conference

Strata + Hadoop World 2016 - San Jose, California: Video Compilation

by O'Reilly Media, Inc.

April 2016

Beginner to intermediate

174h 16m

English

O'Reilly Media, Inc.

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

Watch now

Unlock full access

Course outline

Apache Hadoop at 10 - Doug Cutting (Cloudera)
15m 9s
Driving the on-demand economy with predictive analytics - Eric Frenkiel (MemSQL)
5m 16s
Machine learning for human rights advocacy: Big benefits, serious consequences - Megan Price (Human Rights Data Analysis Group)
11m 39s
Let's get real: Acting on data in real time - Jack Norris (MapR Technologies)
9m 12s
Delivering information in context - Ian Andrews (Pivotal)
6m 41s
Using commerce data to fuel innovation - Bruce Andrews (US Department of Commerce)
13m 35s
Summoning the demon: My perspective from the belly of the beast of AI - Jana Eggers (Nara Logics)
13m 25s
Using computer vision to understand big visual data - Alyosha Efros (UC Berkeley)
13m 23s
Apache Hadoop meets cybersecurity - Tom Reilly (Cloudera) and Alan Ross (Intel Corporation)
9m 36s
Thinking like a Bayesian - Julia Galef (Center for Applied Rationality)
14m 1s
Connected brains - Joseph Sirosh (Microsoft)
11m 19s
Building practical AI systems - Adam Cheyer (Viv)
14m 31s
Advanced analytics and the mystery of the missing jeans - Bob Rogers (Intel)
7m 3s
What's next for BDAS (the Berkeley Data Analytics Stack)? - Michael Franklin (AMPLab/UC Berkeley)
10m 45s
Open by design, open for data - Adam Kocoloski (IBM)
6m 21s
Nonsense science - Paula Poundstone (Star of NPR's #1 radio show, "Wait Wait...Don't Tell Me")
23m 57s
The 21st century leader: Shaping the future - Eric McNulty
54m 7s
Build to lead: Solve leadership challenges using the Lego Serious Play methodology - Dieter Reuther, Donna Denio (Team Dynamics Boston)
41m 45s
Culture is your company's operation system - Dave Gray (XPLANE)
57m 40s
Cross-functional leadership for high-performance product teams - Dan Olsen (The Lean Product Playbook)
40m 30s
The proven ROI of designing culture - Kristi Woolsey (MAYA)
42m 46s
There's nothing basic about the basics of leadership - Michael Lopp (Pinterest)
50m 46s
The importance of technical onboarding, training, and mentoring - Kate Heddleston (Kate Heddleston LLC)
32m 42s
Radical candor: Be a better boss - Kim Scott (Radical Candor, Inc.)
50m 26s
Accomplish big goals with objectives and key results - Christina Wodtke (Wodtke Consulting)
32m 35s
Hiring engineers shouldn't hurt - Erin Ptacek (Starfighters.io)
41m 14s
Scaling Teams - David Loftesness
35m 23s
Ask the CTO: Hard questions, honest answers - Camille Fournier (Formerly Rent the Runway), Michael Lopp (Pinterest)
48m 31s
How to eat change for breakfast: Building an experimental enterprise - Sanjay Mathur (Silicon Valley Data Science)
35m 51s
Analyzing billions of users with Druid and Theta Sketches - Eric Tschetter (Yahoo)
37m 22s
Grounding big data: A meta-imperative - Joe Hellerstein (UC Berkeley), Vikram Sreekanti (Berkeley AMP Lab)
41m 4s
Unified namespace and tiered storage in Alluxio - Calvin Jia (Alluxio), Jiri Simsa (Alluxio)
40m 13s
Building the data infrastructure of the future with persistent memory - Derrick Harris (Mesosphere), Rob Peglar (Micron Technology, Inc), Milind Bhandarkar (Ampool, Inc.), Anil Goel (SAP), Todd Lipcon (Cloudera, Inc.)
41m 43s
Just-in-time optimizing a database - Ted Dunning (MapR Technologies)
37m 15s
Putting Kafka into overdrive - Todd Palino (LinkedIn), Gwen Shapira (Confluent)
46m 35s
Streaming architecture: Why flow instead of state? - Ted Dunning (MapR Technologies)
41m 23s
Elasticsearch and Apache Lucene for Apache Spark and MLlib - Costin Leau (Elastic)
43m 8s
Deploying Hadoop on user namespace containers - Abin Shahab (Altiscale)
41m 10s
Netflix: Making big data small - Daniel Weeks (Netflix)
39m 43s
Lessons learned building a scalable self-serve, real-time, multitenant monitoring service at Yahoo - Sumeet Singh (Yahoo), Mridul Jain (Yahoo)
41m 36s
Data applications and infrastructure at Coursera - Roshan Sumbaly (Coursera Inc), Pierre Barthelemy (Coursera)
37m 16s
When one data center is not enough: Building large-scale stream infrastructure across multiple data centers with Apache Kafka - Guozhang Wang (Confluent)
27m 16s
Toppling the mainframe: Enterprise-grade streaming under 2 ms on Hadoop - Ilya Ganelin (Capital One Data Innovation Lab)
44m 15s
Architecting immediacy: The design of a high-performance, portable wrangling engine - Joe Hellerstein (UC Berkeley), Seshadri Mahalingam (Trifacta)
43m 47s
Building DistributedLog, a high-performance replicated log service - Sijie Guo (Twitter)
40m 13s
Architecting distributed systems for failure: How Druid guarantees data availability - Fangjin Yang (Imply)
35m 48s
Did you accidentally build a database? - Spencer Kimball (Cockroach Labs)
45m 9s
Secrets of natural language UIs: Translating English into computer actions - Joseph Turian (Workday), Alex Nisnevich (Bayes Impact)
38m 16s
Data wrangling and intro to pandas - Part 1 - T.J. Alumbaugh (Continuum Analytics), James Powell (NumFOCUS)
57m 37s
Data wrangling and intro to pandas - Part 2 - T.J. Alumbaugh (Continuum Analytics), James Powell (NumFOCUS)
54m 47s
Intro to data visualization with Bokeh - Part 1 - Bryan Van de Ven (Continuum Analytics), Sarah Bird (Aptivate)
1h 6m 12s
Intro to data visualization with Bokeh - Part 2 - Bryan Van de Ven (Continuum Analytics), Sarah Bird (Aptivate)
46m 22s
Intro to machine learning with scikit-learn - Part 1 - Jake Vanderplas (eScience Institute, University of Washington), Katrina Riehl (Continuum Analytics)
58m 36s
Intro to machine learning with scikit-learn - Part 2 - Jake Vanderplas (eScience Institute, University of Washington), Katrina Riehl (Continuum Analytics)
54m 49s
R quickstart: Transform and visualize data - Garrett Grolemund (RStudio, Inc.)
1h 7m 14s
Validating models in R - Part 1 - Nina Zumel (Win-Vector LLC), John Mount (Win Vector LLC)
49m 13s
Validating models in R - Part 2 - Nina Zumel (Win-Vector LLC), John Mount (Win Vector LLC)
38m 44s
Scaling R: Analytics for big data - Stephen Elston (Quantia Analytics, LLC)
1h 4m 29s
Reproducible reports with big data - Garrett Grolemund (RStudio, Inc.)
1h 2m 59s
A year of anomalies: Building shared infrastructure for anomaly detection - Chris Sanden (Netflix), Christopher Colburn (Netflix)
42m 1s
Augmenting machine learning with human computation for better personalization - Eric Colson (Stitch Fix)
47m 33s
Real-time fraud detection using process mining with Spark Streaming - Hylke Hendriksen (ING)
37m 15s
Building a marketplace: Eventbrite's approach to search and recommendation - John Berryman (Eventbrite)
42m 18s
Docker for data scientists - Michelangelo D'Agostino (Civis Analytics)
42m 49s
How to make analytic operations look more like DevOps: Lessons learned moving machine-learning algorithms to production environments - Robert Grossman (University of Chicago)
41m 29s
Analyzing time series data with Spark - Sandy Ryza (Cloudera)
31m 38s
Faster conclusions using in-memory columnar SQL and machine learning - Wes McKinney (Cloudera), Jacques Nadeau (Dremio)
47m 23s
Putting the “science” into data science: The importance of reproducibility and peer review for quantitative research - Erik Andrejko (The Climate Corporation)
38m 27s
Can deep neural networks save your neural network? Artificial intelligence, sensors, and strokes - Brandon Ballinger (Cardiogram), Johnson Hsieh (Cardiogram)
44m 30s
Deep learning and recurrent neural networks applied to electronic health records - Josh Patterson (Patterson Consulting), David Kale (University of Southern California), Zachary Lipton (University of California, San Diego)
45m 34s
Data science teams: Hold out for the unicorn or build bands of steeds? - Michael Dauber (Amplify), Yael Garten (LinkedIn), Monica Rogati (Data Natives), Daniel Tunkelang (Various)
43m 20s
How LinkedIn built a text analytics platform at scale - Chi-Yi Kuan (LinkedIn), Weidong Zhang (LinkedIn), Yongzheng Zhang (LinkedIn)
40m 10s
Python scalability: A convenient truth - Travis Oliphant (Continuum Analytics)
41m 28s
Data modeling for data science: Simplify your workload with complex types - Marcel Kornacker (Cloudera)
38m 15s
Atom smashing using machine learning at CERN - Siddha Ganju (Carnegie Mellon University)
37m 54s
Large-scale product classification via text and image-based signals using a fusion of discriminative and deep learning-based classifiers - Sreeni Iyer (quadanalytix), Anurag Bhardwaj (Quad Analytix)
49m 22s
Vowpal Wabbit: The essence of speed in machine learning - Jeroen Janssens (Tilburg University)
36m 0s
The polyglot Beaker notebook - Scott Draves (Two Sigma Open Source)
40m 26s
What's gone horribly wrong. . .and how you can protect yourself - Farrah Bostic (The Difference Engine), Paul Soldera (Equation Research)
43m 35s
The rise of the data selfie - Trina Chiasson (Tableau Software)
15m 38s
The future of data and culture - Leah Hunter (Tech Journalist), Amber Case (Esri), Todd Harple (Intel), Claire Michell (Temboo)
27m 35s
Big data sustainability: An environmental management systems analogy - Jonathan King (Ericsson)
24m 20s
Kosher collection: Best practices in data handling - Charles Givre (Booz | Allen | Hamilton)
18m 23s
Three rules every mobile product needs to follow to be successful - Sophie-Charlotte Moatti (Products That Count)
23m 16s
Mapping the matrix: Open cartography with scientific and spatial data - Aurelia Moser (Mozilla Science)
24m 23s
US EPA: A data-driven decision-making agency - Robin Thottungal (US Environmental Protection Agency)
16m 44s
My AlgorithmicMe: Our representation in data - Joerg Blumtritt (Datarella), Majken Sander (BusinessAnalyst.dk)
17m 40s
Stream science: Measuring the new currency of the music industry - Jonathan Gosier (AuDigent)
15m 56s
Making on-demand grocery delivery profitable with data science - Jeremy Stanley (Instacart)
21m 38s
Virtual reality for immersive data visualization - Bob Levy (Virtual Cove)
18m 32s
You have more data than you think. Time to put it to work - Jana Eggers (Nara Logics)
13m 51s
The power of personalization in the travel industry using big data - Sara Ahmadian (Seamless Planet)
6m 50s
How cognitive computing is changing data science for the better - Michael Ludden (IBM Watson)
23m 29s
Afraid of the future? You should be. Deep learning is eating your lunch—and mine. - Arno Candel (H2O.ai)
25m 33s
From drop to deluge: The upcoming wave of enterprise drone data - Keith Bigelow (3D Robotics)
25m 50s
Machine vision is making sense of the explosion of data from space - James Crawford (Orbital Insight)
30m 34s
Opportunities for hardware acceleration in data analytics - Kanu Gulati (Zetta Venture Partners)
25m 48s
Deploying deep learning at scale - Naveen Rao (Nervana)
29m 21s
Virtual reality in 2016 and in the future - Timoni West (Unity Labs)
25m 38s
Network intelligence at LinkedIn - Michael Conover (LinkedIn)
27m 51s
Data science 3.0: Empowering common end users with integrated solutions in a world of tools for engineers and scientists - Faisal Farooq (IBM Watson Health), Balaji Krishnapuram (IBM Watson Health)
27m 28s
Big science problems, big data solutions - Mr Prabhat (Berkeley Lab)
32m 21s
Of market makers and middlemen: How technology is transforming global trade - Renee DiResta (Haven)
31m 28s
Enabling smart consumer health decisions using prediction and personalization - Matt Butner (Stride Health)
31m 17s
Engineering industrial biology with data - Joshua Hoffman (Zymergen)
27m 40s
The business case for Spark, Kafka, and friends - Edd Dumbill (Silicon Valley Data Science)
30m 17s
Distributed systems in one lesson - Tim Berglund (DataStax)
25m 55s
How to use your data science team: Becoming a data-driven organization - Yael Garten (LinkedIn)
29m 23s
Cloud computing and big data - Ben Sharma (Zaloni)
26m 44s
Data visualizations decoded - Julie Rodriguez (Sapient Global Markets)
21m 16s
Developing a modern enterprise data strategy - Part 1 - Edd Dumbill (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
56m 19s
Developing a modern enterprise data strategy - Part 2 - Edd Dumbill (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
29m 47s
Developing a modern enterprise data strategy - Part 3 - Edd Dumbill (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
44m 28s
Developing a modern enterprise data strategy - Part 4 - Edd Dumbill (Silicon Valley Data Science), John Akred (Silicon Valley Data Science)
40m 25s
Empowering business users to lead with data - Denise McInerney (Intuit)
40m 53s
Why a data career is a great choice, now more than ever - Jin Zhang (CA Technologies), Jerry Overton (CSC), Michele Chambers (Continuum Analytics)
39m 44s
Automating decision making with big data: How to make it work - Andreas Schmidt (Blue Yonder)
36m 36s
Best practices for achieving customer 360 - Steven Totman (Cloudera), Nick Curcuru (MasterCard Advisors), Robert Bagley (ClickFox), Lori Bieda (Bank of Montreal)
44m 57s
Working on the blockchain gang: Crunching and visualizing bitcoin data - Benedikt Koehler (DataLion)
38m 58s
Adopting analytics: The Autodesk journey - Adam Sugano (Autodesk)
39m 11s
Inside Cigna's big data journey - Jeffrey Shmain (Cloudera), Mohammad Quraishi (Cigna)
41m 11s
Data scientists, you can help save lives - Jeremy Howard (Enlitic)
42m 18s
How big data is helping to save babies around the world - Linus Liang (Embrace), Brad Allen (Silicon Valley Data Science)
39m 11s
Publicly broadcasting data exhaust at a public broadcaster - Christopher Berry (Canadian Broadcasting Corporation)
30m 10s
Transforming Telefónica - John Belchamber (Telefónica), Arturo Canales (Telefónica)
39m 50s
Apache Hadoop operations for production systems - Part 1 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
40m 31s
Apache Hadoop operations for production systems - Part 2 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
48m 35s
Apache Hadoop operations for production systems - Part 3 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
36m 37s
Apache Hadoop operations for production systems - Part 4 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
33m 41s
Apache Hadoop operations for production systems: Troubleshooting - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
57m 24s
Apache Hadoop operations for production systems: Enterprise Considerations Part 1 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
33m 15s
Apache Hadoop operations for production systems: Enterprise Considerations Part 2 - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Darren Lo (Cloudera), Jordan Hambleton (Cloudera, Inc.)
47m 46s
Developing a big data business strategy - Bill Schmarzo (EMC)
39m 5s
How to build a successful data lake - Alex Gorelik (Waterline Data)
30m 18s
Bringing the Apache Hadoop ecosystem to the Google Cloud Platform - Jennifer Wu (Cloudera), James Malone (Google)
35m 19s
eBay analysts and governed self-service analysis: Delivering “turn-by-turn” smart suggestions - Debora Seys (eBay)
37m 22s
An introduction to Transamerica's product recommendation platform - Vishal Bamba (Transamerica), Nitin Prabhu (Transamerica)
34m 12s
Not your father's database: How to use Apache Spark properly in your big data architecture - Vida Ha (Databricks)
38m 20s
Amazon for information: Building a modern data catalog - Aaron Kalb (Alation)
35m 7s
10 concepts the enterprise decision maker needs to understand about Hadoop - Donald Miner (Miner & Kasch)
38m 11s
Old industries, sexy data: How machine learning is reshaping the world's backbone industries - David Beyer (Amplify Partners)
36m 19s
Best practices for enterprise adoption of big data in the cloud - Prat Moghe (Cazena)
47m 9s
Self-service, interactive analytics at multipetabyte scale in capital markets regulation on the cloud - Scott Donaldson (FINRA), Matt Cardillo (FINRA)
44m 3s
Netflix's big leap from Oracle to Cassandra - Roopa Tangirala (Netflix)
43m 22s
Strategies for agile instrumentation, ingestion, and analytics across many platforms and products - Yann Landrin (Autodesk), Charlie Crocker (Autodesk)
38m 10s
BI on Hadoop: What are your options? - Jacques Nadeau (Dremio)
40m 50s
Analyzing drivers of Net Promoter Score and their impact on customer engagement in the OTA industry - Krishnan Venkata (LatentView Analytics), Jose Abelenda (Hotwire)
42m 0s
Building a scalable, secure data platform: If I knew then what I know now - Bill Loconzolo (Intuit)
42m 36s
Hadoop application architectures: Fraud detection - Part 1 - Jonathan Seidman (Cloudera), Ted Malaska (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent)
42m 52s
Hadoop application architectures: Fraud detection - Part 2 - Jonathan Seidman (Cloudera), Ted Malaska (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent)
43m 53s
Hadoop application architectures: Fraud detection - Part 3 - Jonathan Seidman (Cloudera), Ted Malaska (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent)
53m 19s
Hadoop application architectures: Fraud detection - Part 4 - Jonathan Seidman (Cloudera), Ted Malaska (Cloudera), Mark Grover (Cloudera), Gwen Shapira (Confluent)
37m 21s
The next 10 years of Apache Hadoop - Ben Lorica (O'Reilly Media), Doug Cutting (Cloudera), Mike Cafarella (University of Michigan)
39m 20s
Hadoop's storage gap: Resolving transactional-access and analytic-performance tradeoffs with Apache Kudu (incubating) - Todd Lipon (Cloudera, Inc.)
42m 25s
Format wars: From VHS and Beta to Avro and Parquet - Silvia Oliveros (Silicon Valley Data Science), Stephen O'Sullivan (Silicon Valley Data Science)
40m 44s
Hadoop without borders: Building on-prem, cloud, and hybrid data flows - Hiren Shah (Microsoft), Anand Subbaraj (Microsoft)
44m 22s
Uber, your Hadoop has arrived: Powering intelligence for Uber’s real-time marketplace - Vinoth Chandar (Uber)
50m 15s
Hadoop in the cloud: Good fit or round peg in a square hole?
42m 20s
Successful enterprise data hub design patterns at BT - Phillip Radley (BT)
39m 48s
Subject-matter experts and access to rich data: A case study in protecting a network from the Brobot distributed denial of service attacks. - John Omernik (Secureworks)
39m 58s
Architecting HBase in the field - Jean-Marc Spaggiari (Cloudera), Kevin O'Dell (Rocana)
56m 1s
In search of database nirvana: The challenges of delivering HTAP - Rohit Jain (Esgyn)
39m 31s
Big data for telcos: A trio of use cases - Amy O'Connor (Cloudera)
55m 26s
Scalable schema management for Hadoop and Spark applications - Kelvin Chu (Uber), Evan Richards (Uber)
41m 35s
How the oil and gas industry is igniting a spark with information fusion and metadata analytics - Brian Clark (Objectivity), Marco Ippolito (CGG GeoSoftware)
37m 19s
High-performance clickstream analytics with Apache Phoenix and HBase - Arun Thangamani (CDK)
48m 40s
Lessons learned from building real-life machine-learning systems - Xavier Amatriain (Quora)
32m 6s
The how and why of feature engineering - Alice Zheng (Dato)
26m 22s
A scalable implementation of deep learning on Spark - Alexander Ulanov (Hewlett-Packard Labs)
26m 39s
BIDMach on Spark: Machine learning at the outer limits - John Canny (UC Berkeley)
34m 25s
Dynamic memory networks for visual and textual question answering - Stephen Merity (MetaMind)
25m 59s
Scalable ensemble learning with H2O - Erin Ledell (H2O.ai)
27m 14s
Detecting and scoring anomalies with calibrated probabilistic models - Alexander Gray (Skytree, Inc.)
27m 41s
Scalable collective reasoning in graphs - Lise Getoor (University of California, Santa Cruz)
29m 57s
Phase retrieval algorithms for gigapixel microscopy - Laura Waller (UC Berkeley)
29m 52s
TensorFlow: Machine learning for everyone - Rajat Monga (Google)
24m 54s
A deep dive into DeepDive - Mike Cafarella (University of Michigan)
27m 55s
An introduction to time series with Team Apache - Part 1 - Patrick McFadin (DataStax)
31m 12s
An introduction to time series with Team Apache - Part 2 - Patrick McFadin (DataStax)
30m 3s
An introduction to time series with Team Apache - Part 3 - Patrick McFadin (DataStax)
32m 18s
An introduction to time series with Team Apache - Part 4 - Patrick McFadin (DataStax)
44m 39s
Distributed stream processing with Apache Kafka - Jay Kreps (Confluent)
39m 54s
Real-time Hadoop: What an ideal messaging system should bring to Hadoop - Ted Dunning (MapR Technologies)
42m 16s
How to turn your house into a robot: An adaptive-learning algorithm for the Internet of Things - Brandon Rohrer (Microsoft)
42m 19s
IoT in the enterprise: A look at Intel (IoT) Inside - Moty Fania (Intel)
35m 55s
Fast data made easy with Apache Kafka and Apache Kudu (incubating) - Ted Malaska (Cloudera), Jeff Holoman (Cloudera)
38m 54s
Embeddable data transformation for real-time streams - Joey Echeverria (Rocana)
40m 26s
Twitter Heron at scale - Karthik Ramasamy (Twitter)
43m 47s
Transforming industrial enterprises with data science: From deterministic machines to probabilistic systems - Sean Murphy (PingThings)
39m 19s
Apache Flink: Streaming done right - Kostas Tzoumas (data Artisans)
35m 40s
Scaling your business with a messaging platform on the Zeta Architecture - Jim Scott (MapR Technologies, Inc.)
46m 24s
Pulsar: Real-time analytics at scale leveraging Kafka, Kylin, and Druid - Tony Ng (eBay, Inc.)
36m 38s
Overcoming the top 5 hurdles to real-time analytics - Pat McGarry (Ryft)
34m 5s

404

Not Found

Either this content does not exist or you do not have access to it. Try searching for something new.