Sep 23–26, 2019
 
1A 12/14
Add Efficient ML engineering: Tools and best practices to your personal schedule
9:00am Tutorial Efficient ML engineering: Tools and best practices Sourav Dey (Manifold), Jakov Kucan (Manifold)
Add Apache Metron: Open source cybersecurity at scale to your personal schedule
1:30pm Tutorial Apache Metron: Open source cybersecurity at scale Carolyn Duby (Cloudera)
1A 15/16
Add Hands-on data science with Python (Day 2) to your personal schedule
9:00am 2-day Training Hands-on data science with Python (Day 2) Michael Cullan (The Data Incubator)
1A 21
Add Building a recommender system with Amazon ML services to your personal schedule
1:30pm Tutorial Building a recommender system with Amazon ML services Karthik Sonti (Amazon Web Services), Emily Webber (Amazon Web Services), Varun Rao Bhamidimarri (Amazon Web Services)
1A 23/24
Add Introduction to natural language processing in Python to your personal schedule
9:00am Tutorial Introduction to natural language processing in Python Alice Zhao (Metis)
Add Natural language understanding at scale with Spark NLP to your personal schedule
1:30pm Tutorial Natural language understanding at scale with Spark NLP David Talby (Pacific AI), Alex Thomas (John Snow Labs), Saif Addin Ellafi (John Snow Labs), Claudiu Branzan (Accenture)
1E 09
Add Serverless streaming architectures and algorithms for the enterprise to your personal schedule
9:00am Tutorial Serverless streaming architectures and algorithms for the enterprise Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio), Anurag Khandelwal (RISELab, UC Berkeley)
Add From relational databases to cloud databases: Using the right tool for the right job to your personal schedule
1:30pm Tutorial From relational databases to cloud databases: Using the right tool for the right job Gowrishankar Balasubramanian (Amazon Web Services), Rajeev Srinivasan (Amazon Web Services)
1E 12/13
Add Deep learning from scratch to your personal schedule
9:00am Tutorial Deep learning from scratch Bruno Goncalves (Data For Science, Inc)
Add Architecting a data platform for enterprise use to your personal schedule
1:30pm Tutorial Architecting a data platform for enterprise use Mark Madsen (Teradata), Todd Walter (Teradata)
1E 14
Add Running multidisciplinary big data workloads in the cloud with CDP to your personal schedule
9:00am Tutorial Running multidisciplinary big data workloads in the cloud with CDP James Morantus (Cloudera), Tony Huinker (Cloudera)
Add Kafka and Streams Messaging Manager (SMM) crash course to your personal schedule
1:30pm Tutorial Kafka and Streams Messaging Manager (SMM) crash course Purnima Reddy Kuchikulla (Cloudera), Dan Chaffelson (Cloudera)
1A 01/02
Add Big data for managers (Day 2) to your personal schedule
9:00am 2-day Training Big data for managers (Day 2) Michael Li (The Data Incubator), Gonzalo Diaz (The Data Incubator)
1A 03
Add Recommendation systems using deep learning (Day 2) to your personal schedule
9:00am 2-day Training Recommendation systems using deep learning (Day 2) Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
1A 04/05
1E 06
Add Professional Kafka development (Day 2) to your personal schedule
9:00am 2-day Training Professional Kafka development (Day 2) Jesse Anderson (Big Data Institute)
1E 15/16
Add Getting ready for CCPA: Securing data lakes for heavy privacy regulation to your personal schedule
9:00am Tutorial Getting ready for CCPA: Securing data lakes for heavy privacy regulation Mark Donsky (Okera), Lars George (Okera), Michael Ernest (Dataiku), Ifigeneia Derekli (Cloudera)
Add Hands-on machine learning with Kafka-based streaming pipelines to your personal schedule
1:30pm Tutorial Hands-on machine learning with Kafka-based streaming pipelines Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
1A 17
Add Building a serverless big data application on AWS (Day 2) to your personal schedule
9:00am 2-day Training Building a serverless big data application on AWS (Day 2) Jorge Lopez (Amazon Web Services)
1A 18
1E 07
Add Machine learning from scratch in TensorFlow (Day 2) to your personal schedule
9:00am 2-day Training Machine learning from scratch in TensorFlow (Day 2) Dylan Bargteil (The Data Incubator)
1A 06
Add Data Case Studies to your personal schedule
9:00am Tutorial Data Case Studies David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Rosaria Silipo (KNIME), Leah Xu (Spotify), Arup Nanda (Capital One), Victoriya Kalmanovich (Navy), Tusharadri Mukherjee (Lenovo), David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten), Martin Mendez-Costabel (Bayer Crop Science), gloria macia (Roche AG), Gwen Campbell (Revibe Technologies), Moise Convolbo (Rakuten), Muhammed Idris (Capria VC | TeraCrunch)
1A 07
Add Machine learning for the enterprise (sponsored by IBM) to your personal schedule
9:00am Day-Long Training Machine learning for the enterprise (sponsored by IBM) Matt Kirk (YourChiefScientist.com)
1A 08
Add Findata Day to your personal schedule
9:00am Tutorial Findata Day Alistair Croll (Solve For Interesting), Jennifer Yang (Wells Fargo ECS), Nitzan Mekel-Bobrov (Capital One), Brian Lynch (TD Bank Group), Dan Barker (RSA Security), Rochelle March (Trucost), Catherine Gu (Stanford University), Karan Jaswal (Cinchy), Moto Tohda (Tokyo Century (USA)), Mikheil Nadareishvili (TBC Bank), Viridiana Lourdes (Ayasdi), Peter Swartz (Altana Trade)
1A 10
Add Building and leading a successful AI practice for your organization  to your personal schedule
9:00am Tutorial Building and leading a successful AI practice for your organization Rossella Blatt Vital (Wonderlic), Ross Piper (Wonderlic), Daniel Schmerling (Wonderlic)
Add Managing data science in the enterprise to your personal schedule
1:30pm Tutorial Managing data science in the enterprise Alexander Izydorczyk (Coatue Managment), Benjamin Singleton (JetBlue), Joshua Poduska (Domino Data Lab)
1E 08
Add Learning Presto: SQL on anything to your personal schedule
9:00am Tutorial Learning Presto: SQL on anything Matt Fuller (Starburst)
Add Deep learning methods for natural language processing to your personal schedule
1:30pm Tutorial Deep learning methods for natural language processing Garrett Hoffman (StockTwits)
1E 10
Add Foundations for successful data projects to your personal schedule
1:30pm Tutorial Foundations for successful data projects Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
1E 11
Add Cloudera Edge Management in the IoT to your personal schedule
9:00am Tutorial Cloudera Edge Management in the IoT Purnima Reddy Kuchikulla (Cloudera), Timothy Spann (Cloudera), Abdelkrim Hadjidj (Cloudera), Andre Araujo (Cloudera)
Add Sketching data and other magic tricks to your personal schedule
1:30pm Tutorial Sketching data and other magic tricks Sophie Watson (Red Hat), William Benton (Red Hat)
Add Opening Reception to your personal schedule
5:00pm Opening Reception | Room: Expo Hall - 3B
12:30pm Lunch | Room: Lunch
10:30am Morning break sponsored by Microsoft | Room: Break
3:00pm Afternoon break sponsored by Dataiku | Room: Break
9:00am-12:30pm (3h 30m) Data Science, Machine Learning, & AI Culture and Organization, Model Development, Governance, Operations
Efficient ML engineering: Tools and best practices
Sourav Dey (Manifold), Jakov Kucan (Manifold)
Sourav Dey and Jakov Kucan walk you through the six steps of the Lean AI process and explain how it helps your ML engineers work as an an integrated part of your development and production teams. You'll get a hands-on example using real-world data, so you can get up and running with Docker and Orbyter and see firsthand how streamlined they can make your workflow.
1:30pm-5:00pm (3h 30m) Security and Privacy Privacy and Security
Apache Metron: Open source cybersecurity at scale
Carolyn Duby (Cloudera)
Bring your laptop, roll up your sleeves, and get ready to crunch some cybersecurity events with Apache Metron, an open source big data cybersecurity platform. Carolyn Duby walks you through how Metron finds actionable events in real time.
9:00am-5:00pm (8h) Data Science, Machine Learning, & AI
Hands-on data science with Python (Day 2)
Michael Cullan (The Data Incubator)
Michael Cullan walks you through developing a machine learning pipeline from prototyping to production. You'll learn about data cleaning, feature engineering, model building and evaluation, and deployment and then extend these models into two applications from real-world datasets. All work will be done in Python.
9:00am-12:30pm (3h 30m) Data Science, Machine Learning, & AI Model Development, Governance, Operations
SOLD OUT: Managing the complete machine learning lifecycle with MLflow
Jules Damji (Databricks)
ML development brings many new complexities beyond the software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information. Jules Damji walks you through MLflow, an open source project that simplifies the entire ML lifecycle, to solve this problem.
1:30pm-5:00pm (3h 30m) Data Science, Machine Learning, & AI Cloud Platforms and SaaS, Deep dive into specific tools, platforms, or frameworks
Building a recommender system with Amazon ML services
Karthik Sonti (Amazon Web Services), Emily Webber (Amazon Web Services), Varun Rao Bhamidimarri (Amazon Web Services)
Karthik Sonti, Emily Webber, and Varun Rao Bhamidimarri introduce you to the Amazon SageMaker machine learning platform and provide a high-level discussion of recommender systems. You'll dig into different machine learning approaches for recommender systems, including common methods such as matrix factorization as well as newer embedding approaches.
9:00am-12:30pm (3h 30m) Data Science, Machine Learning, & AI Text and Language processing and analysis
Introduction to natural language processing in Python
Alice Zhao (Metis)
As a data scientist, we are known to crunch numbers, but you need to decide what to do when you run into text data. Alice Zhao walks you through the steps to turn text data into a format that a machine can understand, explores some of the most popular text analytics techniques, and showcases several natural language processing (NLP) libraries in Python, including NLTK, TextBlob, spaCy, and gensim.
1:30pm-5:00pm (3h 30m) Data Science, Machine Learning, & AI Deep dive into specific tools, platforms, or frameworks, Text and Language processing and analysis
Natural language understanding at scale with Spark NLP
David Talby (Pacific AI), Alex Thomas (John Snow Labs), Saif Addin Ellafi (John Snow Labs), Claudiu Branzan (Accenture)
David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (NLP) using the highly performant, highly scalable open source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve.
9:00am-12:30pm (3h 30m) Data Engineering and Architecture, Streaming and IoT Cloud Platforms and SaaS, Data, Analytics, and AI Architecture, Streaming and IoT, Temporal data and time-series analytics
Serverless streaming architectures and algorithms for the enterprise
Arun Kejariwal (Independent), Karthik Ramasamy (Streamlio), Anurag Khandelwal (RISELab, UC Berkeley)
Arun Kejariwal, Karthik Ramasamy, and Anurag Khandelwal walk you through the landscape of streaming systems and examine the inception and growth of the serverless paradigm. You'll take a deep dive into Apache Pulsar, which provides native serverless support in the form of Pulsar functions and get a bird’s-eye view of the application domains where you can leverage Pulsar functions.
1:30pm-5:00pm (3h 30m) Data Engineering and Architecture BI, Interactive Analytics and Visualization, Cloud Platforms and SaaS, Data Management and Storage, Data, Analytics, and AI Architecture
From relational databases to cloud databases: Using the right tool for the right job
Gowrishankar Balasubramanian (Amazon Web Services), Rajeev Srinivasan (Amazon Web Services)
Enterprises adopt cloud platforms such as AWS for agility, elasticity, and cost savings. Database design and management requires a different mindset in AWS when compared to traditional RDBMS design. Gowrishankar Balasubramanian and Rajeev Srinivasan explore considerations in choosing the right database for your use case and access pattern while migrating or building a new application on the cloud.
9:00am-12:30pm (3h 30m) Data Science, Machine Learning, & AI Deep Learning
Deep learning from scratch
Bruno Goncalves (Data For Science, Inc)
You'll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonçalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you'll be prepared to dive deeper into the deep learning applications of your choice.
1:30pm-5:00pm (3h 30m) Data Engineering and Architecture BI, Interactive Analytics and Visualization, Cloud Platforms and SaaS, Data, Analytics, and AI Architecture
Architecting a data platform for enterprise use
Mark Madsen (Teradata), Todd Walter (Teradata)
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build a multiuse data infrastructure that isn't subject to past constraints. Mark Madsen and Todd Walter explore design assumptions and principles and walk you through a reference architecture to use as you work to unify your analytics infrastructure.
9:00am-12:30pm (3h 30m) Data Engineering and Architecture Cloud Platforms and SaaS, Data Management and Storage
Running multidisciplinary big data workloads in the cloud with CDP
James Morantus (Cloudera), Tony Huinker (Cloudera)
Organizations now run diverse, multidisciplinary, big data workloads that span data engineering, data warehousing, and data science applications. Many of these workloads operate on the same underlying data, and the workloads themselves can be transient or long running in nature. There are many challenges with moving these workloads to the cloud. In this talk we start off with a technical deep...
1:30pm-5:00pm (3h 30m) Data Engineering and Architecture, Streaming and IoT Deep dive into specific tools, platforms, or frameworks, Streaming and IoT
Kafka and Streams Messaging Manager (SMM) crash course
Purnima Reddy Kuchikulla (Cloudera), Dan Chaffelson (Cloudera)
Kafka is omnipresent and the backbone of streaming analytics applications and data lakes. The challenge is understanding what's going on overall in the Kafka cluster, including performance, issues, and message flows. Purnima Reddy Kuchikulla and Dan Chaffelson walk you through a hands-on experience to visualize the entire Kafka environment end-to-end and simplify Kafka operations via SMM.
9:00am-5:00pm (8h) Strata Business Summit
Big data for managers (Day 2)
Michael Li (The Data Incubator), Gonzalo Diaz (The Data Incubator)
Michael Li and Gonzalo Diaz provide a nontechnical overview of AI and data science. Learn common techniques, how to apply them in your organization, and common pitfalls to avoid. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making.
9:00am-5:00pm (8h) Data Science, Machine Learning, & AI
Recommendation systems using deep learning (Day 2)
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system.
9:00am-5:00pm (8h) Sponsored
SOLD OUT: Serverless machine learning with TensorFlow and BigQuery (sponsored by Google Cloud) (Day 2)
Jeff Davis (Google Cloud)
Jeff Davis provides a hands-on introduction to designing and building machine learning models on structured data on Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, you'll learn machine learning (ML) concepts and how to implement them using both BigQuery Machine Learning and TensorFlow and Keras.
9:00am-5:00pm (8h) Data Engineering and Architecture
Professional Kafka development (Day 2)
Jesse Anderson (Big Data Institute)
Jesse Anderson offers you an in-depth look at Apache Kafka. You'll learn how Kafka works and how to create real-time systems with it, as well as how to create consumers and publishers. You'll take a look Jesse then walks you through Kafka’s ecosystem, demonstrating how to use tools like Kafka Streams, Kafka Connect, and KSQL.
9:00am-12:30pm (3h 30m) Security and Privacy Privacy and Security
Getting ready for CCPA: Securing data lakes for heavy privacy regulation
Mark Donsky (Okera), Lars George (Okera), Michael Ernest (Dataiku), Ifigeneia Derekli (Cloudera)
New regulations drive compliance, governance, and security challenges for big data. Infosec and security groups must ensure a secured and governed environment across workloads that span on-premises, private cloud, multicloud, and hybrid cloud. Mark Donsky, Lars George, Michael Ernest, and Ifigeneia Derekli outline hands-on best practices for meeting these challenges with special attention to CCPA.
1:30pm-5:00pm (3h 30m) Data Engineering and Architecture Model Development, Governance, Operations
Hands-on machine learning with Kafka-based streaming pipelines
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
Boris Lublinsky and Dean Wampler examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more.
9:00am-5:00pm (8h) Data Engineering and Architecture
Building a serverless big data application on AWS (Day 2)
Jorge Lopez (Amazon Web Services)
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. Join Jorge Lopez to learn how to incorporate serverless concepts into your big data architectures. You'll explore design patterns to ingest, store, and analyze your data as you build a big data application using AWS technologies such as S3, Athena, Kinesis, and more.
9:00am-5:00pm (8h) Data Science, Machine Learning, & AI
Expand your data science and machine learning skills with Python, R, SQL, Spark, and TensorFlow (Day 2)
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but you face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by outlining the abstractions common to these systems. You'll go hands-on exercises to overcome obstacles to getting started using new tools.
9:00am-5:00pm (8h) Data Science, Machine Learning, & AI
Machine learning from scratch in TensorFlow (Day 2)
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil explores TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications.
9:00am-5:00pm (8h)
Data Case Studies
David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Rosaria Silipo (KNIME), Leah Xu (Spotify), Arup Nanda (Capital One), Victoriya Kalmanovich (Navy), Tusharadri Mukherjee (Lenovo), David Boyle (Audience Strategies), Richard Evans (Statistics Canada), Leah Xu (Spotify), Victoriya Kalmanovich (Navy), Moise Convolbo (Rakuten), Martin Mendez-Costabel (Bayer Crop Science), gloria macia (Roche AG), Gwen Campbell (Revibe Technologies), Moise Convolbo (Rakuten), Muhammed Idris (Capria VC | TeraCrunch)
From banking to biotech, retail to government, every business sector is changing in the face of abundant data. Get better at defining business problems and applying data solutions at Strata.
9:00am-5:00pm (8h) Sponsored
Machine learning for the enterprise (sponsored by IBM)
Matt Kirk (YourChiefScientist.com)
Note: This free workshop, courtesy of IBM, is open to the first 50 registrants. You'll take a fascinating deep dive into the power and applications of machine learning in the enterprise.
9:00am-5:00pm (8h)
Findata Day
Alistair Croll (Solve For Interesting), Jennifer Yang (Wells Fargo ECS), Nitzan Mekel-Bobrov (Capital One), Brian Lynch (TD Bank Group), Dan Barker (RSA Security), Rochelle March (Trucost), Catherine Gu (Stanford University), Karan Jaswal (Cinchy), Moto Tohda (Tokyo Century (USA)), Mikheil Nadareishvili (TBC Bank), Viridiana Lourdes (Ayasdi), Peter Swartz (Altana Trade)
From analyzing risk and detecting fraud to predicting payments and improving customer experience, take a deep dive into the ways data technologies are transforming the financial industry.
9:00am-12:30pm (3h 30m) Executive Briefing and best practices, Strata Business Summit Culture and Organization
Building and leading a successful AI practice for your organization
Rossella Blatt Vital (Wonderlic), Ross Piper (Wonderlic), Daniel Schmerling (Wonderlic)
Creating and leading a successful ML strategy is an elegant orchestration of many components: master key ML concepts, operationalize ML workflow, prioritize highest-value projects, build a high-performing team, nurture strategic partnerships, align with the company’s mission, etc. Rossella Blatt Vital details insights and lessons learned in how to create and lead a flourishing ML practice.
1:30pm-5:00pm (3h 30m) Executive Briefing and best practices, Strata Business Summit Culture and Organization
Managing data science in the enterprise
Alexander Izydorczyk (Coatue Managment), Benjamin Singleton (JetBlue), Joshua Poduska (Domino Data Lab)
The honeymoon era of data science is ending and accountability is coming. Not content to wait for results that may or may not arrive, successful data science leaders must deliver measurable impact on an increasing share of an enterprise’s KPIs. The speakers explore how leading organizations take a holistic approach to people, process, and technology to build a sustainable advantage.
9:00am-12:30pm (3h 30m) Data Engineering and Architecture BI, Interactive Analytics and Visualization, Data Management and Storage, Deep dive into specific tools, platforms, or frameworks
Learning Presto: SQL on anything
Matt Fuller (Starburst)
Used by Facebook, Netflix, Airbnb, LinkedIn, Twitter, Uber, and others, Presto has become the ubiquitous open source software for SQL on anything. Presto was built from the ground up for fast interactive SQL analytics against disparate data sources ranging in size from GBs to PBs. Join Matt Fuller to learn how to use Presto and explore use cases and best practices you can implement today.
1:30pm-5:00pm (3h 30m) Data Science, Machine Learning, & AI Deep Learning, Financial Services, Text and Language processing and analysis
Deep learning methods for natural language processing
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include Word2Vec, recurrent neural networks (RNNs) and variants (long short-term memory [LSTM] and gated recurrent unit [GRU]), and convolutional neural networks.
9:00am-12:30pm (3h 30m) Data Engineering and Architecture Data Integration and Data Processing, Deep dive into specific tools, platforms, or frameworks, Streaming and IoT
Real-time SQL stream processing at scale with Apache Kafka and KSQL
Viktor Gamov (Confluent)
Building stream processing applications is certainly one of the hot topics in the IT community. But if you've ever thought you needed to be a programmer to do stream processing and build stream processing data pipelines, think again. Viktor Gamov explores KSQL, the stream processing query engine built on top of Apache Kafka.
1:30pm-5:00pm (3h 30m) Data Engineering and Architecture Culture and Organization
Foundations for successful data projects
Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
The enterprise data management space has changed dramatically in recent years, and this has led to new challenges for organizations in creating successful data practices. Ted Malaska and Jonathan Seidman detail guidelines and best practices from planning to implementation based on years of experience working with companies to deliver successful data projects.
9:00am-12:30pm (3h 30m) Data Engineering and Architecture, Streaming and IoT Deep dive into specific tools, platforms, or frameworks, Streaming and IoT
Cloudera Edge Management in the IoT
Purnima Reddy Kuchikulla (Cloudera), Timothy Spann (Cloudera), Abdelkrim Hadjidj (Cloudera), Andre Araujo (Cloudera)
There are too many edge devices and agents, and you need to control and manage them. Purnima Reddy Kuchikulla, Timothy Spann, Abdelkrim Hadjidj, and Andre Araujo walk you through handling the difficulty in collecting real-time data and the trouble with updating a specific set of agents with edge applications. Get your hands dirty with CEM, which addresses these challenges with ease.
1:30pm-5:00pm (3h 30m) Data Science, Machine Learning, & AI Streaming and IoT, Temporal data and time-series analytics
Sketching data and other magic tricks
Sophie Watson (Red Hat), William Benton (Red Hat)
Go hands-on with Sophie Watson and William Benton to examine data structures that let you answer interesting queries about massive datasets in fixed amounts of space and constant time. This seems like magic, but they'll explain the key trick that makes it possible and show you how to use these structures for real-world machine learning and data engineering applications.
5:00pm-6:30pm (1h 30m)
Opening Reception
Enjoy delicious snacks and beverages with fellow Strata attendees, speakers, and sponsors at the Opening Reception, happening immediately after tutorials on Tuesday.
12:30pm-1:30pm (1h)
Break: Lunch
10:30am-11:00am (30m)
Break: Morning break sponsored by Microsoft
3:00pm-3:30pm (30m)
Break: Afternoon break sponsored by Dataiku

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