New Track

Modern Data Lakes

Online courses are now available.

Solve Your Biggest Data Challenges
Join the Cloud Analytics Academy

Champion cloud analytics in your organization with any of the Academy’s six free instructional tracks. These industry-leading, expert-led courses will equip you with the building blocks and advanced technical concepts to propel your cloud analytics. Join the Academy and you’ll leave as a cloud analytics expert, with the skills to champion your organization to lead with the cloud.

NEW: Modern Data Lakes Track

Featured Track

Data is one of the most critical assets that an organization owns. While almost every organization builds a data architecture to store, prepare, manage, and analyze its data, the method behind the central data repository can vary greatly. To help organizations find the right approach for their data analytics needs, we’ve launched the new Modern Data Lakes track.

Who Should Join?

Business Leaders

Adopt a data-driven strategy that can transform your business, increase revenue, and reveal new market opportunities.

Technical Experts

Benefit from technical topics such as the Agile methodology in data warehousing and the most efficient methods for preparing streaming data for analysis.

Analysts and BI Implementers

Learn about the latest methodologies and technology to foster effective and insightful BI projects.

Cloud Analytics Academy
Tracks

4 Sessions

Once you watch and pass the featured sessions, continue your cloud analytics education by taking advantage of one or all of the following tracks.

Executive Fast Track

5 Sessions

Start Now

Not All Data Warehouses Are Created Equal

There are many options available today, and more being added all the time, for so-called “cloud” data warehousing. But like every evolution in the IT landscape, the abundance of hype and confusing terminology can make it difficult to evaluate your options confidently. This session will give you an overview of the main types of cloud data warehouses, along with a short list of evaluation criteria you can use to vet these options.

Instructor

Chris James

Duo Security

Enabling BI with the Cloud Data Warehouse

More than half of BI projects do not have a successful outcome. Minimize your risks and beat the odds with these helpful tips.

Instructor

Thuy Kim

VP Data Governance

Data sharing: Legacy and State of the Art

In today’s world, data is being generated faster than ever before. Organizations invest huge amounts of money into their Data infrastructure but data remains in silos. To really take advantage of Data, Organizations need to share as well as supplement Data for their own advantage. This course provides an overview of why legacy data sharing processes are difficult to manage, fragile, and have inherited security risks, as well as an overview of how cloud-based database technologies can provide a path to overcome these problems.

Instructor

Robert Fehrmann

Snowflake

Guide to a Successful POC for a Cloud Data Warehouse

After investigating different cloud data warehouse solutions, viewing demos, asking questions and meeting with each vendor’s team, you should do a proof-of-concept (POC) before deciding which solution to choose. A POC is a process of testing a solution to determine how well it serves your needs and meets your success criteria. Think of it as a test drive. This session will give you the guidance you need to set up an effective POC.

Instructor

Amy Kaulius

Snowflake

Understanding Your Analytics ROI

Saying your company is “data-driven” is a lot like telling your Tinder dates you like travel and cooking. It’s easy to say, easy to believe, and significantly harder to actually put into practice. The ROI of all the time, talent and money required to build an effective data system can seem daunting. This session shares perspectives and tips for how to measure and improve your analytic output.

Instructor

Joseph Bates

Sharethrough

Cloud Foundation Track

4 Sessions

Start Now

Bringing Your Data Together in the Cloud

Before you can begin to leverage the benefits of a cloud data platform, you have to get your data loaded. From one-time data migrations to real-time data syncs, this session will provide details on a number of tools and methods for feeding data into a cloud data platform.

Instructor

Todd Beauchene

Snowflake

In the Cloud: ELT vs. ETL

For decades, we have relied primarily on ETL (Extract, Transform, and Load) to get data into enterprise data warehouses. In recent years, ELT (Extract, Load, Transform) has become more viable, and in many cases optimal. What are the fundamental differences in these two approaches? What are the basic use cases and architectural frameworks for each? This session will give you the information you need to decide which approach is best for your organization.

Instructor

Joseph Bates

Sharethrough

The Correct Way to Build a Data Lake

Even though many early attempts at building data lakes have failed to deliver business value, the concept of a data lake continues to have enormous potential. The cloud provides an alternative conduit for building a repository of raw data from many sources and making it accessible to data consumers. This session will explore possible architectures, techniques and technologies you can use to start building your data lake in the cloud.

Instructor

Ashwin Viswanath

Talend

Modern Data Modeling in the Cloud

There’s never been a better time to take advantage of data and analytics using the cloud. With data generated from people, businesses and the Internet of Things moving online and getting instrumented, organizations have an unprecedented opportunity to discover new insights and deliver business results. However, with this opportunity comes complexity; traditional data management tools and techniques aren’t enough to fully realize the potential of data. With greater agility, affordability and the ability to decouple storage and compute, more organizations are turning to the cloud and using a modernized data warehouse as a better approach to data management and analysis.

Instructor

Daniel Mintz

Chief Data Evangelist

Modern Data Analytics Track

4 Sessions

Start Now

Enabling Data Warehouse Development in the Cloud

It’s a brave new word in the cloud. Or is it? You still need standard processes for development and testing, but now there’s an opportunity to go one step beyond with a cloud data warehouse – you really can have production quality data in dev and test! This session will show you how to support a promotion process from development to testing to production using the features of a modern cloud data warehouse.

Instructor

Steve Herskovitz

Snowflake

Agile Data Warehousing in the Cloud

Moving data infrastructure to the cloud can offer your IT organization improved performance, elasticity, scalability and lower operating costs. It is also an opportunity for your IT team to re-evaluate the current processes, roles and tools used to deliver enterprise data warehouses and identify new ways to increase your agility.

Instructor

Jason Laws

WhereScape

Flexible Data Analytics with Python

In this session, you will learn how to use open source Python packages (such as link) to connect to and query data, regardless of the actual data warehouse used. You will also learn how to analyze and transform data sets in Python using pandas and numpy.

Instructor

Kayvon Raphael

SpringServe

Evaluating Performance for Cloud Data Warehousing

With the cloud, it’s easier than ever to evaluate and compare a variety of data warehousing solutions at scale — you can provision a platform nearly instantaneously and pay for what you use. In this course, you will learn how to design and conduct effective performance tests for cloud data warehousing, and compare results that take into account raw processing power, cost, and elasticity features. Whether you use your own data or work with industry-standard benchmarks, this session will prepare you to execute tests to help you select a next-generation analytics platform.

Instructor

Stuart Ozer

Snowflake

Data Science and Machine Learning Track

3 Sessions

Start Now

AI for business processes

With this track, we walk through and weigh the benefits, pros and cons, of ML/DL/AI and the impact on business processes. We also discuss the data infrastructure and resources necessary to support effective development of ML/DL/AI for business processes.

Instructor

Michael Nixon

Sr. Director, Product Marketing

Data Science: A Foundation

This executive overview outlines the goals of any data science project. While this is just a teaspoon sip of what data science is all about, guidelines and a generic checklist to follow show some of the most important steps and goals associated with a Data Science project.

Instructor

Doug Needham

Data Scientist

Data Science and Spark: How science drives revenue and efficiencies

Data Science is the discipline that helps your organization make better decisions and drive top line revenue. In this class, you will learn why most organizations are embracing data science and why and how to use Spark to build your machine learning pipelines.

Instructor

Piero Cinquegrana

Qubole

Data-Driven Marketing Track

4 Sessions

Start Now

The CMO’s Growing Role in Customer Data and Marketing Technology

The role of the CMO is changing. In the past, Marketing was outsourced to agencies to execute strategy, campaign management and other initiatives. There was a bigger emphasis on high funnel metrics such as brand lift & recall, brand exposure, and so on. You fast forward to today, everything has become digital and Marketers have the ability to target specific individuals with a tailored message at the right time. Technology investments in Marketing organizations continue to grow and understanding how to leverage & operationalize data has become a critical differentiator for brands. In this session, you will learn why this trend has evolved and how you can enable your brand to be more data driven.

Instructor

Bill Stratton

Snowflake

Centralizing and Actioning on your Customer Data

When it comes to buying experiences, customers expect the highest level of personalization. To keep up, modern marketing has evolved from broad traditional advertising to data driven digital marketing campaigns. To create these campaigns, marketers must be able to centralize and action on comprehensive customer data in real-time. While this sounds simple, aggregating disparate data sources and empowering marketing to test and iterate can be challenging. In this session, you will see how top marketing teams are centralizing and utilizing customer data to create campaigns that increase engagement and ROI.

Instructor

Tim Fletcher

Stride Software

Best Practices: BI for Marketing

Marketing has evolved a lot since the days of Mad Men. Technology advancements have turned Marketing into not just a data-driven department, but a key driver of revenue for many businesses. With the advent of new marketing technologies and tools, data is now readily available, but much of that data still lives in disparate places, making it hard for marketing leaders to get a full-funnel view of how their investments translate into revenue. This session explores BI challenges and best practices for Marketing, followed by a demo of a full-funnel marketing dashboard example.

Instructor

Britton Stamper

Periscope Data

How Marketing is Embracing Digital Disruption, A Best Practice Guide

The world of marketing continues to get disrupted with the ever growing martech landscape, the shifting customer experience, and the sheer volume of data. Marketers are challenged to continue to drive revenue growth while embracing our data-driven, digitally-led world. Learn how you can gain 100% confidence in your data, improve your productivity and efficiency by 10-50x while increasing your multi-channel campaign performance by 10%.

Join conDati, a leading AI-driven marketing performance platform, to learn best practices in applying AI/ML for higher pipeline and revenue conversions; while gaining a step by step maturity guide to optimize cross-channel campaign performance and return on ad spend.

Instructor

Linh Ho

Condati

Modern Data Lakes Track

4 Sessions

Start Now

Simplifying Data Lakes with a Modern Cloud Data Platform

Beginning with a review of basic data lake fundamentals that originated nearly 10 years ago, this session dives one layer deeper to explain the difference and advantages with a more modern approach. The session covers the main phases of a data lake journey and then describes modern data platforms that simplify and accelerate data lake implementation. Upon completion, you will have a better understanding of how a modern cloud data platform dramatically simplifies data lake design patterns.

Instructor

Michael Nixon

Sr. Director Product Marketing, Snowflake

Data Lakes: The Keys to Success

A data lake can be a great solution for storing raw structured, semi-structured, and unstructured data until it’s needed for analysis. However, without proper management and governance, a data lake can quickly turn into a data swamp where the right information is impossible to find. How do you create a successful data lake that enhances rather than hinders insight? This session takes you through the basic concepts of a data lake, ideal use cases, and best practices for organization.

Instructor

Paul Johnson

Solution Architect, Matillion

How to Build a Modern Enterprise Data Lake for BI and Advanced Analytics

An agile and flexible cloud data platform combined with a unified data management solution work jointly to enable data warehouse and data lake use cases to come together on a single platform. As a result, businesses can enjoy the cost and flexibility advantages of a data lake with the performance and analytics advantages of a data warehouse.

In this session, Talend describes a cloud-based, modern, data platform architecture that embodies data stewardship, governance, and security from the very start, thus handling all aspects of data management and high-performance analytics in a scalable manner.

Instructor

Michael Destein

Director of Technology Alliances, Talend

Relational Databases for Data Lakes: 2010 vs 2020. Assumptions then and now

This session will examine, analyze, and compare the assumptions that served as the basis for the data lake concept originated in 2010, with the capabilities of modern, relational database management systems of today. We assess whether the assumptions and conclusions about relational database management systems in 2010, hold true today in 2020.

Instructor

Michael Nixon

Sr. Director Product Marketing

Cloud Analytics Academy Instructors

Tom Davenport

Best Selling Author | Professor at Babson College

Tom Davenport

Best Selling Author | Professor at Babson College

Amy Kaulius

Senior Engineer

Snowflake

Amy Kaulius

Senior Engineer

Snowflake

Ashwin Viswanath

Director, Cloud Product Marketing

Talend

Ashwin Viswanath

Director, Cloud Product Marketing

Talend

Chris James

Data Warehouse Developer

Duo Security

Chris James

Data Warehouse Developer

Duo Security

Daniel Mintz

Chief Data Evangelist

Looker

Daniel Mintz

Chief Data Evangelist

Looker

Jason Laws

VP of Product Management

WhereScape

Jason Laws

VP of Product Management

WhereScape

Joseph Bates

Head of Analytics

Sharethrough

Joseph Bates

Head of Analytics

Sharethrough

Kayvon Raphael

Software Engineer

SpringServe

Kayvon Raphael

Software Engineer

SpringServe

Paul Sears

Partner Solutions Architect Big Data Ecosystem

Amazon Web Services

Paul Sears

Partner Solutions Architect Big Data Ecosystem

Amazon Web Services

Robert Fehrmann

Field CTO

Snowflake

Robert Fehrmann

Field CTO

Snowflake

Steve Herskovitz

VP of Sales Engineering

Snowflake

Steve Herskovitz

VP of Sales Engineering

Snowflake

Thuy Kim

VP, Data Governance

Thuy Kim

VP, Data Governance

Todd Beauchene

Senior Technical Architect

Snowflake

Todd Beauchene

Senior Technical Architect

Snowflake

Stuart Ozer

Field CTO

Snowflake

Stuart Ozer

Field CTO

Snowflake

Michael Nixon

Senior Director of Product Marketing

Snowflake

Michael Nixon

Senior Director of Product Marketing

Snowflake

Doug Needham

Data Scientist

Cengage

Doug Needham

Data Scientist

Cengage

Claudia Imhoff

Founder

Boulder BI Brain Trust

Claudia Imhoff

Founder

Boulder BI Brain Trust

Piero Cinquegrana

Data Science Senior PM

Qubole

Piero Cinquegrana

Data Science Senior PM

Qubole

Linh Ho

Co-Founder and CMO

Condati

Linh Ho

Co-Founder and CMO

Condati

Tim Fletcher

CEO & Co-Founder

Stride Software

Tim Fletcher

CEO & Co-Founder

Stride Software

Britton Stamper

Sales Engineer

Periscope Data

Britton Stamper

Sales Engineer

Periscope Data

Bill Stratton

Head of Media & Entertainment Strategy at Snowflake

Snowflake

Bill Stratton

Head of Media & Entertainment Strategy at Snowflake

Snowflake

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