Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: Sponsored sessions

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11:20am12:00pm Wednesday, September 27, 2017
Location: 1A 03
Brandon Bunker (Vivint)
Average rating: ****.
(4.00, 1 rating)
Brandon Bunker explains how Vivint delivers fast analytics from big data on a bootstrap budget by leveraging Tableau as a strategic piece of its modern BI architecture. By interactively analyzing data as it lands in its Cloudera Hadoop data lake, Vivint is able to deliver security across homes and data alike, making smart homes even smarter and saving customers money in the process. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1A 01/02
William Merchan (DataScience.com)
Average rating: *****
(5.00, 2 ratings)
The number of inefficiencies in the data science workflow is staggering. Data science platforms have emerged to combat these inefficiencies. William Merchan outlines the key components of a data science platform and demonstrates how these platforms are enabling organizations to realize the potential of their data science teams. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1E 06
Kevin Huiskes and Radhika Rangarajan discuss Intel's strategy to lower barriers to advanced analytics and AI, make results faster and more efficient, and enable data scientists and developers to make better use of existing infrastructure, emphasizing solutions based on the latest Intel Xeon Scalable platform and the open source framework BigDL. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1E 17
Han Yang (Cisco Systems)
For many enterprises, the internet of things represents an opportunity to transform the business by examining its data from a holistic lifecycle perspective and generating, analyzing, and archiving the data to reengineer the enterprise. Han Yang explores the latest trends and the role of infrastructure in enabling such a transformation. Read more.
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11:20am12:00pm Wednesday, September 27, 2017
Location: 1A 04/05
David Mellor (Curriculum Associates)
Curriculum Associates has a mission to make classrooms better places for teachers and students. To achieve this, the company introduces innovative and exciting new products that give every student the chance to succeed. David Mellor explains how Curriculum Associates developed a real-time data pipeline with MemSQL, which empowered teachers to provide immediate and accurate student feedback. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1A 03
Bala Chandrasekaran (Barclays)
Average rating: ****.
(4.00, 1 rating)
Barclays and Dell EMC have partnered on the deployment of a solution called the Elastic Data Platform. Ankit Tharwani offers an overview of this platform, which gives data scientists the ability to self-serve sandbox environments, cutting down the time to provision environments from months to hours. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1E 06
Deepak Majeti (Vertica)
Deepak Majeti explains why the separation of compute and storage has become critical to maximizing the benefits of cloud economics. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1E 17
Todd Mostak (MapD)
For all of the innovation occurring across the GPU software ecosystem, the platforms themselves still remain isolated from each other—until now. Todd Mostak debuts the GPU Open Analytics Initiative’s first project, the GPU Data Frame (GDF), and explains how GDF enables efficient intra-GPU communication between different processes running on the GPUs. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1A 04/05
Jack Norris (MapR Technologies)
Average rating: ****.
(4.00, 1 rating)
Jack Norris shares lessons learned by leading companies leveraging data to transform customer experiences, operational results, and overall growth and details the infrastructure, development, and data management principles used by successful leaders to drive agility regardless of application volume or scale. Read more.
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1:15pm1:55pm Wednesday, September 27, 2017
Location: 1A 01/02
Average rating: *****
(5.00, 2 ratings)
How does your favorite website serve up the perfect content just for you? It's all based on machine learning. By continuously adjusting machine learning models based on real-time data, you can visualize changes and take action on the new information in real time. Juthika Khargharia explains how to build a recommendation engine to surface these recommendations on real-time data. Read more.
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2:05pm2:45pm Wednesday, September 27, 2017
Location: 1E 06
Ben Sharma (Zaloni), Carlos Matos (AIG)
Average rating: ****.
(4.00, 2 ratings)
Envision the next phase of your company’s data future: providing centralized data services for streamlined yet controlled access to data for end users across lines of business. Carlos Matos and Ben Sharma share strategies for developing an enterprise-wide data lake service to drive shared data insights across the organization. Are you ready? Read more.
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2:05pm2:45pm Wednesday, September 27, 2017
Location: 1A 01/02
Michelle Tower (Procter & Gamble)
Average rating: ****.
(4.00, 1 rating)
The early stages of delivering on your data strategies are daunting. With many claims of failed data lakes or “data swamps,” the journey seems risky, which is why you need help from industry experts to get going. Michelle Tower explains how P&G is using big data, Apache Hadoop, and visual analytics to quickly discover new insights and optimize data models for analytics and data visualization. Read more.
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2:05pm2:45pm Wednesday, September 27, 2017
Location: 1A 04/05
Santhosh Mahendiran (Standard Chartered Bank)
Santhosh Mahendiran explains how financial services company Standard Chartered Bank is using self-service data prep and machine learning technologies to democratize its data lake, offering trusted information to analysts, subject-matter experts, and line-of-business executives across 70 countries to help monitor fraud, track money-laundering activities, and perform regulatory compliance reporting. Read more.
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2:05pm2:45pm Wednesday, September 27, 2017
Location: 1E 17
Ben Szekely (Cambridge Semantics)
Average rating: *****
(5.00, 1 rating)
Only with a rich and interactive semantic layer can the data and analytics stack deliver true on-demand access to data, answers, and insights, weaving data together from across the enterprise into an information fabric. Ben Szekely shares the capabilities of the newly launched Anzo Smart Data Lake 4.0, the only end-to-end platform for semantic layers based on open standards. Read more.
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2:05pm2:45pm Wednesday, September 27, 2017
Location: 1A 03
Kenneth Sanford (Dataiku)
Fragmented data science and analytics teams result in duplicate work, poor collaboration, a lack of governance, insufficient adoption at scale, and significant key-man risk. Kenneth Sanford explains how to overcome these challenges and build a centralized analytics practice that empowers data-driven decision making. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1A 04/05
Murthy Mathiprakasam (Informatica), Sravan Kasarla (Fidelity Investments)
In the face of regulatory and competitive pressures, why not use artificial intelligence, along with smart best practices, to manage data lakes? Murthy Mathiprakasam shares a comprehensive approach to data lake management that ensures that you can quickly and flexibly ingest, cleanse, master, govern, secure, and deliver all types of data in the cloud or on-premises. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1E 17
Luke Han (Kyligence)
Luke Han offers an overview of Apache Kylin and its enterprise version KAP and shares a case study of how a top finance company migrated to Apache Kylin on top of Hadoop from its legacy Cognos and DB2 system. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1A 01/02
Ells Campbell (CDC), Connor Carreras (Trifacta), Ryan Weil (Leidos)
Average rating: *****
(5.00, 1 rating)
Ells Campbell, Connor Carreras, and Ryan Weil explain how the Microbial Transmission Network Team (MTNT) at the Centers for Disease Control (CDC) is leveraging new techniques in data collection, preparation, and visualization to advance the understanding of the spread of HIV/AIDS. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1A 03
Chuck Yarbrough (Pentaho)
Average rating: **...
(2.00, 2 ratings)
The IoT can deliver real outcomes that can transform organizations—and societies—for the better. But the IoT is not transformative without the power of big data. Chuck Yarbrough shares examples of where the IoT and big data have combined to solve significant business challenges and take advantage of business opportunities. Read more.
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2:55pm3:35pm Wednesday, September 27, 2017
Location: 1E 06
Average rating: **...
(2.00, 2 ratings)
Evolving big data architectures are creating an increasingly complex landscape. Michelle Mensing explains how to simplify data orchestration across various big data and enterprise sources, demonstrating how to create a complex pipeline and execute the pipeline in Kubernetes clusters, covering data acquisition, transformation, cleaning data, and running the algorithms. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1A 04/05
Jonathan Gray (Cask)
Average rating: **...
(2.50, 2 ratings)
To take advantage of the latest big data technology options in the cloud, more and more enterprises are building hybrid, self-service data lakes. Jonathan Gray discusses the importance of a portability strategy, addresses implementation challenges, and shares customer use cases that will inspire enterprises to embark on a multi-environment data lake journey. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1E 17
George Corugedo (RedPoint Global)
Driving digital transformation is a vital component of continued organizational success and more personalized customer engagement. The best results will come from operationalizing data to automate decisions with machine learning. George Corugedo explains how RedPoint’s customers use connected enterprise data, machine learning, and analytics to impact their businesses. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1E 06
Peter Wang (Anaconda)
Average rating: ****.
(4.00, 1 rating)
Peter Wang explores the typical problems data science teams experience when working with other teams and explains how these issues can be overcome through cohesive collaborative efforts among data scientists, business analysts, IT teams, and more. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1A 01/02
Phil Sewell (Micro Focus)
Phil Sewell discusses standards, options, and use cases for extracting value and delivering business outcomes from data protected at the data level. Read more.
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4:35pm5:15pm Wednesday, September 27, 2017
Location: 1A 03
Mate' Radalj (Kinetica)
Infusing business apps with AI isn’t easy. Mate Radalj explains why you need to master the entire AI process from data to models to operationalization so you can build, train, and deploy predictive models that unleash smart business apps and enable data-driven decisions.   Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1A 01/02
Rick Okin (JW Player)
Rick Okin explains how JW Player strategically leverages video data analytics to power industry- and customer-level insights for the evolving online video space. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1A 03
Tim McKenzie (Pitney Bowes)
Organizations need to have a data strategy that includes the tools to derive location intelligence, enhance existing data with geographic enrichment (geoenrichment), and perform location analytics to reveal strategic and operational insights. Tim McKenzie shares new data quality and location intelligence approaches that operate natively within Hadoop and Spark environments. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1A 04/05
Kevin Stallings provides an inside look at how AIG executed a technological and cultural transformation that had a powerful impact on business outcomes and bottom-line results and explains how to use these lessons to put enterprise-wide big data preparation and self-service analysis to great use within your organization and dramatically increase customer satisfaction and engagement. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1E 06
Ben Snively (Amazon Web Services (AWS))
Average rating: ***..
(3.00, 3 ratings)
How do you incorporate serverless concepts and technologies into your big data architectures? Ben Snively shares use cases, best practices, and a reference architecture to help you streamline data processing and improve analytics through a combination of cloud and open source serverless technologies. Read more.
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5:25pm6:05pm Wednesday, September 27, 2017
Location: 1E 17
Piet Loubser (Hortonworks)
Data has become the new fuel for business success. As a result, business intelligence and analytics are among the top priorities for CIOs today. Piet Loubser outlines the tectonic shift currently taking place in the market and explains why next-gen connected architectures are crucial to meet the demands of an intelligent, connected world. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1A 03
Jim McHugh (NVIDIA), Todd Mostak (MapD), Srisatish Ambati (0xdata Inc), Stanley Seibert (Anaconda)
Average rating: ***..
(3.50, 2 ratings)
Joining Jim McHugh are founders of GOAI: - Todd Mostak, CEO of MapD - SriSatish Ambati, CEO and co-founder of H2O - Stan Seibert, Director of Community Innovation, Anaconda In this session, the speakers will provide an update on the latest advancement and customer use cases leveraging GOAI Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1A 01/02
Matt Winkler (Microsoft)
Matt Winkler shares real-world case studies on how healthcare, agriculture, and manufacturing companies are creating, training, deploying, and managing AI models faster with Microsoft Azure and deploying them to the cloud, on-premises, and to the edge. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1A 04/05
Chad W. Jennings (Google), Eric Schmidt (Google)
Average rating: *****
(5.00, 2 ratings)
Doing “algebra” with emotions can lead to new insights about customer behavior. Chad Jennings presents a serverless big data analytics platform that allows you to capture and analyze raw data and train machine learning models that can process text to discern not just the sentiment but also the underlying emotion driving that sentiment. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1E 06
Ivan Jibaja (Pure Storage)
Ivan Jibaja explains offers an overview of Pure Storage's streaming big data analytics pipeline, which uses open source technologies like Spark and Kafka to process over 30 billion events per day and provide real-time feedback in under five seconds. Read more.
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11:20am12:00pm Thursday, September 28, 2017
Location: 1E 17
Alex Gutow (Cloudera), David Harsh (Microstrategy)
Alex Gutow discusses the importance of adaptive analytics and shares everything you need to know while transitioning from legacy data warehouses to Hadoop-based platforms. Join in to find out why you need modern platforms to move, host, and analyze your data with MicroStrategy and Cloudera. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1A 03
Jagane Sundar (WANdisco), Pranav Rastogi (Microsoft)
Jagane Sundar and Pranav Rastogi explain how to meet your enterprise SLAs while making full use of resources with patented active data replication technology—something computer science still says is impossible. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1A 04/05
Average rating: *....
(1.00, 1 rating)
When analytics applications become business critical, balancing cost with SLAs for performance, backup, dev, test, and recovery is difficult. Karthikeyan Nagalingam discusses big data architectural challenges and how to address them and explains how to create a cost-optimized solution for the rapid deployment of business-critical applications that meet corporate SLAs today and into the future. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1A 01/02
A changing market landscape and open source innovations are having a dramatic impact on the consumability and ease of use of data science tools. Carlo Appugliese examines the impact these trends and changes will have on the future of data science and how machine learning is making data science available to all. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1E 17
NISHA TALAGALA (ParallelM)
Deploying ML in production is challenging. Nisha Talagala shares solutions and techniques for effectively managing machine learning and deep learning in production with popular analytic engines such as Apache Spark, TensorFlow, and Apache Flink. Read more.
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1:15pm1:55pm Thursday, September 28, 2017
Location: 1E 06
Ramesh Menon (Infoworks)
Enterprises want to implement analytics use cases at the speed of business yet spend more time on complicated data management than on creating business value. The solution is automation. Ramesh Menon explains how a large enterprise automated data ingestion, data synchronization, and the building of data models and cubes to create a big data warehouse for the rapid deployment of analytics. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1E 06
Basil Faruqui (BMC Software), Jon Ouimet (BMC Software)
Are you building, running, or managing complex data pipelines across hybrid environments spanning multiple applications and data sources? Doing this successfully requires automating dataflows across the entire pipeline, ideally controlled through a single source. Basil Faruqui and Jon Ouimet walk you through a customer journey to automate data pipelines across a hybrid environment. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1E 17
Bob Patterson (Hewlett Packard Enterprise (HPE))
Bob Patterson offers an overview of Hewlett Packard Enterprise's enterprise-grade Hadoop solution, which has everything you need to accelerate your big data journey: innovative hardware architectures for diverse workloads certified for all leading distros, infrastructure software, services from HPE and partners, and add-ons like object storage. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1A 03
Keith Kohl (Syncsort)
If users get conflicting analytics results, wild predictions, and crazy reports from the data in your data lake, they will lose trust. From the beginning of your data lake project, you need to build in solid business rules, data quality checking, and enhancement. Keith Kohl shares an actionable checklist that shows everyone in your enterprise that your big data can be trusted. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1A 04/05
Much is being written about the economy of everything, but where does the analytics economy fit in? Fiona McNeill shares SAS's vision and roadmap for meeting the unique challenges of the analytics economy, including thoughts on intersections with related technologies like machine learning, deep learning, cognitive computing, and more. Read more.
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2:05pm2:45pm Thursday, September 28, 2017
Location: 1A 01/02
John Morrell (Datameer)
Average rating: *****
(5.00, 1 rating)
While companies have flooded data lakes with billions of records, the technical limitations of Hadoop have kept analysts from interactively exploring this data and delivering real value—until now. John Morrell explores a solution helping analysts interactively and rapidly explore billions of records in Hadoop, offering a truly interactive experience and ushering in the era of Data Lake 2.0. Read more.