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**Data ingestion** involves collecting data from various sources, such as databases, APIs, and files. This step is about getting the data into your **data pipeline**. Next is **data transformation**, which is where you clean, transform, and enrich the data. This can include tasks like handling missing values, scaling features, and encoding categorical variables. This step is about preparing the data for your AI models. Finally, we have **data loading**, which is loading the processed data into a data warehouse or data lake. This step is about making the data accessible to your AI models. The goal is to provide the best possible **data analysis** for your AI model and make it secure and with high **data quality** through the use of **data governance**.
