Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Running data analytic workloads in the cloud

Eugene Fratkin (Cloudera), Vinithra Varadharajan (Cloudera), Mael Ropars (Cloudera), Jason Wang (Cloudera)
9:0012:30 Tuesday, 22 May 2018
Data engineering and architecture
Location: Capital Suite 13 Level: Intermediate
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data engineers, developers, data scientists, system architects, system administrators, and those working in information security

Prerequisite knowledge

  • A basic understanding of data warehousing

Materials or downloads needed in advance

  • A WiFi-enabled laptop
  • To work with a command-line interface (optional), install Python 2.7 or above (Be able to install packages using PIP.)

What you'll learn

  • Learn how to create data pipelines and manage them in the cloud and in hybrid cloud environments
  • Understand how to implement metadata sharing and discovery across data applications


Over the past several years, ever-increasing quantities of data are being processed within public clouds. The cloud promises to provide solutions to some of the limitations of conventional single multipurpose clusters offering hyperscale storage decoupled from elastic, on-demand compute and allows data to be shared between on-demand provisioned processing engines such as Hive, Spark, and Impala. But to fulfill this promise, you first need to solve several technical challenges: simple resource allocation, cross-cluster metadata sharing, and a common authorization framework. Without comprehensive answers to these questions, the challenges of single cluster model are simply duplicated inside a public cloud environment.

The cloud enables the delivery of solutions to single, multipurpose clusters offering hyperscale storage decoupled from elastic, on-demand computing. Vinithra Varadharajan, Jason Wang, Eugene Fratkin, and Mael Ropars detail new paradigms to effectively run production-level pipelines with minimal operational overhead. As a part of the deep dive, they also walk you through creating such a pipeline and executing data processing and data analytic workflows. Join in to learn how to remove barriers to data discovery, metadata sharing, and access control.

Photo of Eugene Fratkin

Eugene Fratkin


Eugene Fratkin is a director of engineering at Cloudera, heading Cloud R&D. He was one of the founding members of the Apache MADlib project (scalable in-database algorithms for machine learning). Previously, Eugene was a cofounder of a Sequoia Capital-backed company focusing on applications of data analytics to problems of genomics. He holds PhD in computer science from Stanford University’s AI lab.

Photo of Vinithra Varadharajan

Vinithra Varadharajan


Vinithra Varadharajan is a senior engineering manager in the cloud organization at Cloudera, where she’s responsible for the cloud portfolio products, including Altus Data Engineering, Altus Analytic Database, Altus SDX, and Cloudera Director. Previously, Vinithra was a software engineer at Cloudera working on Cloudera Director and Cloudera Manager with a focus on automating Hadoop lifecycle management.

Photo of Mael Ropars

Mael Ropars


Mael Ropars is a senior sales engineer at Cloudera, helping customers solve their big data problems using enterprise data hubs based on Hadoop. Mael has 15 years’ experience working around big data, information management, and middleware in technical sales and service delivery.

Photo of Jason Wang

Jason Wang


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