Data integration is closely related to the concepts of data pipeline, data ingestion, and data wrangling. A data pipeline refers to the sequence of processes and tools used to collect, process, and move data from one system to another. Data ingestion is the first step in this pipeline, involving the collection and import of data from various sources into a central repository. Data wrangling, on the other hand, involves cleaning and transforming raw data into a more usable format, which is a crucial part of the data integration process.

To achieve effective data integration, several practices such as data cleaning, data transformation, and data standardization are essential. Data cleaning aims to correct or remove errors and inconsistencies in the data. Data transformation involves converting data into a common format or structure, facilitating its integration. Data standardization ensures that data from different sources adheres to a uniform set of definitions and formats, making it easier to combine and analyze.

Advanced techniques such as data federation, data harmonization, and metadata management further enhance the data integration process. Data federation involves creating a virtual database that provides a unified view of data from multiple sources without physically moving the data. Data harmonization aims to reconcile differences in data from various sources to create a coherent dataset. Metadata management involves managing the data about the data, ensuring that data is easily discoverable and understandable. These techniques, along with data synchronization and data aggregation, ensure that data integration is both comprehensive and efficient.