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Data integration is the process of data combining from multiple sources to provide complete, accurate, and up-to-date information for business intelligence, data analysis, and other business processes. The data integration process involves replicating, ingesting, and transforming data to combine different data types into standardized formats. Such data is stored in the target repository: data warehouse, data lake.

There are 5 approaches to data integration: ETL, ELT, streaming, application integration (API), data virtualization. Implementation of these processes can occur through manual architecture coding using SQL, or customization and management of data integration tools. The second method greatly simplifies development and automates the system.

1. ETL is a traditional data pipeline that transforms disparate data. The transformation process takes place in 3 stages: extraction, transformation and loading. The data is converted in the staging area before being uploaded to the target repository. This facilitates fast and accurate data analysis and is suitable for small datasets;
2. ELT is a more modern pipeline where data is loaded immediately and transformed in the target system (cloud data lake, data warehouse). This approach is appropriate for large datasets where timeliness is important;
3. Data streaming – this approach allows to continuously move data from the source to the target in real time. Modern integration platforms provide the ability to deliver analytical data to streaming and cloud platforms, data warehouses and data lakes;
4. Application Integration (API) – provides the ability for separate applications to work together, move and synchronize data between them. A common use case is to support operational needs (for example, providing the same information to HR and finance departments). Application integration should ensure consistency across datasets. SaaS application automation tools help to create and maintain own API integrations;
5. Data virtualization – delivers data in real time at the request of a user or application. It is possible to create a single data view, make it available on demand through the virtual aggregation of data from different systems. Virtualization is suitable for transactional systems that are built for high-performance queries.

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