How Celtra Optimizes its Advertising Platform with Databricks

On-Demand Webinar

Recorded on 12/09/2015 10:00am PT, 1:00pm ET, 6:00pm UTC

The slides and notebooks for this session are available as attachments within the webinar itself. Please start the webinar, hover over the webinar, click [Attachments], and you will be able to download all the materials.

Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Apache Spark™ deployment, achieve faster project turnaround time, and empower people to make data-driven decisions.

In this webinar, you will learn how Databricks helps Celtra to:

  • Utilize Apache Spark to power their production analytics pipeline.
  • Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics.
  • Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.
  • Grega Kešpret

    Director of Engineering, Analytics - Celtra

    Grega Kešpret is the Director of Engineering for Analytics. He works at Celtra since 2012, where he helped build analytics pipeline and optimization systems. Grega also leads the team of engineers and data scientists at San Francisco and Ljubljana, working on their analytics platform. Prior to Celtra, Grega worked at IBM, helping enterprise customers adopt WebSphere Application Server and before that did a 8-month internship at SANYO (Panasonic) in Japan, working on battery systems. His current technical interests include databases, distributed systems, functional programming and machine learning.

  • Denny Lee

    Technology Evangelist - Databricks

    Denny Lee is a hands-on data architect and developer / hacker with more than 15 years of experience developing internet-scale infrastructure, data platforms, and distributed systems for both On-Premises and Cloud. His key focuses surround solving complex large scale data problems – providing not only architectural direction but hands-on implementation of these systems. Experience in building greenfield teams as well as turn around / change catalyst. His current technical interests include Apache Spark, Big Data, Machine Learning, Graph databases, Cloud Infrastructure, and Distributed Systems Robustness.