the ETL process. You'll explore the three main components of the ETL process and how to apply them. The benefits of using the ETL process and how it's performed using Microsoft Power BI. As you learnt earlier in this course, ETL stands for extract, transform and load, the names given to the three main steps in the ETL process. This process involves taking raw data from various sources, preparing it for analysis, and loading it into a repository or data storage and management system. Let's explore each step of the ETL process in more detail, and how they can be applied in the scenario of the manufacturing company AdventureWorks, which produces and distributes bicycles and accessories. Extract is the first step in the ETL process, which involves retrieving and extracting raw data from different sources, such as databases, files, or other data storage systems. For example, imagine that AdventureWork's data is scattered across multiple systems, as is the case with many organizations. Say customer data is stored in a data management system called Customer Relationship Management, or CRM. Sales, marketing and manufacturing data is in an enterprise resource planning system, or ERP, and purchasing data is in spreadsheets. The extraction process involves pulling the data from these different sources. Then, you consolidate it into an easily accessible central location, often a temporary intermediate storage location known as the staging area, and prepare it for further processing in the next step. Once the data is extracted, the second step is to transform it. Transforming the data involves cleaning, structuring, and enriching the data to make it more suitable for analysis. This may involve removing duplicates, handling missing values, creating new calculated fields, converting data types, and standardizing measurement units. In the case of AdventureWorks, let's say that the sales and marketing data is in US dollars. But the manufacturing and purchasing data is in different currencies, depending on where in the world the sales or purchase take place. As part of transforming the data, you may need to convert all the currency values into a standard unit of measurement, in this case US dollars, to ensure consistency. The third and last step involves loading the transformed data into the final storage system, typically a data warehouse. Where it can be readily accessed and analyzed, for example, using tools like Power BI. Depending on the organization's needs, the loading process can be a one-time event or scheduled to run regularly. In the case of AdventureWorks, the cleaned and transformed data might be loaded into a cloud-based data warehouse, making it accessible to the company's data analysts and decision makers. The ETL process ensures that the data analyzed is accurate, clean, and consistent, which in turn supports informed decision making. This process offers many benefits, including data integration. ETL helps integrate data from different sources, providing a unified view of an organization's data, making it easier for analysts to perform analysis and derive insights. Data Quality. ETL processes involve data cleansing and validation, which significantly improve data quality. Data consistency. By transforming data into a standardized format, ETL ensures consistency across various datasets, enabling analysts to easily compare and analyze data from different sources. Enhanced performance. By aggregating, summarizing or indexing data during the transformation process, ETL can improve query performance and reduce the load on data analysis systems. And data governance. ETL can support data governance initiatives by helping organizations maintain a single source for their data, ensuring that everyone has access to the same accurate information. Widely used in data analytics, tools like Power BI, the ETL process helps you bring together, refine, and assemble different data pieces into a coherent picture that can drive business decisions. Power BI is just one tool that comes equipped with built in ETL capabilities, enabling you to connect to many different data sources, transform your data using Microsoft Power Query, and load it into the Power BI data model. Power Query is a powerful ETL tool within Power BI, providing a graphical interface and formula language, called M, to perform various data transformation tasks. With Power Query, you can extract data from multiple sources, clean and structure it, and load it into Power BI for creating reports and visualizations. The extract, transform, load, or ETL process is essential for any data-driven organization. The importance and benefits of ETL lie in its ability to turn raw data into accurate and consistent information in a centralized system that is easy to analyze and use in decision making. Because data is critical to better decision making, embracing tools that can support the ETL process, such as Power BI, can significantly impact business performance.