Describe the techniques used for data integration in big data engineering.

Data integration in big data engineering refers to the process of combining data from multiple sources, formats, or systems to create a unified and coherent view of the data. It involves extracting data from various sources, transforming it into a consistent format, and loading it into a target system for analysis and processing. Data integration is crucial in big data environments as they often deal with diverse data sources, including structured, semi-structured, and unstructured data. Let's explore some of the techniques used for data integration in big data engineering: 1. Extract, Transform, Load (ETL): ETL is a commonly used technique for data integration. It involves three main steps: extracting data from source systems, transforming it into a consistent format, and loading it into a target system. In the extraction phase, data is fetched from various sources, such as databases, files, APIs, or streaming platforms. The extracted data is then transformed by applying cleansing, enrichment, aggregation, and other operations to ensure consistency, quality, and compatibility. Finally, the transformed data is loaded into a target system, such as a data warehouse or a big data platform, for further analysis and processing. 2. Data Wrangling: Data wrangling, also known as data munging, is the process o....