Data Integration

The correct data at the right time, place, and format is very much important for a smoother data operation, since inconsistency, inefficiency, and inaccuracy in the entire process can cause due to dispersed data.

Big data integration software is used by many organizations to manage and store big data clusters and use them within cloud applications.

Analysts can have a comprehensive view of key performance indicators and other process-related information with data integration. Better quality data for decision making, easy access to data, fewer data silos, better efficiency for users, data-driven business operations and cost reduction are benefits of data integration.

The flow remains the same irrespective of the type of data integration. Gather requirements, profile data sources, review requirements, design, implement and verify, validate, and monitor are six common steps followed in a data integration process.

Data integration techniques are used to automate data integration processes and consolidate data from multiple sources. Extract, transform, and load (ETL); Extract, load, and transform; Change data capture; Data replication; Data virtualization and Streaming data integration are different data integration techniques.