Data Integration Buyer's Guide

Data Integration is a Process, not a Product

Data Integration is a Process, not a Product

Data Integration is a Process, not a Product

Data Integration tools are perhaps the most vital components to take advantage of Big Data. Enterprise organizations increasingly view Data Integration solutions as must-haves for assistance with data delivery, data quality, Master Data Management, data governance, and Business Intelligence and Data Analytics. With data volumes on the rise and with no real end in sight, businesses are leaning on integration tools more and more to meet all of the data consumption requirements for vital business applications. The main function of Data Integration is to give organizations the ability to gain consistent access to their most important data, no matter where it lies, whether virtually or physically, whether on-premise, in the cloud, or in some other disparate location.

The migration, organization, and delivery of key organizational data assets is done in such a way that allow business teams to easily pull what they need for use within other business systems. The most comprehensive Data Integration tools available today will include data quality, data governance and MDM capabilities, providing security and Data Management functionality so that users may deliver only the most relevant data they need for analysis. Integration tools also give companies the ability to ensure data consistency across applications.

Accessing data doesn’t just mean having a unified view of it all, however. For practical purposes of crunching all the data, it needs to be in one place where an analytics program can reach it. That involves moving data from one place to another, usually from storage systems into a data warehouse capable of analyzing it. Methods for doing that include ETL (for Extract, Transform and Load) and replication, the latter of which, while often used for tasks like Disaster Recovery and migration in relation to Big Data, offers a high-performance data movement tool that should be able to quickly synchronize large quantities of data.

In recent years, open source Big Data frameworks such as Hadoop, Spark, and NoSQL have really emerged to give organizations more of a choice in where they store data and run applications on clusters of hardware, giving them additional storage options for all kinds of structured and unstructured data and enough processing power to handle virtually an infinite number of simultaneous tasks. In addition, Data Lakes have gained notoriety for their ability to continuously collect data and store it in a lightly structured repository. This is a positive development because it can help deliver data to stakeholders, business processes and applications with swiftness and ease.

Hadoop and friends have been able to thrive due to the demand that real-time and streaming data has put on organizations. No longer is it enough to store data in traditional relational databases, especially considering the pure volumes of data that now come from embedded sensors, computer applications, social media activity, and mobile devices. As more of these types of data sources become prevalent, open source storage frameworks should continue to grow in popularity, and with the Internet of Things expected to revolutionize virtually every consumer product with the addition of data capturing, this looks like a trend that will stick around for the long haul, making integration solutions that much more vital. The main consideration for buyers of integration software are features and functions of course, but Data Integration is not so much a product as it is a process, and there a variety of ways to move the needle.

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