What Is a Data Integration Service?
A data integration service is programming that can connect to a source system, extract data, transform it and incorporate it in a target system along with data from other source systems. The target system can then be used as a single source of truth for other applications and computing systems.
Common integration solutions include:
- Extract, Transform, Load (ETL): ETL moves data from one or more sources to another destination. This could be a database, data warehouse, data store or data lake. ETL is most appropriate for processing smaller, relational data sets that require complex transformations.
- Extract, Load, Transform (ELT): ELT extracts, loads and transforms data from one or more sources into a repository such as a data warehouse or data lake. ELT can handle any size or type of data and is well suited for processing both structured and unstructured data.
- Data Replication: Changes to the source system are replicated on the target system in real time.
- Publish-Subscribe: Downstream systems subscribe to a data integration service that will update the target system. With a hub and a spoke architecture there can be many subscribers for single data topic/publication, streamlining the whole process.
- Application Programming Interfaces (API) and Web Services: Used to build an architecture that can accommodate many request/response-based data services simultaneously.
How Does a Data Integration Service Work?
Data integration services used to be complicated to build. This required help from data engineers who knew how to code. Today, cloud-based data integration services are designed to be managed through a low-code/no-code dashboard. They also use APIs to extract and transfer data.
The graphical user interface (GUI) dashboard allows users to drag and drop icons. These icons represent tasks, transformations, dataflow templates, pipelines, schemas or workflow designs. Once assembled, the service can be tested before moving to the production environment.
To keep data integration services simple, we recommend avoiding:
- Designs that involve creating and managing too many unstandardized moving parts
- Code-heavy approaches that fail to take advantage of automation
- Legacy programs that do not support native or hybrid cloud architectures
- Integration service engines that are designed to accommodate a specific, narrow use case
Get Started with Data Integration Tools
Platform solutions can help you overcome many common challenges when trying to achieve successful integrations. The Informatica Intelligent Data Management Cloud™ (IDMC) is our open, low-code/no-code solution. IDMC has several cloud data integration services that provide the critical capabilities key to centralizing data in your cloud data warehouse and cloud data lake:
- Rapid data ingestion and integration with an intuitive visual development environment with Informatica Cloud Mass Ingestion
- The ability to build data pipelines and applications faster without managing servers with serverless integration
- Pre-built cloud-native connectivity to virtually any type of enterprise data, whether multi-cloud or on-premises with Informatica connectors
- Intelligent data discovery, automated parsing of complex files and AI-powered transformation recommendations
- The ability to identify, fix and track data quality problems with Informatica Data Quality
- Pushdown optimization for efficient data processing
- Serverless Spark-based processing for scalability and capacity on demand with Informatica Cloud Data Integration-Elastic