Streaming Legacy Data to Kafka – Real Industry Stories

Read this eBook to learn how three organizations used Precisely Connect solutions to fully integrate their legacy systems into their cloud platforms and analytics engines by streaming to Kafka, gaining real-time access to legacy data while eliminating the costs and delays of manual ETL processes.

Introduction

IT executives are constantly challenged by the need to accelerate the adoption of the most modern and powerful technologies while still having to rely upon their existing installed base of storage and compute infrastructure. The only practical path forward is to enable backwards compatibility. The new must connect and interoperate with the old. Leading-edge must leverage legacy.

In truth, given the rate of acceleration in technological advances, even the definition of “legacy” is becoming blurred. For example, would an organization transitioning from data warehouses to an enterprise data hub really consider their data warehouse infrastructure to be “legacy?” Cloud-based, distributed processing frameworks and database platforms are enabling deployment of much faster and more powerful real-time processing and analytics systems. Yet the vast majority of the data they process is still being generated by a pre-existing installed base of older servers and applications.

For many of the largest organizations, that installed base includes major installations of IBM/z mainframe servers and storage, which handle their core, high-volume processing. The costs and disruption that would result from any effort to replace these systems outright are simply incalculable. But the challenges of integrating them with modern, cloud-based distributed processing platforms are daunting.

On the following pages, you will learn how three organizations used the Precisely data integration solution Connect to fully integrate their legacy systems into their cloud platforms and analytics engines by streaming to Kafka, gaining real-time access to legacy data while eliminating the costs and delays of manual ETL processes.