Cloud Native Middleware Microservices – 10 Lessons Learned (O’Reilly Software Architecture 2017, New York)

I want to share my slide deck and video recordings from the talk “10 Lessons Learned from Building Cloud Native Middleware Microservices” at O’Reilly Software Architecture April 2017 in New York, USA in April 2017.

Abstract
Microservices are the next step after SOA: Services implement a limited set of functions; services are developed, deployed, and scaled independently; continuous delivery automates deployments. This way you get shorter time to results and increased flexibility. Containers improve things even more, offering a very lightweight and flexible deployment option.

In the middleware world, you use concepts and tools such as an enterprise service bus (ESB), complex event processing (CEP), business process management (BPM), or API gateways. Many people still think about complex, heavyweight central brokers. However, microservices and containers are not only relevant for custom self-developed applications but are also a key requirement to make the middleware world more flexible, Agile, and automated.

Kai Wähner shares 10 lessons learned from building cloud-native microservices in the middleware world, including the concepts behind cloud native, choosing the right cloud platform, and when not to build microservices at all, and leads a live demo showing how to apply these lessons to real-world projects by leveraging Docker, CloudFoundry, and Kubernetes to realize cloud-native middleware microservices.

Slide Deck

Here is the slide deck “10 Lessons Learned from Building Cloud Native Middleware Microservices“:

You are currently viewing a placeholder content from Default. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.

More Information

Video Recordings / Live Demos

Two video recordings which demo how to apply the discussed lessons learned with middleware and open source frameworks:

Kai Waehner

bridging the gap between technical innovation and business value for real-time data streaming and applied AI.

Recent Posts

Why Databricks and Snowflake Speak the Kafka Protocol: Ingestion vs. Architecture

Databricks and Snowflake now speak the Kafka protocol. But using the Kafka API to feed…

8 hours ago

Choosing an ERP for Manufacturing: How AI Is Reshaping the Vendor Landscape

ERP vendor selection for manufacturing is not a product decision. It is a strategic bet…

1 week ago

Process Intelligence Explained: Mining, Orchestration, and the Decision Gate

Process intelligence is not a single tool. It combines process mining, process orchestration, and a…

2 weeks ago

ERP Migration to SAP S/4HANA and Beyond: Lessons Learned from German Manufacturing

ERP modernization fails when the technology leads and the process work follows. Three German manufacturers…

3 weeks ago

Beyond Enterprise Data Lineage: The Case for a Platform-Independent Data Catalog

Most organizations start their data governance journey by asking how to track where data comes…

1 month ago

Data Ownership in the Age of Agentic AI: Why SAP’s API Policy Forces a Data Integration Reckoning for Every Enterprise

Every enterprise is being told to go agentic. Meanwhile, the platforms holding your most critical…

2 months ago