ELTModelle

ELTModelle, also known as ELT (Extraktion, Laden, Transformations) models, refer to a data processing and integration approach used in data warehousing and analytics. Unlike traditional ETL (Extract, Transform, Load) processes, where data is transformed prior to loading into the target system, ELT models optimize the workflow by performing data transformations after loading the raw data into the destination database.

The main advantage of ELT models lies in their ability to leverage the processing power of modern database systems. Data is first extracted from various sources and loaded in its raw format into a data warehouse or data lake. Subsequently, transformations—such as data cleaning, filtering, or aggregation—are carried out within the target environment. This approach reduces the complexity of data movement and allows for greater flexibility, enabling analysts and data engineers to reconfigure or modify transformation processes directly within the database.

ELT models are particularly suitable for cloud-based and big data environments, where scalable processing resources are available. They support handling large volumes of structured and unstructured data efficiently and enable more dynamic data workflows.

However, ELT models require robust data governance and quality controls since raw data is stored without prior cleansing. Additionally, they depend heavily on the capabilities of the target system to perform complex transformations effectively.

Overall, ELT models contribute to flexible, scalable, and efficient data analytics architectures, aligning well with contemporary data science and business intelligence needs. They have become increasingly popular in modern data ecosystems, especially with the rise of cloud platforms such as Amazon Redshift, Google BigQuery, and Snowflake.

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