Integration of data is a new and timely step that ensures efficiency in the business world. A survey shows that more than 67% of businesses are using data integration, and the number is increasing day by day. Before implementing the data integration process, organizations need to know data integration type, and techniques. Keep reading to learn the basics of data integration.
Data Integration – What is it?
Data integration combines disparate data sources into a single entity and disseminates business intelligence. The origin and use of data are everywhere. Users need to integrate the sources into a single entity to make the data useful. The process of data integration was first used in the early 1980s. The process, tools, and technologies have changed and improved a lot. Cloud technology has made the data integration, collection, and storage process more manageable and smoother.
Common Types of Data Integration
Data integration has multiple forms and types. Here are five common types of data integration:
Manual Data Integration
In the manual data integration process, users collect data from multiple sources. Then they find the relevant information and use them. There is no unified view of data, so this is a more complex process.
Middleware Data Integration
Like the name, this process takes place in the middle of the infrastructure and application layer. Users can transfer data integration logic from the application to the middleware component to use the data.
Uniform Data Access
The goal of uniform data access integration (UDAI) is to generate a holistic data view in a sustainable way. Here the original data do not move from its place. Users can find data in the source system.
Common Data Storage
Standard storage data integration is also known as physical data integration configuration. Here, users can copy the source of data. Members can use and store this data in a new system.
Application-Based Data Integration
Application-based data integration is more effective as it supports automation. It is capable of locating, formatting, and integrating data quickly.
Techniques of Data Integration
Streaming Data: Focuses on achieving continuous integration and feedback of streaming data.
Virtual Storage: Collects data from a different system and gathers it in a virtual storage center.
Replication: Users replicate the data from one database to the other one for synchronization.
Track Data Changes: In this technique, users can track database changes to keep everything in sync.
Extract, Transform, Load: ETL is a process of copying data from heterogeneous sources and moving them into a database.
Extract, Load, transform: ELT is the process of extracting, loading, and transforming data for analytical use.
Business success depends on the data provided by different departments and processes. The advanced data integration process finds valuable insight from the data and allows organizations and users to use the best data.