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Author: Admin | 2025-04-28
Data from source systems, transforming it to match the target system’s requirements, and loading it into a data warehouse or data mart. It’s suitable for batch processing and large data volumes.ELT differs from ETL by loading raw data into a data lake first and then transforming it later. This approach is often used for big data scenarios where schema definition is flexible.Data FederationData federation creates a virtual view of data from multiple sources without physically moving it. It provides a unified access layer, allowing users to query data as if stored in a single location.Data VirtualisationLike data federation, data virtualisation presents a unified view of data but relies on metadata to describe data sources and relationships. It offers real-time access to data without creating a physical copy.Change Data Capture (CDC)CDC tracks data changes in source systems and replicates only the modified data to the target system. This approach is efficient for incremental updates and real-time data processing.Enterprise Application Integration (EAI)EAI focuses on integrating applications within an organisation. It involves connecting different systems and enabling data exchange between them.The Process of Data IntegrationData Integration is a multi-step process that involves transforming raw data from various sources into a consistent and usable format. This process helps businesses and organisations make better decisions based on accurate and complete data. It involves three key steps: data extraction, data transformation, and data loading.In this step, data is collected from various sources, such as databases, spreadsheets, web applications, or cloud storage. Businesses often store data in different formats and locations, making it difficult to use all at once. Data extraction pulls this information together, ensuring it is ready for the next stage.Data TransformationOnce extracted, the data goes through a transformation process to make it clean and uniform. This step removes errors, fills in missing values, and ensures that all information follows the same structure. For example, if one system records dates as “DD/MM/YYYY” while another uses “MM-DD-YYYY,” transformation makes them consistent. This process ensures that the data is accurate and ready for analysis.Data LoadingIt is the final step where transformed data is loaded into a target system, such as a data warehouse or a data lake. It ensures that the integrated data is available for analysis and reporting. Data Integration Techniques in Data MiningFinally, the transformed data is stored in a central location, such as a data warehouse or a data lake. Businesses and analysts can
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