Data integration

Many organizations are facing an avalanche of data originating from different systems, such as relational databases or streaming data services. Business intelligence  required for better decision-making is hidden within all of that data, but solid data integration processes must be followed to make sure that the data is managed and governed, and ultimately trusted. Your integration efforts may be hindered by:

Data latency in multicloud environments
Moving data volumes across multicloud and data lake environments can be slow and prevent you from using that data in real time within your applications or operational systems.

The complexity and cost of multiple tools
Managing multiple data integration tools is time consuming for your resources and can be expensive for your business.

Manual processes and workflows
Manual tasks such as hand coding and job design can delay application building and updates. Manual processes also must be designed for each cloud environment, so if you are working with multiple clouds, this increases development time and costs.

Lack of data quality and governance
Data originating from so many different sources can be difficult to govern and can put your business at risk. Trusted and clean data is also required for effective AI models.

Data repositories include on-premises, cloud and data lake environments. Often, organizations are also using clouds from different vendors to meet specific needs for storage or application deployment. The practice of integrating data across all of these environments for a unified view is cloud data integration.

The complexity of cloud data integration requires a modernized approach. A robust multicloud data integration solution should:

• Simplify and accelerate synchronization of diverse data sources across hybrid multicloud environments
• Locate run times closer to data sources
• Use embedded analytics and AI services on different cloud platforms
• Automate job design and feature pre-built connectors for faster access to data sources
• Include in-line data quality to manage governance and compliance

IBM DataStage for IBM Cloud Pak for Data can deliver this modernized approach.

Data integration and application integration may seem similar, but in fact, the concepts are very different. As described, data integration is the practice of locating and retrieving information from disparate data sources and delivering it in a unified structure and view. Application integration directly links multiple independent applications so that they can work with each other, often through modern APIs or traditional service-oriented architectures. Data and workflows are merged and optimized, helping bridge the gap between on-premises systems and cloud-based applications.

Data migration is simply the process of transferring data between storage types. This can include moving data from on-premises environments to the cloud. Data integration, however, is more complex as the data goes through the ETL or ELT process to be made ready for analysis.