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Big Data in the Cloud
Leverage the Power of Big Data


Space is limited.

Come and learn how companies like Comcast, Pinterest, Oracle, Expedia, Activision Blizzard, Box, Thomson Reuters, Lyft, Autodesk, Under Armour, Adobe, Roku, Reddit and Turner have made big data analytics work in the cloud. This event is for Data Architects, DataOps, Data Analysts, and Data Scientists. 


Key questions that will be answered: 

  • What are the building blocks and best practices of a modern data platform?
  • How to build out Data Lakes for creating a self-service organization? 
  • How to handle the inability to scale infrastructure to meet growing volumes of data?
  • Based on real-world customer experiences, what are the best practices of scaling out big data in the cloud?  
  • How to stop spending more time managing infrastructure vs. exploiting the data? 
  • Enabling access to the data across the organization to leverage data for better decisions/insights? 


Speaker 1: Tom Mack from Qubole will cover several customer case studies as well as best practices. 

Qubole has a cloud-based data platform tool that self-manages, self-optimizes and learns to improve automatically and as a result delivers unbeatable agility, flexibility, and TCO. It allows companies to focus on their data, not their data platform. 

The topics covered will include: 

  • Example customer journeys to the cloud from on-premise Hadoop
  • Bridging the gap between the 'data operations/data engineering teams' and the data analysis/data science teams
  • Saving money and reducing costs in the cloud
  • Focusing on the business value of analytics rather than the operational overhead
  • Making data science more accessible to more users


Speaker 2: Ajith Ramanath will provide technical depth and a demo.

In 2017, Qubole saved customers $140M in cloud costs by smartly - and automatically - leveraging all the cloud resources available to business. Customers provide self-service analytics for Data Analysts and Data Scientists and use machine learning to query and cluster metadata to make recommendations and improve efficiencies. 

The topics covered will include: 

  • Implications of per second billing on cluster management
  • How to scale your big data infrastructure in the cloud
  • Evolving architecture patterns in the cloud - the advantages in separation of compute and storage
  • Persona view: Data Ops / Data Analysts / Data Scientists
  • Platform Demo - Hive, Presto, Hadoop, and Spark

Row 4: 2-Column Break Out Sections

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The 'headline' class removes top margin on heading tags

This is a nested "row" inside of the left column. It's 5/6 width (col-sm-9) and can be adjusted the same as the larger columns (instructions above). You can turn this entire row off by toggling the "Left Column: Nested Row 2" variable to "Hide".

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The 'headline' class removes top margin on heading tags

This is a nested "row" inside of the left column. It's 5/6 width (col-sm-9) and can be adjusted the same as the larger columns (instructions above). You can turn this entire row off by toggling the "Left Column: Nested Row 2" variable to "Hide".

The 'headline' class removes top margin on heading tags

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