Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

A deep dive into running data analytic workloads in the cloud

9:00am12:30pm Tuesday, March 6, 2018

Who is this presentation for?

  • Hadoop administrators, data engineers, and BI analysts

Prerequisite knowledge

  • A basic understanding of AWS or Azure cloud infrastructure
  • Familiarity with big data concepts

Materials or downloads needed in advance

  • A laptop with an SSH client installed

What you'll learn

  • Learn how to successfully run a data analytics pipeline in the cloud and integrate data engineering and data analytic workflows
  • Understand the considerations and best practices for data analytics pipelines in the cloud


Public cloud usage for large-scale data processing is rapidly increasing, and running data engineering workloads in the cloud is becoming easier and more cost effective. Compute engines have adapted to leverage cloud infrastructure, including object storage and elastic compute. For example, Hive, Spark, Impala, and HBase compute engines are able to read input from and write output directly to AWS S3 and Azure Data Lake storage. Moreover, these read and write paths have been optimized for fast processing speeds, lowering the overall cost of running a job. In addition, platform-as-a-service offerings for data processing in the cloud have evolved to minimize the operational overhead of clusters, enabling end users to focus on developing, running, and troubleshooting jobs.

It is important for end users to be able to implement data pipeline workflows that seamlessly transition from one stage of the data pipeline to the next. Aishwarya Venkataraman, Jason Wang, Mala Ramakrishnan, Stefan Salandy, and Vinithra Varadharajan lead a deep dive into running data analytic workloads in a managed service capacity in the public cloud and highlight cloud infrastructure best practices.

Photo of Jason Wang

Jason Wang


Jason is a software engineer at Cloudera focusing on the cloud.

Photo of Mala Ramakrishnan

Mala Ramakrishnan


Mala Ramakrishnan heads product initiatives for Cloudera Altus – big data platform-as-a-service. She has 17+ years experience in product management, marketing, and software development in organizations of varied sizes that deliver middleware, software security, network optimization, and mobile computing. She holds a master’s degree in computer science from Stanford University.

Stefan Salandy


Cloudera Systems Engineer

Aishwarya Venkataraman

Photo of Vinithra Varadharajan

Vinithra Varadharajan


Vinithra Varadharajan is an engineering manager in the cloud organization at Cloudera, where she is responsible for products such as Cloudera Director and Cloudera’s usage-based billing service. Previously, Vinithra was a software engineer at Cloudera, working on Cloudera Director and Cloudera Manager with a focus on automating Hadoop lifecycle management.

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