FAQ
Data integration is the process of combining data from different sources into one unified view. In simple terms, it takes all your separate data (from various databases, applications, files, etc.) and brings it together so you can access it as if it were in one place. This unified data is consistent and up-to-date, making it easier to analyze and use for decision-making.
Data integration is important because it eliminates data silos and ensures everyone in a business is working with the same information. Without integration, different departments might have conflicting or incomplete data. Integrated data leads to better decisions, as you have a complete picture of your operations and customers. It also increases efficiency – teams spend less time searching for or reconciling data, and more time utilizing it for strategic purposes.
Common data integration methods include ETL (Extract, Transform, Load), where data is extracted from sources, transformed to a standard format, and loaded into a central repository. There’s also ELT, a variation where data is loaded first and transformed in the target system (often used in cloud data lakes). Other methods are real-time data streaming, application integration via APIs (connecting applications directly), and data virtualization (creating a virtual unified view of data on the fly without physically moving it). Often, modern data integration platforms will support multiple methods to fit different needs.
Semantic data integration uses a knowledge graph or ontology to map data, focusing on the meaning of data rather than just its format. In traditional integration, you might write custom scripts or use ETL tools that require a lot of manual schema mapping and adjustments for each source. In semantic integration, you define a common data model (ontology) and map sources to that model. This approach is more flexible when dealing with many heterogeneous sources and can automatically reconcile differences in terminology or structure. It also preserves context and relationships in the data, which is useful for advanced analytics and AI. The result is a more intelligent integration process that can adapt over time with less effort.
Yes. Modern data integration solutions (like ours) are designed to handle real-time data. This is often achieved through streaming integration or change data capture. For example, as soon as a new transaction happens in a source system, a real-time integration pipeline can immediately send that data to the target system (like a live dashboard or a digital twin). Real-time integration ensures that your unified data view is always current, which is crucial for scenarios like live analytics, monitoring systems, or any application where up-to-the-second information is needed.
