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A free virtual event designed to change how you do business with data

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30+ Sessions
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Live Q&A With Experts
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Customer Success Stories
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Technical Demos
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Hands-on Learning
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Join us (and 40,000+ fellow data evangelists) where data meets innovation

Welcome to AWS Innovate Online Conference - Data Edition, a free virtual event designed to inspire and empower you to make decisions that drive your business forward. Learn firsthand about key concepts, business use cases, and best practices from AWS experts over 30 technical and business sessions. Innovation starts with you.

Innovation Stories

We changed the world.

Now we're changing how you do business using data.

 Why attend?

It's not enough to have a lot of data - organizations that leverage data-enabled learning outperform those that don't. Uncover the best practices that empower that learning for your company.

 Who should attend?

Whether you're responsible for your data infrastructure or the business insights that can be derived, this conference has relevant content for you.


Rahul Pathak

Lita Sands
Head of Life Sciences Solutions, AWS

Lita Sands is the Worldwide Leader of Life Sciences Strategic Solutions for AWS.  In this role, she works with business leaders to accelerate transformation goals across the value chain through advanced cloud-based solutions to drive better outcomes for patients, caregivers, and healthcare providers.  Prior to this role, Lita led the AWS-Deloitte WW healthcare and life science alliance, where she drove development of enterprise platform solutions for everything from decentralized clinical trials to real world evidence.

Olivier Klein

Imtiaz (Taz) Sayed
Principal Solution Architect, AWS

Imtiaz (Taz) Sayed leads the Worldwide Data Analytics Solutions Architecture community at AWS. He is a Principal Solutions Architect, and works with diverse customers engaging in thought leadership, strategic partnerships and specialized guidance on building modern data platforms on AWS.  He is a technologist with over 20 years of experience across several domains including distributed architectures, data analytics, service mesh, databases, and DevOps.

Session levels designed for you

INTRODUCTORY
Level 100

Sessions are focused on providing an overview of AWS services and features, with the assumption that attendees are new to the topic.

INTERMEDIATE
Level 200

Sessions are focused on providing best practices, details of service features and demos with the assumption that attendees have introductory knowledge of the topics.

ADVANCED
Level 300

Sessions dive deeper into the selected topic. Presenters assume that the audience has some familiarity with the topic, but may or may not have direct experience implementing a similar solution.

EXPERT
Level 400

Sessions are for attendees who are deeply familiar with the topic, have implemented a solution on their own already, and are comfortable with how the technology works across multiple services, architectures, and implementations.

Available tracks

  • Track 1: Business Leader

    Amazon's culture of innovation

    Amazon’s approach to innovation has remained consistent since the company first launched: start with the customer and work backwards. In this session, learn about Amazon’s peculiar culture and how we innovate through four distinct, yet interdependent, elements: culture, mechanisms, architecture, and organization. Dive deep on each topic, and learn more about practical applications including Amazon Leadership Principles, Working Backwards, two-pizza teams, the PRFAQ doc, and why it’s always still Day 1 at Amazon and AWS.

    Data-driven enterprise: Going from vision to value

    Businesses want to unleash the value of data to increase agility, drive innovation, and improve efficiency. While data is abundant and growing rapidly, just producing or storing a lot of it doesn’t automatically create value. Value is realized by creating a culture and operating model that use the data to invent on behalf of customers using actioned insights, analytics, and AI/ML. However, cultural challenges, outdated governance models, organizational silos, and legacy approaches stand in the way of realizing this vision. Join this session to learn strategies rooted in the first-hand experience of former CXOs on how to overcome these barriers to turn this vision into a reality.

    Data and the C-suite/S-team: How do you use data for bigger-picture decisions? 

    ​​Learn how AWS and Amazon have developed mechanisms for applying data and analytics to create scalable business efficiencies and address our customers’ most complex challenges. In this session, learn how to help leaders and teams respond quickly and strategically to unexpected “unknowns,” how to use data as a strategic asset and make better business decisions informed by data, and how to enhance customer experiences and improve business efficiencies and outcomes.

    Building a smarter organization powered by machine learning

    Technology that is going to “change the world” emerges every decade, but adopting the new technology often proves challenging. For several decades, the promise of artificial intelligence (AI) and machine learning (ML) has been tempered by these challenges; however, organizations are now transforming their businesses with AI and ML. In this session, tailored for business leaders, AWS enterprise strategists share how organizations can apply AI and ML to realize their futures. Join this session to learn from stories about how AWS customers are driving profound societal and business results using AI and ML.

    High-velocity decision-making

    In this chalk talk, dive into the mechanisms and mental models that help Amazon and AWS make high-velocity and high-quality decisions. Aimed at executives and team leads, this chalk talk shares AWS best practices with a hands-on approach. Learn about concepts and mechanisms such as Leadership Principles, tenets, one-way and two-way doors that inform speed and prioritization, the benefits of fast and friendly escalation, and more—all in a highly dynamic, collaborative environment that practically applies these concepts to real-world business scenarios.

    ML at Amazon.com

    The practical application of AI and ML helps Amazon deliver innovative customer experiences like Amazon Go, Amazon Prime next-day delivery, and personalized Amazon Video recommendations. These innovations delight customers and increase the value they get from Amazon. Amazon continues to look for ways to deliver better services, and sees AI/ML as a foundational enabler toward innovating at speed and scale to meet its evolving customer needs. In this session, tailored for senior business and technology decision makers, learn about the approach Amazon.com takes to build and scale ML-enabled innovations across all operational areas and create new opportunities to delight customers.

  • Track 2: Technical Leader

    Building a modern data strategy

    Applying old organizational models and methodologies to modern, cloud technology can prevent companies from achieving the kind of results and agility they want. Companies need an operating model, organizational model, and data literacy approach that enables data-driven decision making throughout an organization. In this session, explore the people and process considerations for building a modern data strategy.

    Analytics market trends

    Join this session to hear about emerging trends that AWS is seeing in the analytics market. These trends revolve around data ethics, new architectural patterns, data exchanges, and governance.

    Modern data architecture to accelerate your data strategy

    Being data-driven requires technology solutions that help you work with your data at scale and classify, secure, and govern your data across multiple business domains. In this session, learn how AWS data storage, database, analytics, and machine learning technologies can help you use data to drive business outcomes. See a modern data architecture that allows you to store data at virtually any scale, and how to use AWS purpose-built services to support a variety of complex data transformation workloads with high security and transparent governance. Also see how to build advanced patterns like data marketplaces powered by Amazon Redshift and AWS Data Exchange and how the data mesh pattern joins these technologies.

    Unify data silos by connecting and sharing varied data sources

    Data can live in silos in an organization and experience version control issues across departments, regions. Data copying, transformation, movement can take time, result in errors, and introduce consistency issues. Amazon Redshift data sharing provides a simple and direct way to share internal and external (from 3rd party data providers) data across data warehouses with instant, granular, high performance access to transactionally consistent, live, secure data without data copies or movement. Learn how to share data in a well governed way across your organizations and regions and through the external data exchange with the latest Amazon Redshift data sharing features.

    Invent experiences and optimize processes with AI/ML

    Data fuels innovation and innovation helps organizations stay ahead of the curve. Advanced technologies such as analytics, business intelligence, AI, and ML can help you accelerate innovation and realize your strategic goals faster. Learn how AWS can help you invent new experiences, predict the future, optimize processes, and reduce costs with AI/ML.

    Accelerating your data journey with AWS and AWS Partners

    Join this session if your organization is eager to embark on its digital transformation but is unsure how to get started with minimal business disruptions. In this session, learn how the AWS Partner Network (APN) can help accelerate your data journey from consultative guidance on migration strategy to total cost of ownership assessment to standing up a proof of concept to hosting services. Get actionable takeaways to make your digital transformation a reality.

  • Track 3: Data Developer

    Getting started with serverless analytics and data lakes

    Serverless architectures free developers from having to think about infrastructure and capacity management to scale up or down to suit varying data volumes and data usage needs. AWS analytics going serverless means that developers can focus on building better applications and serving their customers with elasticity and scale. Learn how to get started with analytics in seconds with new serverless options in Amazon EMR, Amazon Redshift, Amazon MSK and Amazon Kinesis.

    Accelerate application development with Amazon Aurora

    Databases are the backbone of modern applications and digital transformation. Developers like to focus on building applications, which is why Amazon Aurora is designed for unparalleled performance and scalability with minimal operational management. This session dives into the architecture and features of Amazon Aurora that provide developers a MySQL- and PostgreSQL-compatible database service without the typical database management overhead. Learn how Amazon Aurora Serverless provides an on-demand, auto-scaling configuration that automatically adjusts database capacity based on an application’s needs. Additional topics include how Aurora integrates with AWS services like Amazon SageMaker, AWS Lambda, and Amazon Redshift.

    Amazon OpenSearch Service: Open-source search and analytics flexibility

    Developers often embrace open source for the freedom it provides. Amazon OpenSearch Service is an open-source search and analytics suite born at AWS and developed with community contributors. In this talk, learn what OpenSearch does, how to use it with popular open-source services, how you can use it on and off AWS, and how you can participate to make it perfect for your particular use case.

    Run inference on real-time data streams from Amazon Kinesis

    In the race for digital transformation, customers are turning to inferring from real-time data for instant decision-making. In this session, see how to easily and securely connect Amazon SageMaker Studio notebooks to Amazon EMR to infer from real-time Amazon Kinesis data streams.

    Build scalable applications with purpose-built, NoSQL applications

    With the explosive data growth in the last 10 years, organizations are moving from monolithic applications to microservice architectures where they need to break down applications into smaller workloads. Adapting to this change, developers need to build on a solid, modern data platform to help address scalability, efficiency, performance, and total cost of ownership. AWS databases are purpose-built for modern applications, enabling developers to increase efficiency by using the right tool for the right job. In this session, review the portfolio of NoSQL databases that are optimized for specific data models (key-value, document, in-memory, and graph) and access patterns based on use cases to help you build modern applications on AWS.

    Migrate critical applications to increase business value

    Running custom or enterprise applications on premises often leads to challenges, such as capacity planning, as application demand continues to grow, issues with unplanned downtime emerge, and the cost of hardware refreshes grow. In this session, learn about AWS programs, tools, and best practices for migrating your critical applications to the cloud, and discover the business value offered by AWS technologies.

  • Track 4: Data Analyst + BI Professional

    Machine Learning for All

    Machine learning (ML) is driving innovation through more accurate predictions, reduced operational overhead, and improved customer experiences. The business results of ML applications have caused a significant demand for data science skills, and it’s hard for organizations to keep pace. In this session, we discuss putting ML within reach of more users – from embedding ML within business applications to enabling line of business analysts supporting finance, operations, marketing, sales, and operations to build their own models directly. We will show ML capabilities embedded in business intelligence services like QuickSight, explain how to you can create ML models using SQL with Redshift ML, and go from CSV files to predictive analytics using no-code service Amazon SageMaker Canvas.

    Unlock insights in your applications using Amazon QuickSight embedded analytics

    Users today expect insights to find them. They don't want to learn new tools or platforms to find answers to their business questions. By embedding interactive dashboards and visualizations, ML predictions and natural language query interfaces into your existing applications and portals, you can take insights to your users while keeping them in the flow of their daily work. Learn how Amazon QuickSight's 1-click embedding capabilities help you add insights to all your applications and portals - including wikis, SharePoint sites, CRM systems and more - powered by QuickSight's scalable, serverless, cloud-native architecture. Join this session to get started with embedding in minutes.

    Generate ML predictions with no code solutions from AWS (SageMaker Canvas)

    Amazon SageMaker Canvas is a visual, point-and-click service that makes it easy for business analysts to build ML models and generate accurate predictions without writing code or having ML expertise. In this session, learn how SageMaker Canvas can help you seamlessly access and combine data from a variety of sources, automatically clean data and apply a variety of data adjustments, and create ML models to generate predictions with a single click. Also learn how you can use SageMaker Canvas to easily publish results, explain and interpret models, and share models with others within your organization to improve productivity.

    Using natural language to answer business questions

    In a fast-paced world, it is critical for companies to make data-driven business decisions quickly without relying on business intelligence (BI) teams. Amazon QuickSight Q is a machine learning-powered capability that uses natural language processing to instantly answer business questions about data. Q interprets questions to understand their intent and generates an answer instantly, without requiring authors to create visuals, dashboards, or analyses. This session provides an overview of this new capability and how to get started.

    Accelerating Access to External Data by Removing ETL with AWS Data Exchange for Amazon Redshift

    Banks, Investment firms, Retailers, & Healthcare companies are making better decisions with more data. AWS customers across all industries are using AWS Data Exchange to more quickly find and integrate external data with no code, so they can build more predictive forecasts, measure risks in their supply chain, and monitor the spread of a virus in near-real time. In this session, see how you can subscribe to data in AWS Data Exchange, query it instantly in Amazon Redshift, and increase your speed to data-driven insight.

    End-to-end ML and data science workflows with Amazon EMR and SageMaker Studio

    Analyzing, transforming, and preparing large amounts of data is a foundational step of any data science and machine learning (ML) workflow. In this talk, we will demonstrate recent integrations between Amazon EMR – a cloud big data platform – and Amazon SageMaker Studio - the first fully integrated development environment (IDE) for machine learning (ML) – that make it simple for Data Scientists and Machine Learning Engineers to use distributed big data frameworks, such as Spark, in their machine learning workflow.

  • Track 5: Data Management Professional

    Building a modern data architecture on AWS

    Organizations are being challenged by the unprecedented scale of data as the amount of data under analysis increases from terabytes to petabytes and exabytes. In this session, learn about the current state of analytics on AWS, focusing on the latest service innovations. Also, learn about the importance of a modern data architecture and what’s to come in terms of scale, performance, security, and cost-effectiveness.

    Data management best practices

    When using a document database like Amazon DocumentDB (with MongoDB compatibility), the lack of an appropriate data archival strategy can result in increased cost and poor performance. By storing operational data within the document database and archiving data in a suitable storage service like Amazon S3, you can optimize both cost and performance. In this session, learn about common solutions to archive and purge data from Amazon DocumentDB as well as design considerations.

    Centralize governance for your data lake while enabling a modern data architecture

    When evaluating how to modernize your data platforms with Amazon Redshift, storage is only one component. It’s equally important to modernize your ETL—for simplicity, control, and cost benefits. This session shares how taking a holistic approach to modernizing your data platform—with Amazon Redshift, AWS Glue, and AWS Lake Formation for centralized governance—helps you accelerate your data-driven business insights and deliver secure, managed self-service analytics across the business.

    Securing personally identifiable information across data architecture

    Managing personally identifiable information (PII) across your data architecture can be complex and high risk. In this session, learn how you can apply sensitive data detection from AWS Glue with tag-based access control from AWS Lake Formation to automate the detection of PII and ensure fine-grained access control for that data for the right users.

    Prepare data for ML with ease, speed, and accuracy

    Join this session to learn how to prepare data for ML in minutes using Amazon SageMaker. SageMaker offers tools to simplify data preparation so that you can label, prepare, and understand your data. Walk through a complete data-preparation workflow, including how to label training datasets using SageMaker Ground Truth as well as how to extract data from multiple data sources, transform it using the prebuilt visualization templates in SageMaker Data Wrangler, and create model features.

    Ingest real-time data for batch analytics with Amazon Kinesis Data Streams

    In this session, learn how to evolve your Amazon Redshift data warehouse with real-time streaming analytics with Amazon Kinesis Data Streams. Many organizations want to augment their data warehouses and perform downstream processing and transformations of streaming data in real time using existing and familiar tools with little to no cost impact. Streaming ingestion support for Kinesis Data Streams in Amazon Redshift can help organizations achieve low latency while ingesting hundreds of megabytes of streaming data per second into their data warehouses. In this session, see how you can enable real-time analytics on your Amazon Redshift data warehouse without the need to stage data in Amazon S3.

  • Track 6: Industry Innovations

    Rethink possible: AWS for Data industry innovations

    Discover the innovation stories changing the way we live, work, play, look after the planet, and accelerate space exploration. Join this session to learn how machine learning, supercomputing, artificial intelligence, and robotics are powering manufacturing in space, enabling the growth of “super dust rice” to feed the world’s growing population, helping build the first human base camp on the moon, creating a new era of Formula 1 racing, and fighting climate change.

    Building a modern data strategy to improve patient care

    Hear from leaders at AWS about the role of health data in delivering personalized medicine. As health data grows and diversifies, clinicians and radiologists need easy access to data that is seamlessly integrated, aggregated, and visualized in applications and services across modalities and within their existing workflows. To realize this vision, they need an end-to-end data strategy to help unify data in the cloud and use advanced analytics and machine learning tools to inform insights, accelerate decision-making, and improve patient care. In this fireside chat, hear some of the top challenges facing healthcare organizations today and how technology is enabling innovation to unlock the potential of science and health data.

    Ryanair uses machine learning to innovate through disruption

    Ryanair, a leading European airline group, has continuously innovated with AWS through disruption to increase operational efficiencies and improve customer service. Learn how they used machine learning to reduce their carbon footprint, paper waste, and food waste and to enhance the customer experience.

    Data strategies to build a life sciences lab of the future

    Hear how leading life science organizations are balancing traditional “wet lab” environments while developing modernized digital lab data strategies. Learn how analytics, artificial intelligence, and machine learning (AI/ML) in the research lab can accelerate new discoveries while automating repetitive tasks; allowing scientists to free their time for value-add activities. Hear examples of how AWS and AWS partner solutions are helping life science orgs accelerate their time to science while complying with industry regulations.

    Supply chain and logistics

    This session provides an overview of the Amazon.com and AWS supply chain and logistics strategy, starting with an overview of the digital supply chain strategy that underlies the capabilities. Get an overview of Amazon supply chain global operations, automation, and scale as well as an overview of Amazon’s fulfillment center strategy, including robotics. Also, learn about the AWS supply chain for producing and delivering equipment to our global data centers. Join this session for guidance on how you can use AWS services to support the operation and forecasting of a digital supply chain.

Agenda

Dive deep into any of the 30+ business and technical sessions. Don’t forget to prepare your questions and get them answered live by our AWS experts.

Agenda overview

Frequently asked questions

Learn more about Data & Analytics on AWS

10,000

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data warehouse
on AWS

Leader in Gartner Magic Quadrant for Cloud AI Developer Services

550,000

databases migrated to AWS

3x

faster petabyte-scale analysis than standard Apache Spark

89%

of deep learning projects in the cloud run on AWS