Data-Warehousing and -Processing with Spark

Hi All,

I just started a new adventure with Tenable.

At the same time Daniel and Terry from Gilt/HBC reached out ...

... and here we are!!!

We have 2 great talks about what Gilt/HBC and Tenable are doing with Spark (and will/want to do with Spark in the future) (see below).

Doors will open at 18:30. Talks will start at 19:00. We expect the talks to be ~30 mins with ~10 mins for questions.

Pizza and Beer will be served.

See you then/there.

Regards ...
Roland

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Title: Using Spark to power our next-gen Data-Warehouse

Presenter: Terry McCartan

Bio: Senior Data Lead at HBC; Background in Scala Engineering, Micro-Services and API Design

Target Audience:

• Enterprise DW users considering moving to Spark
• Beginner/Moderate level Spark users
• Both Engineers and Data Architects
• No previous Scala - Spark coding knowledge required

Abstract:

HBC has taken on the challenge of redesigning its data stack from a traditional all-in-one Data Warehousing solution to a Spark - Apache ecosystem.

Terry McCartan, Senior Data Lead will take you through the exciting journey that is a migration; sharing challenges encountered, solutions and lessons learned.

This talk will appeal to Data Engineers and Architects alike, looking to be convinced to migrate, learning about considerations of a migration, or wanting to compare their already established Spark environment.

Although the talk will include technical specifics and code snippets (in Scala), it is beginner friendly.

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Title: Getting started with Spark for Data Scientists

Presenter: Bryan Doyle

Bio: Senior Data Scientist at Tenable

Target Audience:

• Beginner / Intermediate level Spark users
• Data Scientists

Abstract:

This presentation will discuss how Spark has been adopted within the Data Science team at Tenable to crunch large volumes of raw sensor data.

The talk will cover topics such as; building ETL pipelines for analytics and developing machine learning models with MLlib. It will also touch on some important considerations for getting the best performance out of your Spark jobs.