There are many ways to integrate data from a targeted source. Data integration tools have long been the workhorses behind data movement to and from databases, applications, and the cloud. The type of integration tool you’ll need depends largely upon where the data sits at any given moment and where you’d like it to wind up. An in-between stage also takes precedent, as data manipulation — cleansing, wrangling, blending, transforming — are all processes to be done under the hood, depending on your specific use case.
We’ve outlined four data delivery styles in which integration software tends to perform, but it’s worthwhile to dig a bit deeper and really iron out the keys that make these capabilities tick. Data replication is one of these vital integration processes. In traditional settings, extract, transform and load (ETL) solutions were utilized to bring data together in one place. Use cases are increasingly being shifted to incorporate more data and these tools aren’t able to keep pace. It is much easier to replicate an entire database as it’s being filled with new information than it is to run a continual stream of data from one place to another while manipulating it on the fly.
Replicating data is a pseudo-integration technique that enables all the users in an organization to gain access to the same data. This is done by software that generates copies of a database in one server and transmits it to another. What replication aims to do is effectively turn all of an organization’s databases into one so that a unified view of the data can be viewed in different locations. The end result is removal of incomplete or non-quality data.
There are three main types of replication:
- Transactional: Automates changes made between databases in near real-time from the primary server to the subscriber. This process is used in environments where frequent changes are made and need to be recorded individually.
- Snapshot: Only copies data that has been changed since the database was previously replicated. Utilized largely in scenarios where data doesn’t change often or when only notable changes occur in a short window.
- Merge: Allows both the publisher and subscriber to make changes to the database. Merge agents sit on both servers to update changes and resolve conflicts in data.
Where does Change Data Capture fit in?
Change data capture (CDC) functionality brings replicating databases to a new level in that it updates target servers with only the data that has changed in the primary source. In this way, they quite literally capture the changes in data and nothing more. These fill a very specific need for organizations that commonly replicate large data stores and want to avoid sifting through all of their data to find out what’s changed.
Users utilize CDC patterns to identify only data that has been changed so that it can be used for analysis. Instead of being forced to replicate entire databases over and over, CDC tools ensure that only changed data is updated, greatly enhancing database performance and ensuring that bottlenecks are avoided.
We hope this quick guide is helpful in your search for the best data integration software. For more, consult our vendor comparison map, a graphical resource that plots the top providers in the marketplace.
Latest posts by Timothy King (see all)
- Workato Nabs $70M Series C Funding, Updates Automated Integration Platform - November 13, 2019
- Top 15 Best Data Integration Books You Should Read - November 11, 2019
- Gartner Names Winners of 2019 Voice of the Customer for Enterprise Integration Platform as a Service - November 8, 2019