A data lake is a storage repository that can handle large volumes of raw

[nusaiba130]

In addition to traditional ETL processes, data transformation is increasingly being integrated into modern data processing architectures, such as data lakes and data pipelines. , unstructured, or semi-structured data. In a data lake, data transformation often occurs as part of the data pipeline, which is an automated workflow that moves data from one system to another while applying transformations along the way.


Data pipelines can be designed using a variety of tools, such as lithuania mobile phone numbers database Apache Kafka, Apache Airflow, or cloud-native solutions like AWS Glue. These tools allow organizations to create scalable, real-time data transformation workflows that can handle data from multiple sources and process it quickly and efficiently. The use of data pipelines is particularly important in environments where data is constantly being generated, such as in IoT (Internet of Things) applications or real-time analytics systems.


In the context of machine learning, data transformation is an essential step in preparing data for model training. Raw data often needs to be transformed into a format that can be fed into machine learning algorithms. This might involve tasks such as feature engineering, where new features are created from the raw data, or normalization, where data is scaled to ensure that all features contribute equally to the model.