Case Study: From a Monolith to Cloud, Containers, Microservices

The following shows a case study about successfully moving from a very complex monolith system to a cloud-native architecture. The architecture leverages containers and Microservices. This solve issues such as high efforts for extending the system, and a very slow deployment process. The old system included a few huge Java applications and a complex integration middleware deployment.

The new architecture allows flexible development, deployment and operations of business and integration services. Besides, it is vendor-agnostic so that you can leverage on-premise hardware, different public cloud infrastructures, and cloud-native PaaS platforms.

The session will describe the challenges of the existing monolith system, the step-by-step procedure to move to the new cloud-native Microservices architecture. It also explains why containers such as Docker play a key role in this scenario.

A live demo shows how container solutions such as Docker, PaaS cloud platforms such as CloudFoundry, cluster managers such as Kubernetes or Mesos, and different programming languages are used to implement, deploy and scale cloud-native Microservices in a vendor-agnostic way.

Key Takeaways

Key takeaways for the audience:

– Best practices for moving to a cloud-native architecture

– How to leverage microservices and containers for flexible development, deployment and operations

– How to solve challenges in real world projects

– Understand key technologies, which are recommended

– How to stay vendor-agnostic

– See a live demo of how cloud-native applications respectively services differ from monolith applications regarding development and runtime

Slides and Video from Microservices Meetup Mumbai

Here are the slides and video recording. Presented in February 2017 at Microservices Meetup Mumbai, India.

Click on the button to load the content from www.slideshare.net.

Load content

Kai Waehner

bridging the gap between technical innovation and business value for real-time data streaming, processing and analytics

Recent Posts

When (Not) to Use Queues for Kafka?

Apache Kafka has long been the foundation for real-time data streaming. With the release of…

3 days ago

Diskless Kafka at FinTech Robinhood for Cost-Efficient Log Analytics and Observability

Diskless Kafka is transforming how fintech and financial services organizations handle observability and log analytics.…

1 week ago

Shift Left in Automotive: Real-Time Intelligence from Vehicle Telemetry with Data Streaming at Rivian

Rivian and Volkswagen, through their joint venture RV Tech, process high-frequency telemetry from connected vehicles…

2 weeks ago

Etihad Airways Makes Airline Operations Real-Time with Data Streaming

Airlines face constant pressure to deliver reliable service while managing complex operations and rising customer…

3 weeks ago

Stream Processing on the Mainframe with Apache Flink: Genius or a Glitch in the Matrix?

Running Apache Flink on a mainframe may sound surprising, but it is already happening and…

1 month ago

10 FinTech Predictions That Depend on Real Time Data Streaming

Financial services companies are moving from batch processing to real time data flow. A data…

2 months ago