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

Schedule: Visualization and user experience sessions

Data doesn’t matter if it doesn’t produce outcomes. This track tackles augmentation, user experience, new interfaces, interactivity, and visualization.

9:00am–5:00pm Tuesday, March 6, 2018
Data Case Studies
Location: LL20 A
Madhav Madaboosi (BP), Meenakshisundaram Thandavarayan (Infosys), Matt Conners (Microsoft), Katie Malone (Civis Analytics), Mike Prorock (mesur.io), Thomas Miller (Northwestern University), Ann Nguyen (Whole Whale), Jennie Shin (Kaiser Permanente), Valentin Bercovici (PencilDATA), Wayde Fleener (General Mills), Joe Dumoulin (Next IT), Jules Malin (GoPro), Taylor Martin Martin (O'Reilly Media), Divya Ramachandran (Captricity)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions. Read more.
1:30pm–5:00pm Tuesday, March 6, 2018
Custom interactive visualizations and dashboards for one billion datapoints on a laptop in 30 lines of Python
Location: 210 D/H
James Bednar (Anaconda), Philipp Rudiger (Anaconda)
Average rating: ****.
(4.50, 2 ratings)
Python lets you solve data science problems by stitching together packages from its ecosystem, but it can be difficult to choose packages that work well together. James Bednar and Philipp Rudiger walk you through a concise, fast, easily customizable, and fully reproducible recipe for interactive visualization of millions or billions of datapoints—all in just 30 lines of Python code. Read more.
1:50pm–2:30pm Wednesday, March 7, 2018
Spark on Kubernetes: A case study from JD.com
Location: LL21 E/F
Zhen Fan (JD.com), Wei Ting Chen (Intel Corporate)
Average rating: ****.
(4.00, 4 ratings)
Zhen Fan and Wei Ting Chen explain how JD.com uses Spark on Kubernetes in a production environment and why the company chose Spark on Kubernetes for its AI workloads. You'll learn how to run Spark with Kubernetes and gain an understanding of the advantages this provides. Read more.
2:40pm–3:20pm Wednesday, March 7, 2018
Why nobody cares about your anomaly detection
Location: LL20 A
Secondary topics:  Graphs and Time-series
Baron Schwartz (VividCortex)
Average rating: ****.
(4.80, 5 ratings)
Anomaly detection is white hot in the monitoring industry, but many don't really understand or care about it, while others repeat the same pattern many times. Why? And what can we do about it? Baron Schwartz explains how he arrived at a "post-anomaly detection" point of view. Read more.
2:40pm–3:20pm Wednesday, March 7, 2018
Semi-automated analytic pipeline creation and validation using active learning
Location: 212 A-B
Secondary topics:  Data Integration and Data Pipelines
Sean Ma (Trifacta)
Organizations leverage reporting, analytic, and machine learning pipelines to drive decision making and power critical operational systems. Sean Ma discusses methods for detecting, visualizing, and resolving inconsistencies between source and target data models across these pipelines. Read more.
4:20pm–5:00pm Wednesday, March 7, 2018
Personalization at scale: Mastering the challenges of personalization to create compelling user experiences
Location: LL21 E/F
Rahim Daya (Pinterest)
Average rating: ***..
(3.50, 4 ratings)
Personalization is a powerful tool for building sticky and impactful product experiences. Rahim Daya shares Pinterest's frameworks for building personalized user experiences, from sourcing the right contextual data to designing and evaluating personalization algorithms that can delight the user. Read more.
4:20pm–5:00pm Thursday, March 8, 2018
Big data insights equal big money: Stories from the trenches at GoDaddy
Location: 210 C/G
Felix Gorodishter (GoDaddy)
Average rating: ***..
(3.00, 2 ratings)
GoDaddy ingests and analyzes over 100,000 data points per second. Felix Gorodishter discusses the company's big data journey from ingest to automation, how it is evolving its systems to scale to over 10 TB of new data per day, and how it uses tools like anomaly detection to produce valuable insights, such as the worth of a reminder email. Read more.