Data Integration involves combining data from different sources into a unified view, making it accessible and usable across an organization. It involves collecting, transforming, and loading data from various sources such as databases, files, applications, and external systems to create a single source of truth.

A few key components of Data Integration are listed below:

Data Sources Identification: Identify the various sources of data within your organization, which may include databases, flat files, APIs, cloud-based services, and external systems.

Data Extraction: Extract data from the identified sources using appropriate methods and techniques. E.g. – querying databases, reading files, consuming web services, or accessing APIs.

Data Cleansing:  This involves identifying and correcting errors, removing duplicates, standardizing formats, and resolving inconsistencies.

Data Transformation: Transform the cleansed data into a standardized format that is suitable for analysis and consumption.

Data Loading: This step involves inserting, updating, or merging the data into the target system while maintaining data integrity and consistency.

Data Synchronization: Implement mechanisms to keep the integrated data up-to-date and synchronized with changes occurring in the source systems. This may involve real-time or batch-based synchronization depending on the requirements of the organization.

Data Quality Assurance: Data quality is essential for ensuring that the integrated data is reliable and trustworthy for decision-making. Ensure the quality and reliability of the integrated data through data profiling, cleansing, deduplication, and validation.