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1
Intro
2
Challenges
3
ML Pipeline
4
Kubernetes
5
Cloud Native
6
Spark Over Kubernetes
7
Running Spark on Kubernetes
8
Different modes of operation
9
Spark Operator on Kubernetes
10
Recap
11
Repo
12
Challenges around Kubernetes
13
Automating devops
14
Serverless Spark
15
Functions
16
Workflows
17
Payoneer
18
CICD
19
Microservice Architecture
20
Serverless Architecture
21
MLRun
22
Summary
Description:
Discover how to leverage Kubernetes for running Apache Spark jobs in this 52-minute video presentation by Databricks. Learn about the challenges of managing Spark infrastructure and how Kubernetes offers a simplified approach to workload isolation, resource management, and on-demand deployment. Explore the benefits of unifying analytics and data science on a single cloud-native architecture, eliminating the need for separate big data clusters. Gain insights into different operational modes, the Spark Operator on Kubernetes, and strategies for automating DevOps processes. Delve into topics such as serverless Spark, functions, workflows, and microservice architecture. Understand how this integration enables more efficient ML pipelines and streamlined CICD processes. By the end of the talk, grasp the potential of combining Spark and Kubernetes to enhance your data processing capabilities and simplify your analytics infrastructure.

Running Apache Spark Jobs Using Kubernetes

Databricks
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