Data integration involves combining data from multiple disparate sources, which are stored using various technologies, and upon completion of the project, provides a unified view of the data. Simply put, data integration is the essential link between information and insight. Businesses that ensure their various databases can “talk” to one another are able to take advantage of the details they’re collecting.
Data integration into a centralized data warehouse becomes more important as application environments trend towards microservice architectures. As applications grow in size and complexity over time the ability to react to business changes and add new features becomes extremely difficult to manage without affecting other parts of the system. As a result, IT development teams are breaking these monolithic applications up into smaller easy to manage services that can scale more effectively. This is what gives them name microservices.
Other IT organizations may have their applications split across business lines in a Federated Architecture. The result is systems that are distributed and heterogeneous. They also may contain overlapping datasets for entities like customers and products. It becomes the job of the data integration team to bring these disparate sources together and align them across business processes. Conflicts between systems can result in poor data quality and require data stewards to define which system is the master dataset, and business rules for managing and processing that data.
Traditionally, data warehouse architectures employed an Extract, Transform, Load (ETL) architecture. This means you would get the data (extract), you would transform the data in flight (transform), and then you would load it into a system. Newer architectures are choosing an ELT approach, where after you get the data you load it as is (EL), and then transform it on the database you load it to, or transform it via front-end analytics tools/visualization tools. The benefits you get is greater speed of extraction and loading, and you capture all of the data, whether or not you need it today.
Every organization that produces reports or dashboards often has some flavor of data integration platform working behind the scenes to pull data from source databases, transform data, and load data into a dedicated data repository for BI and analytics. Click HERE to discover how Think Data Insights can mentor and guide you on your reporting and dashboards.