#LETITSNOW20

Save the date for Summit 2021

#LETITSNOW20

Hands-On Labs

Experience 30+ instructor-led and self-paced labs in our dedicated hands-on classrooms. You can try Snowflake’s newest features with personalized coaching from Snowflake experts and partners.

These labs are designed for Snowflake users of all skill levels, including foundational, intermediate, and advanced. You’ll gain the confidence to tackle both common and unique use cases, while broadening your understanding of Snowflake and partner tools.

Classroom space is limited and seating will be first-come-first-serve. Attendees must bring their own laptop. Lab sessions may require a dedicated Snowflake account, which you can obtain from the Snowflake website.

INSTRUCTOR LED

Generating Data at Scale for Performance Testing

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.

 

Track: Beyond the Data Warehouse
Skill-level: Advanced
Instructor: Robert Fehrmann, Snowflake Field CTO

Snowflake API for Developing Analytics Apps

Are you building a SaaS app or adding analytics to an existing one? In this lab, you’ll compare various API design patterns to determine if one is better suited for analytics. You’ll develop a serverless API that leverages Snowflake as an engine for an analytics application. You’ll also learn how advanced Snowflake features such as multi-cluster warehouses and multiple caching layers enable you to build a truly scalable and performant analytics API at a fraction of the cost of legacy systems.

 

Track: Enabling Developers
Skill-level: Advanced
Instructor: Ahmad Khan, Snowflake Senior Sales Engineer

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.

 

Track: Beyond the Data Warehouse
Skill-level: Advanced
Instructor: Nileema Shingte, Snowflake Software Engineer

Troubleshooting Snowflake Connectors

This lab will cover troubleshooting Python, JDBC, ODBC, SnowSQL connectivity errors and debugging techniques to make successful connections; including parsing driver logs.

 

Track: Integrating and Streaming Data
Skill-level: Advanced
Instructor: Mehul Suresh Kumar Jain, Snowflake Customer Technical Support Engineer

Managing Replication for Disaster Recovery

This lab will cover administrative tasks for setting up replication between global databases and performing switchover operations. Tasks include refreshing a secondary instance, monitoring progress, failover to the secondary instance, and failback to the original primary instance. Participants will also learn basic techniques to validate a secondary instance before switchover and receive an overview of account-level entities that are not replicated currently.

 

Track: Operating Snowflake
Skill-level: Advanced
Instructor: Vinay Srihari, Snowflake Field CTO

Snowflake Your Data: Fast, Easy BI with Snowflake

Despite years of hype and promise, BI is too often a complex, slow, and cumbersome process. Come and learn how to get rapid insight and easily-managed, scalable performance in this hands-on session with InterWorks, 2018 Partner of the Year from both Snowflake and Tableau.

 

Track: Accelerating Analytics
Instructor: Ben Young, InterWorks

Enabling Data Sharing Within Your Modern Data Architecture

Learn how enterprises are monetizing their data assets, enabling deeper insights, creating new business opportunities, and accessing and sharing data with their business partners in a live, secure, and governed process. In this workshop, you will discover the advantages of modern data sharing over a traditional data sharing model and the accompanying opportunities for your organization.

 

Track: Secure Data Sharing
Instructor: Ali Sajanlal, Clarity Insights

Legacy Got You Down? Liberate and Monetize Your Data!

Need fresh ideas on getting economic benefit from your analytics pipeline? The time to modernize and monetize is now. This not-to-be-missed session takes you through the journey of migrating from legacy systems, such as Netezza and Teradata, to Snowflake, the data warehouse built for the cloud, enabling you to productize your analytics pipeline and monetize your data.

 

Track: Data Warehouse Modernization
Instructor: Newel Rice, Sirius

Salesforce = Snowforce ... Cleaned Salesforce Data with Stitch and Snowflake

Doing our sales forecasting out of Salesforce used to be such a bother due to manually entered data. Using a combination of Stitch, Looker, and Snowflake as our data provider, we were able to fix our data inconsistencies, incorporate other data, and increase the accuracy of our sales forecasts.

 

Track: Enabling Developers
Instructor: Andrew Crider, Trianz

Supercharging Data Science and Machine Learning with Snowflake

Tired of “big data” turning into big overhead? Join us to gain tips on how to leverage the power of Snowflake to manage your enterprise data and enable your data science capabilities. In this session you will learn how to easily build, train, and score models using data managed by Snowflake.

 

Track: Beyond the Data Warehouse
Instructor: Sudha Regmi, Cervello

HOW TO: Integrate Streaming Data into Snowflake

Snowflake provides easy mechanisms to integrate data and can handle ingesting streaming data 3 different ways. We will cover the easiest and best ways to integrate batch and streaming data into Snowflake. We will demonstrate how to use SnowPipe, Databricks/Spark, and Confluent/Kafka on how to integrate streaming data into Snowflake.

 

Track: Integrating and Streaming Data
Instructor: Frank Bell, Vice President of Data and Analytics, Fairway Technologies
President & Founder, IT Strategists

Drowning In Your Data Lake? Come Up For Air With Snowflake.

Are you drowning in the complexity and cost of your data lake project? Maybe you’re treading water or just dipping your toe in? Jump in with Slalom and learn how they are helping customers simplify their data lake journeys using Snowflake. From loading, to integration, processing, and security, Snowflake is the cost-effective and clear-cut lifeline your data lake project needs.

 

Track: Beyond the Data Warehouse
Instructor: Dave Masino, Slalom

Self PACED

Exploring Semi-Structured Data in Snowflake

In this lab we’ll explore weather data, that is provided in the sample database in VARIANT format. We’ll extract certain values from the VARIANT data, and leverage our existing SQL skill to view the high temps by city for the past 24 hours. We’ll then create semi-structured objects from existing relational sample data.

 

Track: Accelerating Analytics
Skill-level: Advanced

Managing Snowflake Warehouse Workloads

This lab shows you how to identify warehouse behavior under load, handle capacity bottlenecks, and perform scale up and scale out techniques based on workload.

 

Track: Data Warehouse Modernization
Skill-level: Advanced

Auto-Clustering for Performance Efficiency

This lab covers post-implementation use cases that demonstrate how choosing the right clustering keys prevents query performance bottlenecks. You will also learn the benefits of auto-clustering and clustering materialized views.

 

Track: Data Warehouse Modernization
Skill-level: Advanced

Optimizing Query Performance

In this lab you’ll run performance tests and analyze profile metrics on several unique queries. You’ll then gather clustering information for a table and based on this information, set a clustering key and initiate a recluster. Finally, you’ll rerun performance tests on the newly clustered table and compare the results to those you gathered earlier.

 

Track: Data Warehouse Modernization
Skill-level: Advanced

Snowflake Fundamentals Lab - Part 1

This entry-level lab introduces you to Snowflake from an end user perspective. Get experience with Snowflake’s browser-based UI where you will create tables, load data, and execute queries.

 

Track: Data Warehouse Modernization
Skill-level: Foundational

Snowflake Fundamentals Lab - Part 2

This entry-level lab introduces Snowflake’s unique database capabilities from an end user and administrative perspective. Building on Snowflake Fundamentals Lab Part 1, it gives you hands-on experience with Snowflake’s patented Zero Copy Cloning,loading and querying semi-structured data, time travel through data, and sharing data with third parties in real time.

 

Track: Data Warehouse Modernization
Skill-level: Foundational

Data Ingestion with SnowPipe

Attend this lab to familiarize yourself with data ingestion using Snowflake’s SnowPipe service. Users will load data files into an external stage, create a SnowPipe with the auto-ingest feature, configure SQS notification, and validate data in target table. You will also investigate common issues and errors and learn strategies to resolve them.

 

Track: Integrating and Streaming Data
Skill-level: Advanced

WebUI and SnowSQL

Learn how to use UI wizards to complete various tasks. Understand how to open and use worksheets to query data in Snowflake. Learn how to explore objects available for query in the WebUI, where to download and install SnowSQL, and connect to Snowflake from the command line tool.

 

Track: Integrating and Streaming Data
Skill-level: Foundational

Loading Structured and Semi-Structured Data

In this lab, you will load structured and semi-structured data into Snowflake. You will use local files, internal stages, and external stages to source and load data. You will also leverage Snowflake’s VALIDATION_MODE and ON_ERROR parameters to troubleshoot and handle errors.

 

Track: Integrating and Streaming Data
Skill-level: Intermediate

Validating Your Data Loads

In this lab, you will learn how to validate data that has been loaded into Snowflake.

 

Track: Integrating and Streaming Data
Skill-level: Advanced

How to Unload Data from Snowflake

In this lab, you will unload different types of structured and semi-structured data from a Snowflake data warehouse into a stage. To achieve this, you will need to review the following concepts: internal stages, semi-structured files (such as JSON and Parquet), structured files (CSV), and file format objects.

 

Track: Integrating and Streaming Data
Skill-level: Intermediate

Identifying Performance Jams Using Query Profiles

This lab will show you how to review query profiles and rewrite queries to mitigate query performance bottlenecks and implement best practices to achieve a better query execution plan.

 

Track: Operating Snowflake
Skill-level: Advanced

Managing Network Policies for Access Lab

This lab will walk you through creating and validating network policies to restrict access to an IP subnet specific to Snowflake.

 

Track: Operating Snowflake
Skill-level: Foundational

Role-Based Security in Snowflake

This lab takes you through the steps needed to identify role-based requirements and implement them in Snowflake.

 

Track: Operating Snowflake
Skill-level: Advanced

Monitor and Optimize Data Storage in Snowflake

In this lab, you’ll explore how data objects are organized and the various ways to monitor and optimize data storage within Snowflake. You’ll first explore the Databases tab of the Snowflake user interface. You’ll then create objects via the UI and SQL and examine how they are organized. You’ll then execute queries to prove that Snowflake’s metadata enables you to execute certain queries without requiring compute. Next, you’ll programmatically execute various system functions to pull and analyze clustering metrics. Lastly, you’ll explore the Billing & Usage section of the Account tab and execute Information Schema and Account Usage commands to pull and analyze storage metrics.

 

Track: Operating Snowflake
Skill-level: Foundational

Create, Configure and Monitor Virtual Warehouses

Attend this lab to explore how to create, configure, and monitor virtual warehouses in Snowflake. You’ll first create a virtual warehouse using both the Create Warehouse wizard in the UI as well as using SQL. You’ll then execute commands to alter, resume, and suspend the warehouse. Lastly, you’ll explore the various ways to monitor warehouse usage.

 

Track: Operating Snowflake
Skill-level: Foundational

Restoring Database Objects with Time Travel

Complete exercises in this lab that will familiarize you with Snowflake’s Time Travel functionality and syntax. You will create, modify, and drop database objects, and load and delete data. You will then use Time Travel to restore these objects at various points in time.

 

Track: Operating Snowflake
Skill-level: Intermediate

How to Assess Usage and Storage within an Account

Complete various exercises in this lab to assess the usage and storage activity within a Snowflake account. You’ll also view and set parameter values to control behavior within the account. Finally, you’ll execute various commands against the information schema and the account usage share to uncover more detailed information about the account, the objects, and users within the account.

 

Track: Operating Snowflake
Skill-level: Advanced

Using Secure Views in Snowflake

This lab is a walkthrough for creating secure views to showcase restricted access to data.

 

Track: Secure Data Sharing
Skill-level: Foundational

A Primer on Data Sharing

Data Sharing is the process by which organizations share data inside and outside of the organization
in order to gain better and deeper insights to improve operational efficiencies.
What makes Snowflake Data Sharing better:
● No data movement: Data consumers directly access the shared data, so the data doesn’t need
to be moved or copied
● Live access: Consumers directly access the shared data and always see the current version
of data, even when the provider updates data
● Ready to use: Consumers can immediately start querying shared data and combining it with
their own data; they can just launch a virtual warehouse and go

 

Track: Secure Data Sharing
Skill-level: Advanced

Justification letter

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