Apache Spark Streaming in K8s with ArgoCD & Spark Operator
Databricks
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Apache Spark Streaming in K8s with ArgoCD & Spark Operator
4 348 просмотров · 5 лет назад
Databricks
168 тыс. подписчиков
4 348 просмотров · 5 лет назад
Over the last year, we have been moving from a batch processing jobs setup with Airflow using EC2s to a powerful & scalable setup using Airflow & Spark in K8s.
The increasing need of moving forward with all the technology changes, the new community advances, and multidisciplinary teams, forced us to design a solution where we were able to run multiple Spark versions at the same time by avoiding duplicating infrastructure and simplifying its deployment, maintenance, and development.
In our talk, we will be covering our journey about how we ended up with a CI/CD setup composed by a Spark Streaming job in K8s consuming from Kafka, using the Spark Operator and deploying with ArgoCD.
This talk aims to provide an holistic view: from the early and more rudimentary model – batch setup in Airflow & EC2 instances – to the current and more sophisticated one – a streaming with CI/CD in K8s.
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