Data Integration is a combination of technical and business processes used to combine different data from disparate sources for analysis. This process generally supports the analytical processing of data by aligning, combining, and presenting each data store to an end-user, and is usually executed in a data warehouse with integration software.
If you’re just beginning your search for a new Data Integration tool, knowing the different feature offerings each solution offers is key. Between traditional ETL (extract, transform, load), virtualization, Integration Platform as a Service (iPaaS), Big Data and self-service, it can be difficult to distinguish between what a particular platform does as its main focus. Sifting through the various styles of integration tools can be confusing, so we put together this list of the four major data delivery techniques common in integration tools:
Acts as a support mechanism for extract, transform and load (ETL) processes to consolidate data from primary databases. This involves bulk and or batch data extraction that draws data from across system and organizational data stores. This is an efficient way of processing large data volumes over a period of time. Data is collected and processed, then batch results are created.
Allows users to create a virtual abstract layer than can be mirrored to provide one single view of all the data that resides in the database instead of having to run through the process of ETL to get the data loaded up into an analytic framework. This enables the user to piece together databases, data warehouses, and even cloud services to gain a comprehensive view of the data that matters most.
Groups data into messages that applications can read so data can be exchanged in real-time. This depends on a message bus which becomes triggered by events and delivers data packets to application integration technologies. Oftentimes middleware is involved, acting as a software or hardware infrastructure that supports sending and receiving messages between distributed systems.
Frequent copying of data from a database to another that allows all users to share the same level of information, resulting in a distributed database that enables user access to data relevant to their own tasks. This provides data synchronization that enables users to manage growing data volumes while gaining access to real-time information.
Latest posts by Timothy King (see all)
- 5 Things You Need to Know About GDPR Right Now - November 20, 2017
- Datameer Announces AWS-based Data Preparation Platform - November 14, 2017
- Jitterbit Nabs $25 Million Funding Round for API Transformation - November 9, 2017