Data Integration involves merging data from discrete sources, providing a unified view of the data. It simplifies and transforms data to make it useful for decision-making, predictive analytics, and machine learning tasks.
Imagine you are trying to make a picture puzzle and pieces are stored in different boxes. To make sense of the puzzle, you need to bring all those pieces together in one box. Now you can see all the pieces and try to make sense of the whole picture. This is what we do in Data Integration, we combine data from different sources so we can fully understand it.
Data integration is a necessary prerequisite for most artificial intelligence (AI) and machine learning (ML) tasks, which can rarely be accomplished with data from a single source. Rather, multiple datasets often need to be combined to provide the necessary context or improve the quality and reliability of models.
Real world data is often distributed among several databases or files, often of different types. Data integration involves the process of consolidating this data into a unified form. It often involves the process of Extract, Transform, Load (ETL) wherein data is extracted from original databases or files, transformed into a format suitable for the analytic task at hand, and then loaded into a target data store, be it a database or a data warehouse.
Data integration is commonly used in data warehousing, where it’s essential to combine different sources. It’s also used in data migration, where data is moved from one location to another and it’s necessary to ensure that the migrated data is of high-quality and consistent.
Beyond mere merging, data integration often involves a cleaning step where inconsistencies are solved, missing values may be filled, and noisy data is dealt with. More advanced integration might involve aligning records from different databases that refer to the same entity, a process known as data matching or record linkage.
Data integration can become complex because each new data source may introduce inconsistencies or discrepancies, requiring sophisticated integration strategies to resolve them in a way that respects the validity and accuracy of the final dataset.
Methods for data integration must handle issues like data duplication, misalignment, and the preservation of critical metadata. They also often need to ensure the integrated data is stored in a format suitable for machine learning models, which commonly require numeric matrices.
Data Cleaning, Data Wrangling, Data Preparation, Data Warehousing, ETL (Extract, Transform, Load), Data Migration, Metadata, Data Duplication, Record Linkage.