Strata + Hadoop World 2016 - San Jose, California
2016-03-29..2016-03-31
San Jose, United States
[Strata + Hadoop World 2016 - San Jose, California, Strata + Hadoop World 2016 - New York, New York, Strata + Hadoop World 2016 - Singapore, Strata + Hadoop World 2017]
who it's for
data scientists, data engineers, machine learning engineers, platform engineers, analytics managers, security engineers, product managers
topics
commerce data catalogs; real-time streaming decisioning; MemSQL LLVM execution engine; Kafka to Spark pipelines; convolutional neural networks; computer vision at scale; Apache Hadoop ecosystem; data lakes and data swamp; Bayesian updating for inference; IoT sensor analytics
| title | author | length |
|---|---|---|
| Using Commerce Data to Fuel Innovation | Bruce Andrews | 03m |
A Commerce Data Usability project launched in January catalogs datasets and publishes runnable examples and tutorials so teams can replicate results; data… | ||
| Driving the On-Demand Economy with Predictive Analytics | Eric Frenkiel | 05m |
Announces MemSQL 5 GA with a new LLVM-based execution engine that claims 10x faster than the prior release, plus native geospatial SQL for zoomable queries… | ||
| Delivering Information in Context | Ian Andrews | 07m |
Argues that delivering information in context is the commercial differentiator, not another BI dashboard; claims the BI market is saturated — calls it peak BI… | ||
| Let's Get Real: Acting on Data in Real Time | Jack Norris | 09m |
Real time means from data collection to business action, not just low-latency queries, and requires collapsing hundreds of operational silos into a converged… | ||
| From the Belly of the Beast of AI | Jana Eggers | 02m |
Argument states AI remains rudimentary and gives the Roomba as proof: its vacuum can't detect poop even though it remembers where it went, docks itself… | ||
| Machine Learning for Human Rights Advocacy | Megan Price | 01m |
Claims that standard machine learning for conflict violence methods can accelerate investigations into atrocities by extracting patterns from text, images, and… | ||
| Using Computer Vision to Understand Big Visual Data | Alyosha Efros | 04m |
Shows that modern pipelines train convolutional neural networks on millions of images to map pixels to labels and beat humans on parts of the ImageNet… | ||
| Apache Hadoop at 10 | Doug Cutting | 02m |
Apache Hadoop at 10 positions Hadoop as a core platform for a decade of enterprise data work and claims the last ten years shift architectures from silos to… | ||
| Building practical AI systems | Adam Cheyer | 04m |
Describes a working 1993 prototype tablet that supported speech plus handwriting recognition, calendars, email and maps and argues launching earlier would have… | ||
| Open by design, open for data | Adam Kocoloski | 06m |
A 14‑ton detector generates massive raw images and a distributed metadata problem, so teams adopt NoSQL and cloud delivery, even spinning a company that later… | ||
| Advanced analytics and the mystery of the missing jeans | Bob Rogers | 07m |
Retail inventory accuracy runs about 65% so Intel and Levi's build an end to end system that uses RFID to sensors to gateways streaming into cloud analytics… | ||
| Thinking like a Bayesian | Julia Galef | 03m |
Claim: Bayes' rule isn’t mainly a formula to plug numbers into but a source of intuitive habits for updating beliefs. Describes how modern spam filters use… | ||
| Connected brains | Joseph Sirosh | 11m |
Real-time cortical recordings from surface electrodes (ECoG) stream hundreds of channels into cloud analytics where machine learning decodes perception and… | ||
| What's next for BDAS? | Michael Franklin | 05m |
Berkeley Data Analytics Stack adds three concrete systems: Succinct stores compressed data that supports regex and numeric queries without full decompression… | ||
| Nonsense science | Paula Poundstone | 01m |
A comedian argues that centralized big data is a security liability and cites the Anthem Blue Cross breach as an example of nation-state intrusions, claiming… | ||
| Apache Hadoop Meets Cybersecurity | Tom Reilly, Alan Ross | 03m |
Claim that the number one horizontal use case for Hadoop across customers is cybersecurity, backed by an open source project called Open Network Insight… | ||
| Interview with CBC's Christopher Berry | Christopher Berry | 05m |
CBC extracts podcast server logs to reconstruct download patterns and infer reach and engagement (no third‑party demographics, but popular shows and… | ||
| Interview with EMC's Ted Bardasz | Ted Bardasz | 08m |
EMC describes a concrete stack: a scale‑out storage foundation (Isilon data lake) and object tier (Elastic Cloud Storage ECS), a converged… | ||
| Why data science will help Instacart succeed where others failed - Interview with Jeremy Stanley | Jeremy Stanley | 08m |
Instacart describes a fulfillment system that only works at scale with data science: predictive time-and-space models estimate per-shopper travel and shopping… | ||
| How Orbital Insight is harnessing machine vision technology - Interview with James Crawford | James Crawford | 05m |
Machine vision uses deep learning and traditional computer vision to turn satellite pixels into counts and measurements, not vague examples. The system counts… | ||
| Data science and streaming music - Interview with Jonathan Gosier | Jonathan Gosier | 05m |
AuDIGENT frames music data as stream science that captures play starts, stops, shares and repeat listens across Spotify, Pandora and YouTube, not just… | ||
| Pitfalls and practical applications of data visualization - Interview with Julie Rodriguez | Julie Rodriguez | 10m |
Argues that visualization tools are not use-case driven and drown users in choices so people resort to trial-and-error; documents a finance-focused, use-case… | ||
| Technical onboarding and hiring diversity - Cultivate interview with Kate Heddleston | Kate Heddleston | 07m |
Technical onboarding means preparing new engineers to ship code reliably by teaching the codebase plus the team’s deployment and review practices; recommended… | ||
| Robots, houses & the IoT - Microsoft 's Brandon Rohrer on making things intelligent | Brandon Rohrer | 07m |
Claim: putting a learning brain between existing sensors and actuators turns a house into an adaptive robot rather than a blunt instrument. Example: the… | ||
| Interview with Jack Norris, MapR SVP Data & Applications | Jack Norris | 09m |
Claims that 2016 is the year of real time across the whole process with analytics embedded into operations to make machine generated adjustments, not just… | ||
| Avoiding legal privacy and security snafus with big data and the IoT | Alysa Z Hutnik, Kristi Wolff | 34m |
Hypothetical my life app ties phone, Apple Watch, connected home and car into one data stream and attracts the exact risks regulators are enforcing now… | ||
| nothing matches | ||