conference

Strata + Hadoop World 2016 - London, United Kingdom: Video Compilation

by O'Reilly Media, Inc.

June 2016

103h 45m

English

Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional)

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Course outline

Modern data strategy and CERN - Mike Olson (Cloudera) and Manuel Martin Marquez (CERN)
15m 8s
The Internet of Things: It’s the (sensor) data, stupid - Martin Willcox (Teradata International)
11m 11s
Data relativism and the rise of context services - Joe Hellerstein (UC Berkeley)
15m 9s
Saving whales with deep learning - Piotr Niedzwiedz (deepsense.io)
5m 15s
Data wants to be shareable - Mona Vernon (Thomson Reuters Labs)
13m 21s
Analytics innovation in cancer research - Gilad Olswang (Intel)
5m 53s
The future of (artificial) intelligence - Stuart Russell (UC Berkeley)
20m 19s
The curious case of the data scientist - David Selby (IBM)
11m 5s
Drawing insights from imperfection: A year of Dear Data - Stefanie Posavec (NA)
14m 41s
Big data at Google: Solving problems at scale - Jordan Tigani (Google)
5m 3s
The other half of big data - Tricia Wang (Constellate Data)
17m 13s
Bringing big data and design to policy making - Cat Drew (UK Policy Lab and Government Data Science Partnership)
13m 45s
Machine learning for human rights advocacy: Big benefits, serious consequences - Megan Price (Human Rights Data Analysis Group)
15m 13s
A hands-on introduction to Apache Kafka - Ian Wrigley (Confluent) - Part 1
1h 26m 3s
A hands-on introduction to Apache Kafka - Ian Wrigley (Confluent) - Part 2
1h 33m 54s
AI for business: A hands-on introduction to what machine learning can do - Marc Warner (ASI) - Part 1
1h 23m 7s
AI for business: A hands-on introduction to what machine learning can do - Marc Warner (ASI) - Part 2
1h 8m 30s
Experiments in The Data Lab: Creating a national hub for data science in Scotland - Brian Hills (The Data Lab)
36m 29s
The innards of H2O - Cliff Click (0xdata)
40m 55s
TensorFlow: Machine learning for everyone - Sherry Moore (Google)
38m 57s
The future of column-oriented data processing with Arrow and Parquet - Julien Le Dem (Dremio)
43m 41s
90% of the world's trade is transported by sea, but what data do we have about ship activity worldwide? - Tal Guttman (Windward)
39m 50s
The evolution of massive-scale data processing - Tyler Akidau (Google)
41m 36s
Streaming analytics at 300 billion events per day with Kafka, Samza, and Druid - Xavier Léauté (Metamarkets)
43m 50s
Triggers in Apache Beam (incubating): User-controlled balance of completeness, latency, and cost in streaming big data pipelines - Kenneth Knowles (Google)
44m 32s
Introducing Kafka Streams, Apache Kafka's new stream processing library - Neha Narkhede (Confluent)
47m 5s
R and reproducible reporting for big data - Aimee Gott (Mango Solutions), Mark Sellors (Mango Solutions), and Richard Pugh (Mango Solutions) - Part 1
1h 19m 29s
R and reproducible reporting for big data - Aimee Gott (Mango Solutions), Mark Sellors (Mango Solutions), and Richard Pugh (Mango Solutions) - Part 2
1h 17m 56s
Deep learning and natural language processing with Spark - Andy Petrella (Data Fellas) and Melanie Warrick (Skymind)
40m 29s
Semantic natural language understanding with Spark Streaming, UIMA, and machine-learned ontologies - David Talby (Atigeo) and Claudiu Branzan (Atigeo)
45m 30s
Sightseeing, venues, and friends: Predictive analytics with Spark ML and Cassandra - Natalino Busa (Teradata)
38m 44s
Introduction to generalized low-rank models and missing values - Jo-fai Chow (H2O.ai)
29m 12s
Petascale genomics - Tom White (Cloudera)
40m 13s
Panel: The future of intelligence - Marc Warner (ASI), Stuart Russell (UC Berkeley), and Jaan Tallinn (CSER)
39m 20s
The polyglot data scientist - Jeroen Janssens (Tilburg University)
25m 11s
Beyond guide dogs: How advances in deep learning can empower the blind community - Anirudh Koul (Microsoft) and Saqib Shaikh (Microsoft)
37m 52s
Predicting out-of-sample performance of a large cohort of trading algorithms with machine learning - Thomas Wiecki (Quantopian)
38m 30s
Scala: The unpredicted lingua franca for data science - Andy Petrella (Data Fellas) and Dean Wampler (Lightbend)
42m 56s
Land mine or Coke can: Machine learning from GPR data - Dirk Gorissen (Skycap | World Bank)
33m 39s
Data modeling for data science: Simplify your workload with complex types - Marcel Kornacker (Cloudera)
40m 28s
Applications of natural language understanding: Tools and technologies - Alyona Medelyan (Entopix)
39m 31s
Developing a modern enterprise data strategy - Scott Kurth (Silicon Valley Data Science) and John Akred (Silicon Valley Data Science) - Part 1
1h 28m 28s
Developing a modern enterprise data strategy - Scott Kurth (Silicon Valley Data Science) and John Akred (Silicon Valley Data Science) - Part 2
1h 24m 42s
The Bag of Little Bootstraps: A/B experimenting with big data made small - Emily Sommer (Etsy)
38m 23s
Beyond the hunch: Communicating uncertainty for effective data-driven business - Abigail Lebrecht (uSwitch)
40m 31s
What’s next for music services? The answer is in the data - Paul Shannon (7digital Group Plc) and Alan Hannaway (7digital)
42m 48s
Intuit, Uber, and Etsy: Scaling innovation with A/B testing - Lucian Lita (Intuit), Mita Mahadevan (Intuit Inc.), Shalin Mantri (Uber), and Gabrielle Gianelli (Etsy)
43m 48s
How AI revolutionizes business strategy - Kenneth Cukier (The Economist)
43m 32s
The best university in the world - Duncan Ross (TES Global) and Francine Bennett (Mastodon C)
44m 50s
20 percent blissful, 80 percent ignorance - Phil Harvey (DataShaka)
24m 40s
Data gravity and complex systems - Dave McCrory (Basho Technologies)
28m 21s
Analytics: A first-class architectural concern in a SaaS platform - Calum Murray (Intuit)
35m 8s
Situational awareness: On the importance of mapping - Simon Wardley (Leading Edge Forum (CSC))
42m 31s
Data-driven businesses: Disrupting business models with big data - Carme Artigas (Synergic Partners)
24m 35s
Building better cross-team communication - Ellen Friedman (Independent)
23m 46s
What Esperanto can teach us about collaboration in the big data environment - Anne Sophie Roessler (Dataiku)
19m 53s
What should I eat: The road map to better food and smarter nutrition science - Taryn Fixel (ingredient1)
22m 35s
Your TOS is not informed consent: Ethical experimentation for the Web - Rachel Shadoan (Akashic Labs)
22m 19s
How to ask good questions - Farrah Bostic (The Difference Engine)
30m 27s
Every business is a data business - Mona Vernon (Thomson Reuters Labs)
27m 36s
Data scientists everywhere - Kim Nilsson (Pivigo)
21m 9s
Harnessing big data to transform the energy sector - Erik Nygard (Limejump Ltd)
13m 57s
Data science as catalyst of Autodesk's business model transformation - Laurent Gaubert (Autodesk)
19m 24s
My AlgorithmicMe knows me better than Google or my mum - Majken Sander (BusinessAnalyst.dk)
22m 49s
Otto’s little army of real-time bots: How online retailers can defend shopping carts and retarget customers in real time - Rupert Steffner (Otto GmbH & Co. KG)
21m 42s
My AlgorithmicMe: The "Who is. . .?" of the future - Majken Sander (BusinessAnalyst.dk) and Joerg Blumtritt (Datarella)
38m 28s
Demonstrating the art of the possible with Spark and Hadoop - Joy Spohn (IBM) and Adrian Houselander (IBM)
34m 48s
Apache Hadoop operations for production systems - Jayesh Seshadri (Cloudera), Justin Hancock (Cloudera), Mark Samson (Cloudera), and Wellington Chevreuil (Cloudera) - Part 1
1h 25m 46s
Apache Hadoop operations for production systems - Jayesh Seshadri (Cloudera), Justin Hancock (Cloudera), Mark Samson (Cloudera), and Wellington Chevreuil (Cloudera) - Part 2
1h 34m 4s
Apache Hadoop operations for production systems - Jayesh Seshadri (Cloudera), Justin Hancock (Cloudera), Mark Samson (Cloudera), and Wellington Chevreuil (Cloudera) - Part 3
1h 23m 20s
Apache Hadoop operations for production systems - Jayesh Seshadri (Cloudera), Justin Hancock (Cloudera), Mark Samson (Cloudera), and Wellington Chevreuil (Cloudera) - Part 4
1h 19m 21s
Architecting a data platform - John Akred (Silicon Valley Data Science) and Stephen O'Sullivan (Silicon Valley Data Science) - Part 1
1h 24m 41s
Architecting a data platform - John Akred (Silicon Valley Data Science) and Stephen O'Sullivan (Silicon Valley Data Science) - Part 2
1h 36m 3s
Big SQL: The future of in-cluster analytics and enterprise adoption - Moderated by: Surya Mukherjee (Ovum) - Panelists: Lloyd Tabb (Looker Data Science), Nick Amabile (FullStack Analytics), Rex Gibson (Knewton), dp Suresh (Yahoo!)
39m 16s
BI on Hadoop: What are your options? - Tomer Shiran (Dremio)
40m 44s
Hadoop application architectures: Fraud detection - Jonathan Seidman (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent), and Ted Malaska (Cloudera) - Part 1
1h 29m 48s
Hadoop application architectures: Fraud detection - Jonathan Seidman (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent), and Ted Malaska (Cloudera) - Part 2
1h 23m 17s
The next 10 years of Apache Hadoop - Doug Cutting (Cloudera), Tom White (Cloudera), and Ben Lorica (O'Reilly Media)
39m 56s
Hadoop's storage gap: Resolving transactional access/analytic performance trade-offs with Apache Kudu (incubating) - Todd Lipcon (Cloudera, Inc.)
41m 54s
Building real-time BI systems with HDFS and Kudu - Ruhollah Farchtchi (Zoomdata)
35m 37s
Why is my Hadoop job slow? - Bikas Saha (Hortonworks Inc)
39m 4s
Scaling out to 10 clusters, 1,000 users, and 10,000 flows: The Dali experience at LinkedIn - Carl Steinbach (LinkedIn)
35m 43s
Floating elephants: Developing data wrangling systems on Docker - Chad Metcalf (Docker) and Seshadri Mahalingam (Trifacta)
29m 7s
Developing data scientists: Breaking the skills cap - Yuelin Li (ASI)
28m 42s
The business case for Spark, Kafka, and friends - John Akred (Silicon Valley Data Science)
31m 12s
What is AI? - Melanie Warrick (Skymind)
28m 1s
Mobile advertising: The preclick experience - Mounia Lalmas (Yahoo)
26m 40s
Analytics for large-scale time series and event data - Ira Cohen (Anodot)
29m 31s
Recent trends in recommender systems - Danny Bickson (1972)
28m 50s
Visual data analysis for intelligent machines - Francesca Odone (University of Genova)
33m 9s
Deep learning for web-scale text - Piotr Mirowski (Google DeepMind)
27m 54s
Detecting anomalies in the real world - Alessandra Staglianò (The ASI)
31m 5s
Recent advances in deep learning research - Olivier Grisel (Inria & scikit-learn)
31m 46s
Hardcore data science in practice - Mikio Braun (Zalando SE)
29m 16s
Data science++: Improving data science by adding domain understanding - Matthew Smith (Microsoft Research)
28m 31s
A methodology for taxonomy generation and maintenance from large collections of textual data - Roxana Danger (reed.co.uk)
27m 58s
A functional data integration pipeline using Scala - Johannes Bauer (Cambridge Analytica)
40m 11s
An Introduction to time series with Team Apache - Patrick McFadin (DataStax) - Part 1
1h 12m 50s
An Introduction to time series with Team Apache - Patrick McFadin (DataStax) - Part 2
1h 31m 21s
What does your smart car know about you? - Charles Givre (Booz | Allen | Hamilton)
42m 25s
When it absolutely, positively has to be there: Reliability guarantees in Kafka - Gwen Shapira (Confluent) and Jeff Holoman (Cloudera)
43m 27s
Real-time epilepsy monitoring with smart clothing: A case study in time series, open source technology, and connected devices - Eric Kramer (Dataiku)
37m 58s
Industrial big data and sensor time series data: Different but not difficult - Gopal GopalKrishnan (OSIsoft, LLC.) and Hoa Tram (OSIsoft)
50m 56s
High-performance data flow with a GUI—and guts - Simon Elliston Ball (Hortonworks)
41m 47s
Watermarks: Time and progress in streaming dataflow and beyond - Slava Chernyak (Google Inc.)
35m 1s
Putting Kafka into overdrive - Gwen Shapira (Confluent) and Todd Palino (LinkedIn)
39m 39s
Stream analytics in the enterprise: A look at Intel’s internal IoT implementation - Moty Fania (Intel)
39m 42s
Legacy or Kafka? What an ideal messaging system should bring to Hadoop - Jim Scott (MapR Technologies, Inc.)
38m 51s
Making sense of exactly-once semantics - Flavio Junqueira (Confluent)
39m 45s
Processing billions of events in real time with Heron - Karthik Ramasamy (Twitter)
48m 5s
Data privacy in the age of the Internet of Things - Alasdair Allan (Babilim Light Industries)
35m 3s
Kappa architecture in the telecom industry - Ignacio Manuel Mulas Viela (Ericsson) and Nicolas Seyvet (Ericsson AB)
33m 51s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Jayant Shekhar (Sparkflows Inc.), Vartika Singh (Cloudera), and Krishna Sankar (Volvo Cars) - Part 1
1h 30m 58s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Jayant Shekhar (Sparkflows Inc.), Vartika Singh (Cloudera), and Krishna Sankar (Volvo Cars) - Part 2
1h 28m 39s
Spark 2.0: What’s next? - Tathagata Das (Databricks)
41m 39s
Anomaly detection in telecom with Spark - Ted Dunning (MapR Technologies)
44m 47s
Beyond shuffling: Tips and tricks for scaling Spark jobs - Holden Karau (IBM)
41m 25s
Securing Apache Spark on production Hadoop clusters - Kostas Sakellis (Cloudera)
40m 19s
The future of streaming in Spark: Structured streaming - Tathagata Das (Databricks)
41m 57s
Introduction to Apache Spark for Java and Scala developers - Ted Malaska (Cloudera)
39m 53s
Breaking Spark: Top five mistakes to avoid when using Apache Spark in production - Neelesh Srinivas Salian (Cloudera)
27m 42s

Content preview from Strata + Hadoop World 2016 - London, United Kingdom: Video Compilation

Simplifying Hadoop with RecordService, a secure and unified data access path for compute frameworks - Alex Leblang (Cloudera)

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Strata + Hadoop World London 2015: Video Compilation

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Publisher Resources

ISBN: 9781491944639Errata Page

Strata + Hadoop World 2016 - London, United Kingdom: Video Compilation

by O'Reilly Media, Inc.

Keynotes

Data innovations

Data science & advanced analytics

Data-driven business

Enterprise adoption

Hadoop internals & development

Data 101

Hardcore data science

IoT & real-time

Spark & beyond

Visualization & user experience

Sponsored

Security

Hadoop use cases

Law, ethics, governance

Content preview from Strata + Hadoop World 2016 - London, United Kingdom: Video Compilation

Simplifying Hadoop with RecordService, a secure and unified data access path for compute frameworks - Alex Leblang (Cloudera)

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

You might also like

Strata + Hadoop World London 2015: Video Compilation

Strata + Hadoop World Conference in Barcelona 2014: Complete Video Compilation

Strata Conference New York + Hadoop World 2014: Video Compilation

Strata + Hadoop World New York 2015: Video Compilation

Publisher Resources

Keynotes

Data innovations

Data science & advanced analytics

Data-driven business

Enterprise adoption

Hadoop internals & development

Data 101

Hardcore data science

IoT & real-time

Spark & beyond

Visualization & user experience

Sponsored

Security

Hadoop use cases

Law, ethics, governance

Content preview from Strata + Hadoop World 2016 - London, United Kingdom: Video Compilation

Simplifying Hadoop with RecordService, a secure and unified data access path for compute frameworks - Alex Leblang (Cloudera)

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,and much more.

You might also like

Strata + Hadoop World London 2015: Video Compilation

Strata + Hadoop World Conference in Barcelona 2014: Complete Video Compilation

Strata Conference New York + Hadoop World 2014: Video Compilation

Strata + Hadoop World New York 2015: Video Compilation

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.