ETL and Advanced Modeling - inf unibz 2025

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ETL is process-driven, focusing on the movement and transformation of data from sources to a central system. Data Modeling is structure-driven, focusing on how to organize data in a way that it supports efficiency and integrity in database systems.
Extract, transform, and load (ETL) is the process of combining data from multiple sources into a large, central repository called a data warehouse. ETL uses a set of business rules to clean and organize raw data and prepare it for storage, data analytics, and machine learning (ML).
ETL means extraction transformstion and load. It is a process of extracting the data, transforming it (converting it into any other format if required) and then loading it into datasource like mysql, cassandra etc. On the other hand, business intelligence is the process of transforming data into visualizations.
ETLmeaning extract, transform, loadis a data integration process that combines, cleans and organizes data from multiple sources into a single, consistent data set for storage in a data warehouse, data lake or other target system.
ELT, which stands for Extract, Load, Transform, is another type of data integration process, similar to its counterpart ETL, Extract, Transform, Load. This process moves raw data from a source system to a destination resource, such as a data warehouse.

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The layers of ETL are: Extraction Layer. Transformation Layer. Load Layer.
Advanced ETL tools can load and convert structured and unstructured data into Hadoop. These tools read and write multiple files in parallel from and to Hadoop, simplifying how data is merged into a common transformation process.

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