Buyer’s Guide and Checklist for Data Integration

Read this eBook to discover 10 key features to help you choose a vendor that offers both software and an approach that can grow and change with your organization, with recommendations on:

• Designing once with a deploy anywhere approach
• Resiliency and backup
• Future proofing investments

Snapshot: The data integration landscape today

Gone are the days of sole batch ETL (extract, transform, and load) with the help of a few skilled developers fulfilling data integration requirements. A new dynamic and fluid model of data integration has taken its place – bringing data from across a business to users when and how they need it. Much of the changed approach is driven by a broader diversity of cloud data consumption models, as well as a surge in the number and types of applications demanding real-time data delivery. Not to mention the all-consuming interest in leveraging generative AI and AI models from organizations across industries.

Cloud delivery models and applications have become part of every organization’s strategic business strategy, allowing them to expand capabilities, reduce costs, and drive digital transformation. However, bringing the right data from existing infrastructure to the cloud for business consumption can be an impossible task for many organizations. Cloud, and the benefits that it promises, requires a new way of thinking about data integration.

As a data integration leader in your organization, you can leverage moving to cloud as an opportunity to modernize existing integration approaches while maximizing your investment in your chosen infrastructure. To modernize, you cannot merely “copy” and “paste” existing data integration pipelines – traditional data pipelines are not adaptive enough. Instead, looking for tools that can build links between existing infrastructure and new cloud investments in an environment-agnostic, future-proof way is imperative. You need only look at what’s transpired over the past ten years to understand why.