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1
Introduction
2
Intuits Data Lake
3
Kubernetes vs Yarn
4
Native workflow
5
Spark operator
6
Transaction categorization
7
Personalization problem
8
Learnings
9
SpoK Part 3
10
Advantages
11
Future plans
12
Contact details
13
Questions
14
Cost Reduction
15
Cost Advantages
16
Effort
17
Spark
18
Network bottlenecks
19
Spark Operator on Kubernetes
20
What new things should data scientists learn
21
Container Journey
22
Slides
23
Spark Containers
24
Feature Processing
25
Spark Overlay
26
What do data scientists need to learn
27
Wrap up
Description:
Explore how Intuit leverages Spark on Kubernetes to process big data at scale in this conference talk. Learn about the advantages of running data processing workloads on Kubernetes, including cost reduction and increased production speed. Discover Intuit's journey in building a data processing platform, their experiences with the Spark operator, and how they addressed challenges like network bottlenecks. Gain insights into the benefits of containerization for data scientists and the future plans for Intuit's big data infrastructure. Understand the comparison between Kubernetes and Yarn, native workflows, and the impact on transaction categorization and personalization problems.

Running Big Data Applications at Scale on K8s

CNCF [Cloud Native Computing Foundation]
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