1/5/2024 0 Comments Incremental load etl processes![]() ![]() Having no constraints lets decision-makers easily analyze data, apply machine learning, and gain valuable insights to make data-driven decisions. ![]() The data lake then stores this data in all manners, including structured, semi-structured, and unstructured data, which enables organizations to ingest data without constraints on schema or structure. This data comes from different sources, such as databases, IoT devices, SaaS data, and log files. Data ingestion differs from data integration in that it typically handles raw data without applying any changes to its original format.Ī data lake is a centralized storage repository that stores large amounts of raw data in its original format. ![]() Meanwhile, data ingestion refers to collecting, importing, and processing raw types of data from multiple data sources and transferring them into a storage system or repository for further data analysis. This process involves extraction, transformation, and loading (ETL) to ensure data consistency and usability across different applications and systems. As explained earlier, data integration focuses on combining and transforming data from various data sources into a consistent and unified format, enabling analysis and decision-making. While data integration and data ingestion both handle data from multiple sources, the two processes differ in their data management roles.
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