Skip to Content conference Strata + Hadoop World 2016 - San Jose, California: Video Compilation April 2016
Beginner to intermediate
174h 16m
English
Closed Captioning available in German, English, Spanish, French, Japanese, Korean, Portuguese (Portugal, Brazil), Chinese (Simplified), Chinese (Traditional) Course outline 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 Science & Advanced Analytics 23h 28m
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
Data-driven Business 23h 10m
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
Enterprise Adoption 15h 20m
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 Internals & Development 4h 59m
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
Hardcore Data Science 5h 13m
Law, Ethics, Governance 2h 44m
Guest talk: Choosing an optimal storage backend for your Spark use case - Sameer Farooqui and Vida Ha (Databricks)14m 1s
Architecting a data platform - Part 1 - John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Silicon Valley Data Science), Gary Dusbabek (Silicon Valley Data Science)48m 2s
Architecting a data platform - Part 2 - John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Silicon Valley Data Science), Gary Dusbabek (Silicon Valley Data Science)46m 32s
Architecting a data platform - Part 3 - John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Silicon Valley Data Science), Gary Dusbabek (Silicon Valley Data Science)51m 38s
Architecting a data platform - Part 4 - John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Silicon Valley Data Science), Gary Dusbabek (Silicon Valley Data Science)21m 54s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Part 1 - Jayant Shekhar (Cloudera), Amandeep Khurana (Cloudera), Krishna Sankar (Volvo Cars), Vartika Singh (Cloudera)53m 3s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Part 2 - Jayant Shekhar (Cloudera), Amandeep Khurana (Cloudera), Krishna Sankar (Volvo Cars), Vartika Singh (Cloudera)49m 18s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Part 3 - Jayant Shekhar (Cloudera), Amandeep Khurana (Cloudera), Krishna Sankar (Volvo Cars), Vartika Singh (Cloudera)48m 52s
Building machine-learning apps with Spark: MLlib, ML Pipelines, and GraphX - Part 4 - Jayant Shekhar (Cloudera), Amandeep Khurana (Cloudera), Krishna Sankar (Volvo Cars), Vartika Singh (Cloudera)29m 2s
The state of Spark and where it is going in 2016 - Reynold Xin (Databricks)39m 2s
SparkNet: Training deep networks in Spark - Robert Nishihara (UC Berkeley)44m 51s
Fast big data analytics and machine learning using Alluxio and Spark in Baidu - Bin Fan (Alluxio), Haojun Wang (Baidu)27m 37s
Scala and the JVM as a big data platform: Lessons from Apache Spark - Dean Wampler (Lightbend)39m 44s
Designing a scalable real-time data platform using Akka, Spark Streaming, and Kafka - Alex Silva (Pluralsight)39m 32s
Testing and validating Spark programs - Holden Karau (IBM)37m 42s
Apache Spark and real-time analytics: From interactive queries to streaming - Michael Armbrust (Databricks)39m 24s
Taking Spark Streaming to the next level with DataFrames - Tathagata Das (Databricks)33m 33s
Breaking Spark: Top 5 mistakes to avoid when using Apache Spark in production - Neelesh Srinivas Salian (Cloudera)34m 3s
Cancer genomics analysis in the cloud with Spark and ADAM - Timothy Danford (Tamr, Inc.)43m 2s
Dash forward: From descriptive to predictive analytics with Apache Spark + end-user feature with Kellogg's JR Cahill - Eric Frenkiel (MemSQL), JR Cahill (Kellogg)35m 19s
Globally distributed hybrid on-premises/cloud big data - Jagane Sundar (WANdisco)33m 2s
Building a scalable data science platform with R - Mario Inchiosa (Microsoft), Roni Burd (Microsoft)39m 44s
How Siemens handles complexity in streaming data from millions of sensors - Yvonne Quacken (Siemens), Allen Hoem (Teradata)38m 14s
The Internet of Things: How to do it. Seriously! - Chris Rawles (Pivotal)33m 40s
Tame that beast: How to bring operations, governance, and reliability to Hadoop - Keith Manthey (EMC)32m 8s
How GE created a pervasive culture of data-driven insights at scale - Don Perigo (GE Power)34m 2s
Transactional streaming: If you can compute it, you can probably stream it - John Hugg (VoltDB)35m 42s
Can you afford to drop ACID? Understanding real-world SQL requirements in the big data era - Emma McGrattan (Actian)41m 7s
From X-ray to MRI: New insights on data about data - Dave Wells (Paxata), Nenshad Bardoliwalla (Paxata), Travis Ringger (PwC), Conrad Mulcahy (K2 Intelligence)40m 25s
Creating intelligence: An applications-first approach to machine learning - Carlos Guestrin (Dato Inc.)37m 18s
The emerging data imperative - Wei Wang (Hortonworks), Scott Gnau (Hortonworks)36m 21s
Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks - Grega Kespret (Celtra Inc.) and Matthew J. Glickman (Snowflake)39m 40s
How TD Bank is using Hadoop to create IT 3.0 and launch the next-generation bank - Mok Choe (TD Bank Group ), Paul Barth (Podium Data)40m 50s
What it takes to develop enterprise-grade Hadoop SQL Analytics - Bob Hansen (HPE)26m 29s
A survival guide for machine learning: Top 10 tips from a battle-tested solution - Patrick Hall (SAS), Paul Kent (SAS)37m 48s
Moving beyond the enterprise: Data sharing as the next big idea - Sandy Steier (1010data), Dennis Gleeson (1010data)30m 28s
Containers: The natural platform for data applications - Partha Seetala (Robin Systems)19m 41s
Remedying the accounts receivable reporting gap for a large multinational imaging and electronics company using a Hadoop-based open source platform - Ganesan Pandurangan (Infosys Limited)13m 8s
How we Hadoop: Inmar’s transformation from a business-services outsourcing company to a data-driven enterprise - Kevin Goode (Inmar)28m 1s
High-frequency decisioning - Steve Wooledge (MapR Technologies)39m 27s
Master the Internet of Things with integrated analytics - Bob Rogers & Bridget Karlin (Intel)39m 34s
Automated model selection and tuning at scale with Spark - Peter Prettenhofer (DataRobot), Owen Zhang (DataRobot)45m 2s
Building a modern data architecture - Ben Sharma (Zaloni)40m 6s
Solr as a SparkSQL datasource - Timothy Potter (Lucidworks)40m 9s
Delivering "DARPA hard" - Matthew Van Adelsberg (CACI)36m 20s
Big data-fueled feedback loops leveraging streaming data in SDN/NFV - Matt Olson (CenturyLink)43m 1s
TensorFlow: Large-scale analytics and distributed machine learning with TensorFlow, BigQuery, and Dataflow (Apache Beam) - Kazunori Sato (Google), Amy Unruh (Google)38m 32s
Virtualizing big data: Effective approaches from real-world deployments - Martin Yip (VMware), Justin Murray (VMware)46m 36s
Turn big data into big results - Jeff Pohlmann (Oracle)37m 51s
Transforming core business operations with SAP HANA Vora on Hadoop and Apache Spark - Amit Satoor (SAP), Balalji Krishna (SAP)44m 34s
Batch is back: Critical for agile application adoption - Joe Goldberg (BMC Software Inc.)38m 19s
Visualization & User Experience 8h
Ask me anything: Apache Hadoop operations for production systems - Kathleen Ting (Cloudera), Vikram Srivastava (Cloudera, Inc.), Jordan Hambleton (Cloudera, Inc.)41m 28s
Ask me anything: Hadoop application architectures - Mark Grover (Cloudera), Jonathan Seidman (Cloudera), Ted Malaska (Cloudera), Gwen Shapira37m 12s
Ask me anything: Apache Spark - Reynold Xin (Databricks), Tathagata Das (Databricks), Michael Armbrust (Databricks)38m 13s
Ask me anything: Developing a modern enterprise data strategy - John Akred (Silicon Valley Data Science), Colette Glaeser (Silicon Valley Data Science)40m 59s
Solutions Showcase Theater 6h 27m
Empowering Self-Service Data Science at Autodesk - Daniel Rose (Qubole)7m 55s
Taking the Complexity Out of Big Data Visualization - Priyank Patel (Arcadia Data)11m 19s
When Results Matter - Darin Jones (CACI)8m 12s
R.E.A.L. Big Data -- Now with 1TB for Free in Every Box - Joey Echeverria (Rocana)10m 9s
Is Your Data Ready for In-Memory Analytics? Think Again. - Juthika Khargharia, Ph.D. (SAS)11m 41s
Managing the Data Lake: Creating Actionable Insights and Value - Suntosh Murthy (Zaloni)9m 9s
Data Marshalling: An approach to Optimizing Your Data Lake - Jennifer Reed (Novetta)10m 12s
Enterprise Transformation with Solix big data suite: data lake, analytics and archiving use cases - Vikram Gaitonde (Solix)11m 6s
How Big Data and IoT Are Helping to Feed the World - Ashley Stirrup (Talend)10m 5s
What you need to know about addressing Data Quality within Hadoop - Scott Arnett (Pitney Bowes)10m 31s
Streaming Analytics - Sean Baseman (FICO)10m 10s
Growing the sharing economy, by sharing data - Jeremy Sokolic (SiSense)8m 0s
Advanced Cyber Threat Detection with Securonix Snyper - Tanuj Gulati (Securonix)11m 1s
AI meets Cyber Security... - Greg Martin (Jask)10m 11s
Leveraging the power of automation to enable the creation of more accurate, easy to use machine-learning models in less time - Alexander Gray (Skytree)12m 18s
Data Deluge in Digital Advertising - Teddy Rusli (DataTorrent)8m 11s
Make Big Data and Enterprise Data Work Together in Retail, Healthcare, and Agriculture - Karen Sun (SAP)8m 18s
Network Reconnaissance Solution - Paul Hahn (Cray)7m 42s
Real Time Fraud Detection using IBM z/OS Platform for Apache Spark - Mythili Venkatakrishnan & Sreeram Nudurupati (DataFactz)9m 14s
Why BI tools fail the Hadoop test, and how to become the BI Hadoop hero - Eric Sit (Quotient)5m 58s
Unravel the mystery of why your big data applications are slow to deliver business value - Kunal Agarwal, (Unravel) and Jeff Magnusson (Stitchfix)10m 28s
Big Data, Behavioral Analysis, and Sears - Denise Hemke (Platfora)11m 12s
Delivering Advanced Analytics Capabilities in Banking - Rohit Balasubramanian (Deloitte)11m 2s
Using Behavior Analytics on Big Data to Drive New Revenue - John Morrell (Datameer)11m 46s
In-Memory Data Fabrics for Screaming Fast Big Data - Nikita Ivanov (GridGain)8m 5s
Cloud driving innovation - Goutham Belliappa and Keith Reid (Capgemini)9m 50s
Data, Insights, to Action: When Transactions and Analytics Converge - Ali Hodroj (GigaSpaces)10m 10s
Learn how you can solve big data operations problems through intelligent management - Shivnath Babu (Unravel), Charlie Cocker (Autodesk)7m 32s
SQL based in-database analytics on Hadoop - Raman Rajasekhar (Fuzzy Logix)10m 48s
Manulife + Microsoft + KPMG solving big data problems for the insurance industry - Nate Shea-Han (Microsoft)10m 59s
Lookalike audience among billion devices - Xiatian Zhang (TalkingData)10m 12s
Connecting SAP HANA and Apache Impala (incubating) - Sunita Sharma and Sreedhar Bolneni (Cloudera)8m 8s
Gaining Customer Satisfaction Insights using IBM z/OS Platform for Apache Spark - Mythili Venkatakrishnan (IBM) and Sreeram Nudurupati (DataFactZ)8m 48s
Lambda-B-Gone: Better Answers for Less Money - John Hugg (VoltDB)11m 8s
Leveraging the power of Hadoop to parallelize Real-Time PCR computations and enable acceleration of genetic discovery - Salil Kumar (Hadoolytics Inc.)10m 13s
Growing a community around the Trusted Analytics Platform - Chuck Freedman (Intel)10m 31s
Data Prep and Quality Together – How to Uncover the Truth About Your Customers - Mark Pierce (Trillium)8m 17s
Make Your Data Strategy Work Through Streaming - Steve Wilkes (Striim)10m 16s
Enterprise Data Lake Power Tips - Paul Barth (Podium Data)10m 20s
Advanced Threat Detection on Streaming Data - Carol McDonald (MapR)6m 52s
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