The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

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Spark on Kubernetes

Submitted by Shrashti Gupta (@shrashtigupta90) on Wednesday, 10 April 2019


Preview video

Session type: Full talk of 40 mins

Abstract

Typical data processing and machine learning workloads includes heavy setups like Hadoop stack, Kafka, NoSQL databases, Application APIs and so on. Traditionally, these workloads run on top of dedicated setups which adds overhead to IT teams as well as developers in managing multiple clusters. It is a need of the hour to develop unified solution to manage all the workloads on single control plane. With the help of containerization and Kubernetes we can achieve that easily.

Apache Spark is an essential tool for data engineers and data scientists, offering a robust platform for a variety of applications ranging from large scale data transformation to analytics to machine learning. We are already convinced and adopting containers to improve our workflows by realizing benefits such as packaging of dependencies and creating reproducible artifacts
it makes total sense to run Spark with our rest of the solution already running on top of Kubernetes. Thanks to the Apache Spark and Kubernetes contributors who have put lot of efforts for bringing Apache Spark 2.3 with native Kubernetes support.

Why it’s a big deal?

  • Unified platform for entire complicated data pipelines: simplifies cluster management.
  • Better utilization of resources.
  • All the good things of Kubernetes are readily available to be used with Spark such as Pluggable Authorization and Logging.
  • Better allocation of resources across multiple spark application because of great feature called “Namespace” in Kubernetes and resource quotas.
  • Future opportunities: managing streaming workloads and leveraging service meshes like Istio.

Outline

  • How Spark works on Kubernetes: Architecture and internal working.
  • Why it’s important for present day applications.
  • Use cases.
  • Spark future roadmap for Kubernetes.
  • Quick demo.

Requirements

Basic knowledge of Kubernetes and Spark

Speaker bio

Shrashti is a Google Cloud certified Data Engineer currently associated with Publicis Sapient. She has worked on multiple engagements with clients from Automobile as well Telecom domain. In current role, she is working on Hyper-personalized recommendation system for Automobile industry focused on Machine Learning in which she is responsible for handling Realtime data processing as well as batch data processing pipelines and extensively worked on Kubernetes for managing overall infrastructure.

Links

Slides

https://drive.google.com/file/d/1Y9QPREzbWaRScWMpHYPRGpJSewlWN5q7/view?usp=sharing

Preview video

https://drive.google.com/file/d/12Sto8-vKE_x3E7GvHYv-23r7w6hl_R4q/view?usp=sharing

Comments

  • Anwesha Sarkar (@anweshaalt) Reviewer 8 months ago

    Thank you for submitting the proposal. Submit your slides and preview video by 20th April (latest) it helps us to close the review process.

  • Zainab Bawa (@zainabbawa) Reviewer 6 months ago

    Thanks for the slides and preview video, Shrashti.

    The following has to be incorporated in your slides:

    1. What is the problem in general, which Spark on Kubernetes solves? Why is this problem important enough for participants at The Fifth Elephant to look at?
    2. Why choose Spark on Kubernetes as the solution? Which other tools are solving the same problem? How do these compare with Spark on Kubernetes?
    3. How does this work in implementation?
    4. How does this work at various stages in data engineering in different companies?
    5. How was life before Spark on Kubernetes and how is life afterwards – show before-after scenarios.
    6. What are the trade-offs of implementing this?

    Share the revised slides, incorporating all of the above by or before 21 May. We will make a final decision accordingly.

    • Shrashti Gupta (@shrashtigupta90) Proposer 6 months ago (edited 6 months ago)

      Hi Zainab, I have revised the slides as per the feedback. Please review.

  • Shrashti Gupta (@shrashtigupta90) Proposer 6 months ago

    Hi can you please provide feedback on the same?

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