Several types of data integration exist, each with its own strengths and weaknesses. Choosing the most appropriate data integration method depends on factors such as the organization's data needs, technology landscape, performance requirements and budget constraints.
Extract, load, transform (ELT) involves extracting data from its source, loading it into a database or data warehouse and then later transforming it into a format that suits business needs. This might involve cleaning, aggregating or summarizing the data. ELT data pipelines are commonly used in big data projects and real-time processing where speed and scalability are critical.
The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.
With extract, transform, load (ETL), the data is transformed before loading it into the data storage system. This means that the transformation happens outside the data storage system, typically in a separate staging area.
In terms of performance, ELT often has the upper hand as it leverages the power of modern data storage systems. On the other hand, ETL data pipelines can be a better choice in scenarios where data quality and consistency are paramount, as the transformation process can include rigorous data cleaning and validation steps.
Real-time data integration involves capturing and processing data as it becomes available in source systems, and then immediately integrating it into the target system. This streaming data method is typically used in scenarios where up-to-the-minute insights are required, such as real-time analytics, fraud detection and monitoring.
One form of real-time data integration, change data capture (CDC), applies updates made to the data in source systems to data warehouses and other repositories. These changes can then be applied to another data repository or made available in a format consumable by ETL, for example, or other types of data integration tools.
Application integration (API) involves integrating data between different software applications to ensure seamless data flow and interoperability. This data integration method is commonly used in scenarios where different apps need to share data and work together, such as ensuring that your HR system has the same data as your finance system.
Data virtualization involves creating a virtual layer that provides a unified view of data from different sources, regardless of where the data physically resides. It enables users to access and query integrated data on demand without the need for physical data movement. It is useful for scenarios where agility and real-time access to integrated data are crucial.
With federated data integration, data remains in its original source systems, and queries are executed across these disparate systems in real-time to retrieve the required information. It is best suited for scenarios where data doesn't need to be physically moved and can be virtually integrated for analysis. Although federated integration reduces data duplication, it may suffer from performance challenges.
