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Using Machine Learning to Ensure the Capacity Safety of Individual Microservices

Uber Engineering

Reliability engineering teams at Uber build the tools, libraries, and infrastructure that enable engineers to operate our thousands of microservices reliably at scale.

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Four lessons learned when working with Microservices

Zalando Engineering

For a couple of weeks now, our team at Zalando has been implementing Microservices for a new feature, which we’re looking forward to sharing with you. This is our first big project as a team and there are a lot of learnings to share. With this blog post I’d like to address the lessons we’ve taken on when working with Microservices.

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Staying in the Zone: How DoorDash used a service mesh to manage  data transfer, reducing hops and cloud spend

DoorDash Engineering

There have been many benefits gained through DoorDash’s evolution from a monolithic application architecture to one that is based on cells and microservices. As the number of microservices and back-ends has grown, however, DoorDash has observed an uptick in cross-availability zone (AZ) data transfer costs.

Bytes 84
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Apache Kafka Vs Apache Spark: Know the Differences

Knowledge Hut

Kafka stream can be used as part of microservice, as it's just a library. In other words, because Apache Spark uses current machine learning frameworks and processes graphs, it has the ability to do more than merely understand data. In fact, some models perform continuous, online learning, and scoring. cache, local space).

Kafka 98
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Keeping Multiple Databases in Sync Using Kafka Connect and CDC

Confluent

Microservices have numerous benefits, but data silos are incredibly challenging. Learn how Kafka Connect and CDC provide real-time database synchronization, bridging data silos between all microservice applications.

Kafka 119
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12 Golden Signals To Discover Anomalies And Performance Issues on Your AWS RDS Fleet

Zalando Engineering

TL;DR : Database per service pattern in the microservices world brings an overhead on operating database instances, observing its health status and anomalies. We have incorporated learning from past incidents, anomalies and empirical observations into a methodology of observing the health status using 12 golden signals.

AWS 76
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How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka

Confluent

Apache Kafka’s Streams API embeds Machine Learning into any app or microservice (Java, Docker, Kubernetes, etc.) to add business value.