Data integration aims to bring together data from various sources, such as relational databases, NoSQL databases, spreadsheets, and log files, into a consistent and unified format.

Data warehousing is a widely used data integration technique that involves storing data from multiple sources in a central repository, known as a data warehouse, for analysis and reporting.

Data extraction involves selecting and retrieving specific data from a larger dataset based on predefined criteria or conditions.

ETL (Extract, Transform, Load) tools are specifically designed to extract data from various sources, transform it into a consistent format, and load it into a target system or data warehouse.

Data transformation involves converting data from one format to another, such as from a raw format to a structured format, or from one data type to another, to ensure compatibility and consistency across different data sources.

Data inconsistency, where data from different sources may have conflicting or contradictory values, is a common challenge in data integration, as it can lead to inaccurate or misleading results.

Data cleansing aims to identify and correct errors, inconsistencies, and missing values in data to ensure its accuracy and reliability for analysis and decision-making.

Data warehouse architecture is a widely adopted data integration architecture where data from various sources is extracted, transformed, and loaded into a central repository, known as a data warehouse, for analysis and reporting.

Data standardization involves defining and enforcing consistent data formats, data types, and data values across different data sources to ensure data integrity and comparability.

Data integration enables the combination of data from disparate sources, allowing for the identification and correction of inconsistencies, resulting in improved data accuracy and consistency.

Metadata in data integration provides information about the structure, content, and characteristics of data, enabling better understanding, management, and utilization of data.

Data volume can be a significant challenge in data extraction, especially when dealing with large datasets, as it can impact the efficiency and performance of the extraction process.

Data profiling in data integration aims to analyze and understand the structure, content, and characteristics of data, including data types, data distribution, and data quality, to facilitate better data management and decision-making.

Informatica PowerCenter is a widely used commercial data integration tool that provides a comprehensive set of features for data extraction, transformation, and loading (ETL), data quality management, and data governance.

Data governance in data integration involves establishing and enforcing data standards, policies, and procedures to ensure data quality, consistency, and compliance with regulatory and organizational requirements.