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Enabling data in motion within an organization involves much more than simply setting up a Confluent cluster, piping in a few data sources, and writing a set of streaming applications. You need to consider how to ensure reliability of your applications, how to enforce data security, how to integrate delivery pipelines, and how your organization will develop and evolve governance policies. It’s difficult to know where to start.
Whether you’re a developer or architect, a platform owner or an executive, you must consider different efforts when moving workloads to production depending on whether those workloads are simple or mission critical—and how these roll up into a sound enterprise-wide data strategy. While there is no one-size-fits-all solution for data streaming systems across industries, there are logical, broad sets of knowledge and actions required to be successful. For example, understanding event-driven designs and patterns, shifting to a strategy on how to deploy and operate clusters, and ensuring security, operational, and governance processes along the way.
When you are just starting on this journey, it is not always obvious what concerns need to be addressed and in what order. To help organize the knowledge, actions required, and resources available to you, we’re proud to announce the launch of “Setting Data in Motion: The Definitive Guide to Adopting Confluent.”
This guide was written by our professional services team—architects, engineers, and managers who have worked in the field, developing data streaming solutions with hundreds of our customers. We have worked with companies including the largest global enterprises and the most innovative digital natives at various stages of maturity with Confluent and Apache Kafka®; some early in their journey focused on initial interest and proof of concepts, to more mature organizations with event streaming at the heart of their business.
We have distilled the knowledge from this experience into a set of topics, or recipes, grouped into major themes:
Each topic addresses questions that we have repeatedly faced during implementation programs:
This guide is designed to:
The guide is not intended to be read end to end, but rather as a reference for you to dive into throughout your journey. This makes it a valuable resource, regardless of whether you are just starting out, or if you are consolidating multiple clusters as part of a data mesh strategy.
We hope this book helps you to successfully implement and grow event streaming within your business at each stage of platform adoption.
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At Treehouse Software, when we speak with customers who are planning to modernize their enterprise mainframe systems, there’s a common theme: they are faced with decades of mission-critical and historical legacy mainframe data in disparate databases,