Data Integration Buyer's Guide

The Four Most Commonly Used Data Integration Delivery Types

The Four Most Commonly Used Data Integration Delivery Types

The Four Most Commonly Used Data Integration Delivery Types

Data integration is a combination of technical and business processes used to combine different data from disparate sources in order to answer important questions. This process generally supports the analytic processing of data by aligning, combining, and presenting each data store to an end-user.

Organizations increasingly view data integration tools for enterprise-wide data delivery, data quality, governance, and analytics. Data integration allows organizations to better understand and retain their customers, support collaboration between departments, reduce project timelines with automated development, and maintain security and compliance.

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If you’re just beginning your search for a new data integration tool, its important to arm yourself with the knowledge about the different delivery methods traditional software products offer. 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.

Data Virtualization

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.

Message-Oriented Movement

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.

Data Replication

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.

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