Data Science

ASICS: Predicting an Athlete’s Performance … in the Rain

Is ASICS Runkeeper traffic moving because of a recent release, or is it just a change in the weather? Using shared weather data and over a billion historical runs and other workouts stored in Snowflake, representatives from ASICS attempt to resolve this question. This session describes how ASICS used the Snowflake Python connector and key data science packages in Jupyter to build predictive models and animated geographic data visualizations. This session also explores the impact that weather has on a runner’s race performance and how it can alter or disrupt the habits of individual runners.

SPEAKER
Chris Drouin
Manager of Analytics, Asics

Be The Match: Using Data and Analytics to cure Blood Cancer

Hear the incredible story of Be the Match and how leveraging data is directly enabling the organization’s goals to save more lives through the democratization of cell therapy. Mike McCullough, CIO, will discuss their journey, from recognizing the need for a modern data & analytics platform to defining the vision and sharing the progress towards realizing it.

SPEAKER
Mike McCullough
Chief Information Officer, Be The Match

CCI: Lessons Learned Building a New Data Science Platform

Brought to you by SnapLogic

Castleton Commodities International (CCI) is a leader in global commodities trading and investing. Chris Throop, head of CCI’s global data science initiative explains how his team developed a next-generation data science platform from the ground up to improve the company’s data processes and outcomes.
Learn how CCI crafted its data strategy, why it chose to make Snowflake and SnapLogic core elements of the data science platform, and how it successfully deployed that platform.

SPEAKER
Chris Throop
Managing Director, Global Head of Data Science, Castleton Commodities International

Chesapeake Energy Uses Databricks and Snowflake to Automate Data Pipelines

Brought to you by Databricks

Chesapeake Energy is augmenting real-time IOT streams with machine learning techniques to explore historical IOT data and other reference datasets to enhance operational and exploratory business needs. The result is increased efficiency and reduced operational maintenance in the field. Attend this session and learn how:

• Data engineering and data science workflows became more integrated and time to operationalize data science projects was reduced
• Workflow pattern between Snowflake and S3 were established for data engineering and data science use cases
• MLflow was incorporated to provide a repeatable approach for machine learning

SPEAKER
Blake Blackwell
Principal Data Architect, Chesapeake Energy

Devon: From Many Pools to Just One Modern Data Lake

You’ve implemented Snowflake, or you’re about to. What’s next? How about consolidating all of your hard-to-handle, underperforming solutions into a single repository? For years, Devon Energy had an underused data lake, a cumbersome data warehouse, unstable enterprise data sets, and an unmonitored open database. Find out how the natural gas exploration company consolidated this into a modern cloud data lake with Snowflake, Databricks, and Attunity. Devon’s consolidated data analytics platform now provides governed access to all users, and it has streamlined operational support and tooling. In addition it has more closely aligned IT teams with their business counterparts.

SPEAKER
Larry Querbach
Enterprise Data Architect, Devon

How Expedia Uses Snowflake To Supercharge Its Data Lakes

Expedia has multiple data lake silos due to its numerous brand acquisitions. In previous years, Expedia spent a significant amount of energy and resources to standardize these data lakes. The company’s data lake journey has evolved from using Hive to Presto to Spark. Join this session to learn how Expedia uses Snowflake to manage its data lakes and supercharge its data lake queries. And, don’t forget to ask how little effort Expedia spent to migrate its data lake queries to Snowflake.

SPEAKER
Aaron Dillow

Database Developer, Expedia

Saurin Shah

Product Manager, Snowflake

Harmoney: Averting Credit Issues With Predictive Modeling

Harmoney operates as a marketplace lending platform, providing personal loans to consumers in New Zealand and Australia. The company needed to rapidly produce and deploy a predictive model to avert a previously unseen credit issue that had serious implications for the business, which operates in an automated online environment. The combination of Snowflake and DataRobot enables V1 Harmoney to rapidly produce and deploy models, which are executed in near real time. This session explores the business problem, how Harmoney applied a machine learning model to help minimize the recurrence of the problem, the practical issues associated with deploying this model, performance monitoring, and ongoing refinements.

SPEAKER
Andrew Cathie
Chief Data Scientist, Harmoney

iDirect: How To Democratize Modern Data Analytics for All Your Users

Brought to you by Talend

iDirect, one of the world’s largest manufacturers for satellite network and communications hardware, wanted to enable big data analytics in the cloud. By deploying a joint solution from Datalytyx, Talend, and Snowflake, iDirect business users, including sales and marketing, now have direct access to relevant data analytics and data science capabilities. Any business user can manipulate data and spot business opportunities without a background in statistics or technology, and without incurring additional IT costs or needing dedicated resources.

SPEAKERS
Guy Adams
CTO of Datalytyx

Malwarebytes: Data Science With a Snowflake Data Lake

Malwarebytes is accelerating its data science efforts with Snowflake as its primary data lake. Snowflake’s unlimited scale, on-demand execution clusters, and support of a rich set of data formats have allowed Malwarebytes to centralize all data from transactions to billions of telemetry logs. This session demonstrates how data science, algorithms, and domain expertise are packaged into usable machine learning. You’ll learn how Malwarebytes ingests real-time application telemetry into Snowflake, using Kafka for training data science models in R and Python, and applies the classification models to real-time streaming data for identifying detections and false positives.

SPEAKER
Manjunath Vasishta
Director, Data Science and Engineering, Malwarebytes

Outreach: Faster and More Accurate Query Results With Snowflake

Outreach’s sales engagement platform leverages machine learning (ML) and A/B testing to coordinate email, voice, and social interactions. The need for trustworthy decision-making through A/B testing in sales was clear. However, lack of quality infrastructure frequently resulted in invalid tests and the inability to correctly guide users through the testing process. This session covers how Snowflake served as the quality foundation for implementing a trustworthy A/B testing solution. Snowflake’s native support for semi-structured data eliminated the need to conduct schema planning while delivering a single source of truth for error and user event data. You’ll also learn how Snowflake’s easy integration with Databricks enables Outreach to efficiently run about 1 million statistical tests per day.

SPEAKER
Pavel Dmitriev
VP of Data Science, Outreach

ShopRunner: Bringing Data + Machine Learning Together for Repeatable Success

Brought to you by Databricks

ShopRunner helps online shoppers get what they love, faster by connecting retailers to high-value customers with free shipping, highly personalized product feeds, granular inventory information, increasing both conversion rates and average spend. See how ShopRunner uses Databricks and Snowflake to tackle data science problems across personalization, recommendations, targeting, and analysis of text and images.

SPEAKER
Kenneth Dallmeyer
Sr. Data Engineer, ShopRunner

Strava: Using Data as the key to uncovering growth channels

Strava is the global network for athletes. Strava’s mission is to connect athletes to what motivates them and help them find their personal best. We do this by being the record of the world’s athletic activities and creating the technology that makes every effort count. This session will explore how athletes discover Strava today, and how Strava uses customer data to improve product discoverability. During the session, you will learn how Strava uses Snowflake to connect data from disparate sources with internal data to uncover its growth channels and analyze the customer acquisition journey. Topics covered include attribution modeling, incrementality measurement of paid acquisition and web experimentation.

SPEAKER
Mike Li
Marketing Data Scientist, Strava

Trimble : Predictive Modeling for Better, Faster Insights

Trimble develops Global Navigation Satellite System receivers, laser rangefinders, unmanned aerial vehicles, inertial navigation systems, and software processing tools. Learn how Trimble uses Snowflake’s robust data connectors and scalable compute to efficiently store and transfer data between tools during the analytics lifecycle of model-building. The end result? Trimble’s data scientists now spend more time developing predictive models and less time waiting for data to become available. They can quickly build R&D data sets and discover insights that inform and support decision-making for predictive maintenance, video analysis, and employee-churn analysis.

SPEAKER
Anne Hunt
Manager, Data Science, Trimble

Turner's Journey: From Hadoop to Snowflake via the CLEAR Framework

As Turner’s teams became more data-driven, the media company ran into a
(good) problem: their legacy system couldn’t handle the volume. It
became clear they not only needed a data warehouse that could meet their
needs, but they needed a better way to migrate to reduce business costs
and interference. In this session, Turner Senior Tech Director Vikram
Marathe will share how and why his team selected Snowflake, and Clarity
Insights Snowflake CoE Leader Ali Sajanlal will break down the CLEAR
migration framework which helped Turner migrate from Hadoop. You’ll see
how best-in-class organizations take advantage of Snowflake’s data
sharing and learn firsthand how to execute a seamless migration.

SPEAKER
Vikram Marathe
Sr. Technical Director, Warner Media

Yieldmo: Building the Modern Data Lake With Snowflake

Learn how Yieldmo successfully migrated its enterprise data stack from Hadoop to Snowflake. This session details the limitations of Yieldmo’s legacy system and how Yieldmo partnered with Snowflake to accelerate its data practice. Yieldmo uses Snowflake compute to ingest, load, and transform disparate semi-structured source formats, and co-locate data. Today, its Snowflake data lake efficiently supports various mission-critical business functions such as billing, attribution, reporting, and targeting as well as distributed machine learning pipelines.

SPEAKERS
Indu Narayan
VP Data, Yieldmo

Rohit Matthews
Big Data Architect, Yieldmo

Building a Digital Platform at Uniper

Uniper, an international energy company located in Düsseldorf, Germany is using Snowflake as a central data lake in its Data Analytics Platform on Microsoft Azure. In this session Uniper will share their insights about motivation, their data strategy and transformation road-map into a data driven organization. Sample use case will be shared with the audience to show how Snowflake is used for data analytics across Uniper’s organization.

SPEAKER
Rene Greiner
VP Data Integration, Uniper

Automated Machine Learning with DataRobot and Snowflake

Brought to you by DataRobot

Predictive modeling using machine learning techniques is transforming every aspect of modern business. Traditional approaches to machine learning is a time-consuming, resource-intensive and highly error-prone process. Automated machine learning platforms can make the process of building highly accurate predictive models fast and efficient. In this session, we will show how DataRobot can collaborate with data scientists to quickly build hundreds of highly accurate predictive models in a transparent and flexible manner, generate deep insights and deliver immediate value to business with easy deployment options. We will also illustrate how Snowflake users can bring in data from their data warehouse to DataRobot, delivering the performance, simplicity, concurrency, and affordability not possible with other data analytics platforms.

SPEAKER
James Johnston
Head of Field Engineering, DataRobot

Deep Dive on Multi-Cloud External Stages in Snowflake

External stages enable customers to load data directly from cloud-based storage services. Come learn about recent enhancements to the set of supported cloud storage services, security and permissioning improvements, and how your organization can leverage the new external stages while enforcing rules on data exfiltration to secure your data.

SPEAKER
Saurin Shah
Sr. Product Manager, Snowflake

Enabling AI Initiatives Through Operationalization and Self-Serve Analytics

Brought to you by Dataiku

Many organizations with the hope of becoming more data-driven ask the question: self-service analytics, or data science operationalization – which will get me where I need to be? And the answer is: you need both together. The fact is, it’s the interplay and balance between operationalization (o16n) and self-service analytics (SSA) initiatives that makes a successful data-powered company that executes on all projects to its fullest potential. While at first glance the two appear to be completely different (maybe even contradictory), it’s precisely because they differ in value, scale, and more that they round out a complete data strategy. This talk takes focuses on how to implement a complete strategy for both, pitfalls to avoid along the way, and use cases of large enterprises who have successfully implemented the two.

SPEAKER
Jesse Bishop
Lead Data Scientist, Dataiku

End-to-End Machine Learning with Snowflake and XGBoost

At least 80% of the work in machine learning is basic data management and processing: things at which databases excel. Snowflake’s engineering team will walk you through an end-to-end machine learning example using various factors in a Chicago taxi data to predict taxi fare. We’ll cover data ingestion, data cleaning, and preprocessing using Snowflake; integration with XGBoost for training; and deployment of the resulting model back to Snowflake as a simple SQL query. Except for the training step, which happens on an external machine, everything relies on Snowflake’s strong data processing power.

SPEAKER
Bradley Jiang
Software Engineer, Snowflake

How to Unify Your Data Lake With Snowflake

Attend this session to learn how to use Snowflake’s new features to integrate your existing data lake platform and strategy with your data warehouse.

SPEAKER
Saurin Shah
Sr. Product Manager, Snowflake

Overview of AI, ML, and Data Science in the Snowflake Ecosystem

The Snowflake partner ecosystem powers a number of robust AI, ML and Data Science solutions. While each of these solutions provides distinct value for various types of users, all of them rely on data to drive insight. This session will highlight the benefits of leveraging Snowflake’s Cloud Data Warehouse as a flexible and scalable data platform to provide data for all these solutions, including real-life use cases.

SPEAKER
Todd Beauchene
Senior Sales Engineer, Snowflake

Predicting the Future With AWS Forecast and Snowflake

No crystal balls are necessary when you have Snowflake and AWS to predict your future. See how easy it is to integrate Snowflake with the AWS machine learning and AI stack. This session includes a code walkthrough and demo of Snowflake integrated with AWS Forecast.

SPEAKER
Daniel Freundel
Sales Engineer, Snowflake

Snowflake as an Engineering Feature Repository

Organizations are increasingly realizing they need to consolidate the outputs of their data and feature engineering into a central repository for reuse. This session covers the techniques and best practices for data reuse in Snowflake, and how the key capabilities of Snowflake provide an optimal feature repository.

SPEAKER
Simon Field
Field CTO, Snowflake

Using TensorFlow with Snowflake Secure Data Sharing

Snowflake extends the power of cloud data warehousing. In this session, see how Snowflake uses live and secure data sharing with native programmatic connectors to integrate and process data in custom models. This session includes a demo of Snowflake used with a TensorFlow image recognition model.

SPEAKER
Daniel Freundel
Sales Engineer, Snowflake

Bring Your Own Schema: Performance Evaluation with Generated Data

You need a new data warehouse and have done your homework, you’ve compared features and benchmarks, and you’ve settled on a short list. But one important question is left. How will the new system perform with your actual data? This is impossible to answer unless you put your valuable data through a proof of concept.
If that’s not an option, come to this session and learn how to easily generate data that is structured like your data, looks like your data, and most importantly, scales like your data–so you can run your actual queries and see firsthand how the new system performs. Run your own benchmarks without the hassle of putting your data into the new system by tapping into the power of Snowflake’s data generation capabilities.

SPEAKER
Robert Fehrmann
Field CTO, Snowflake

Run Analytics on a Data Lake Using Snowflake

Most companies have made significant investments in building out data lakes as their central data repository. And most customers use a data warehouse to query and analyze business-critical data and run reports on that data. But what about other non-business critical data? What about ad-hoc queries that need to be executed on archived data or infrequently used data? How can you achieve fast performance on these queries? In this hands-on lab you will use Snowflake features to run analytics on data in your data lakes.

SPEAKERS
Nileema Shingte
Software Engineer, Snowflake

Saurin Shah
Product Manager, Snowflake

Supercharging Data Science and Machine Learning with Snowflake

Brought to you by Cervello

Tired of big data turning into big overhead? Join us in this session to learn how to harness the power of Snowflake to manage your enterprise data and enable your data science capabilities. You will learn how to easily build, train, and score models using data managed in Snowflake.

SPEAKER
Sudha Regmi
Data Scientist, Cervello