There are many different methods of data integration. Below, we will discuss approaches and best practices for integrating data. Keep reading to learn more!
Data Integration Approaches
Data integration is the process of combining data from different sources into a single, unified view. This can be a challenge because the data may be stored in different formats, use different naming conventions, and reside in different databases or file systems.
There are several effective approaches to data integration:
Extract, Transform, Load (ETL): The ETL process begins by extracting the data from its source system, then transforming it into a common format, and finally loading it into the target system. This approach is often used when there is a need to consolidate data from multiple sources into a single database.
Master Data Management (MDM): MDM involves creating a centralized repository of master data — such as customer contact information or product descriptions — and then ensuring that all systems that need access to this data are updated whenever changes are made. This approach can help ensure the accuracy and consistency of data across multiple applications and platforms.
Data Federation: With data federation, individual systems remain responsible for their data but can still access information from other systems as needed. This approach is often used when there are multiple sources of information that need to be combined but where it is not necessary or practical to move the data into a single repository.
Data Synchronization: This method involves copying data from one source to another so that both sources have the same data. This can be done manually or using a tool.
Application Programming Interface (API) Integration: API integration involves connecting two systems using an API to exchange information between them automatically. This is often used for integrating cloud-based applications with on-premise applications or for integrating different parts of an organization’s IT infrastructure
Master-Slave Replication: Master-slave replication copies all or part of a database from one server to another server. The slave server is a replica of the master server and can be used for backup or disaster recovery purposes.
Mirroring: Mirroring creates a real-time copy of a database that can be used for failover or load-balancing purposes. If the primary database fails, the mirror will take over and serve requests from clients.
ETL, Replicate, and Pushdown
To understand best practices for data integration, it is important to understand ETL, replicate, and pushdown.
As discussed, extract, transform, load (ETL) is the most common method of data integration. It involves extracting data from one or more sources, transforming it into a format that is suitable for the target system, and loading it into the target system. This method is typically used when there is a need to move large amounts of data or when the source systems are not able to provide the needed information promptly.
Replicate is a simpler method of data integration than ETL. It involves replicating all or part of the data from one source system to another destination system. This method can be used when there is a need to keep multiple copies of the same data or when there is a need to quickly populate a new system with existing data.
Pushdown is an advanced method of data integration that can be used when there are complex dependencies between source and target systems. In this method, the logic for integrating the data is pushed down into the target systems so that they can handle all of their processing requirements. This can improve performance and reduce the load on both the source and target systems.
Overall, data integration methods are important because they allow businesses to combine data from different sources into a single, cohesive view. This can help businesses make better decisions, identify trends, and understand their customers better.
