Hero guide: Data and analytics
This guide highlights sessions at re:Invent 2024 that cater to various levels of expertise with the primary objective of driving innovation in businesses by promoting data-driven strategies and governance control. It covers focus areas such as data analytics, data foundation, and generative AI.
Sanchit Jain
AWS Data Hero
The data and analytics track at re:Invent 2024 is designed to emphasize the vital role that both disciplines play in driving innovation and business success. Recognizing the complexities of data operations, this guide focuses on sessions that empower data professionals to build robust data foundations, leveraging AWS services for analytics and exploring generative AI applications.
By aligning with AWS Well-Architected pillars, such as operational excellence, performance efficiency, cost optimization, reliability, and sustainability, participants gain insights to create resilient and effective data strategies that enhance decision-making and customer experiences.
There’s a ton of great content this year. These sessions vary from intermediate (200) to expert (400) levels and help you accelerate the pace of innovation in your business.
Keynote
*Walk-up only: No reserved seating required
KEY002 | CEO Keynote with Matt Garman
Join AWS CEO Matt Garman to hear how AWS is innovating across every aspect of the world’s leading cloud. He explores how we are reinventing foundational building blocks as well as developing brand new experiences, all to empower customers and partners with what they need to build a better future.
Breakout session
DAT420 | Achieving scale with Amazon Aurora PostgreSQL Limitless Database
Amazon Aurora is a relational database service built for the cloud that is designed for unparalleled high performance and availability at global scale, with full MySQL and PostgreSQL compatibility. In this session, learn how Amazon Aurora PostgreSQL Limitless Database enables applications to scale to millions of transactions per second across petabytes of data. Explore the architecture, distributed transaction management, and serverless scaling capabilities of Aurora PostgreSQL Limitless Database. Also, discover application patterns that are a good fit for Aurora PostgreSQL Limitless Database and which patterns to avoid. Learn how Aurora PostgreSQL Limitless Database makes it easier than ever to scale Aurora.
BSI101 | Reimagine business intelligence with generative AI
In this session, get an overview of the generative AI capabilities of Amazon Q in QuickSight. Learn how analysts can build interactive dashboards rapidly, and discover how business users can use natural language to instantly create documents and presentations explaining data and extract insights beyond what’s available in dashboards with data Q&A and executive summaries. Hear from Availity on how 1.5 million active users are leveraging Amazon QuickSight to distill insights from dashboards instantly, and learn how they are using Amazon Q internally to increase efficiency across their business.
DAT319 | A practitioner’s guide to data for generative AI
In this session, gain the skills needed to deploy end-to-end generative AI applications using your most valuable data. While this session focuses on the Retrieval Augmented Generation (RAG) process, the concepts also apply to other methods of customizing generative AI applications. Discover best practice architectures using AWS database services like Amazon Aurora, Amazon OpenSearch Service, or Amazon MemoryDB along with data processing services like AWS Glue and streaming data services like Amazon Kinesis. Learn data lake, governance, and data quality concepts and how Amazon Bedrock Knowledge Bases, Amazon Bedrock Agents, and other features tie solution components together.
STG201 | Build a data foundation to fuel generative AI
Data has long been considered a strategic asset by organizations, but generative AI puts a renewed emphasis on the importance of a data strategy. Whether you are building your own model or customizing a foundation model, your data is a key differentiator for generative AI, so you need a data strategy that supports relevant, high-quality data. In this session, learn about the data tools that can fuel your generative AI strategy and the common data patterns required to transform your generative AI application from a generic tool to a program that truly knows your business and your customer.
ANT405 | Data engineering for ML and AI with AWS analytics
The performance and accuracy of AI and ML systems directly depend on the quality, relevance, and integrity of the data, which are used to either train the model or accessed as part of Retrieval Augmented Generation (RAG) systems for generative AI assistants. Data engineering plays a crucial role in ensuring high-quality data is available, accessible, and usable for successful AI and ML implementations, allowing models to learn, reason, and act effectively. This session explores how AWS analytics provides complete engineering solutions for ingesting, storing, processing, and integrating data and making the data accessible for generative AI chat assistants and other AI and ML applications.
ANT302 | Data foundation in the age of generative AI
An unparalleled level of interest in generative AI is driving organizations of all sizes to rethink their data strategy. While there is a need for data foundation constructs such as data pipelines, data architectures, data stores, and data governance to evolve, there are also business elements that need to stay constant such as organizations wanting to be cost-efficient while efficiently collaborating across their data estate. In this session, learn how laying your data foundation on AWS provides the guidance and the building blocks to balance both needs and empowers organizations to grow their data strategy for building generative AI applications.
ANT303 | Explore what’s new in analytics and governance
Join this session to explore the latest data governance innovations and features in AWS analytics. Our experts guide you through the latest innovations in Amazon DataZone, AWS Lake Formation, and AWS Glue that are helping organizations establish robust data governance frameworks and maintain compliance standards.
Builders' session
PEX403 | Mainframe modernization powered by generative AI on AWS
This builders’ session offers a comprehensive exploration of mainframe modernization and hands-on experience using generative AI services on AWS. Learn how to use AWS Partner solution AveriSource and integrate with Amazon Bedrock for both discovery and code analysis to gain valuable insights into existing mainframe application codebase and data structure. Also, discover how to use Amazon Bedrock to create test data, enhancing the testing process for modernization projects. Finally, explore Amazon Q capabilities in fine-tuning and refining the generated code from AveriSource, ensuring it meets specific requirements and standards. You must bring your laptop to participate. This builders’ session is intended for AWS Partners.
Chalk talk
MFG301 | Design an industrial data foundation for generative AI in manufacturing
This chalk talk dives deep into the architectural patterns that underpin an industrial data strategy. This foundation is critical to enabling the application of generative AI for use cases such as assisted diagnosis and troubleshooting, advanced quality defect detection, and automated information modeling and digital threads. In addition to sharing and whiteboarding architectural patterns for operational and enterprise data ingestion, data storage and transformation, and contextualization, the talk showcases generative AI manufacturing use cases with live demos and code samples.
ANT305 | Strategies for efficient zero-ETL integrations
In this chalk talk, learn techniques for extracting data from diverse sources, advanced transformation methods for data cleansing and enrichment, and optimized loading into data warehouses and data lakes. Real-world use cases showcase how organizations have overcome ETL challenges and unlocked the potential of their data assets. Gain comprehensive insights into streamlining ETL processes, handling large-scale data volumes, ensuring scalability and performance, and using ETL to drive data-driven decision-making, foster innovation, and gain a competitive edge.
Code talk
DAT413 | Using generative AI to accelerate database modernization with AWS DMS
Does the thought of modernizing your application and database architecture fill you with concern? In this code talk, learn how to overcome common migration issues and concerns that arise during modernization projects. See live coding examples to learn how to use generative AI and the AWS DMS Schema Conversion feature to successfully modernize your database.
Workshop
ANT308-R | Build and govern your data mesh with Amazon DataZone [REPEAT]
A data mesh is a distributed, domain-driven architecture that defines responsibilities and coordination across separate domain teams and data products. A data mesh requires data governance to ensure data quality, consistency, security, and trust across domains. In this workshop, learn how to build with Amazon DataZone to help manage and govern your data mesh. Explore automating data discovery, cataloging, and sharing; analyzing data quality; visualizing data lineage; and governing data access to meet objectives. Through hands-on exercises, gain practical experience designing, deploying, and maintaining a well-governed data mesh solution using Amazon DataZone. You must bring your laptop to participate.
PEX302-R | Data foundations and Amazon Q Business generative AI workshop [REPEAT]
Successful generative AI projects are built on solid data foundations that include data quality, data privacy, and data governance tools and techniques. Join this workshop to get hands-on experience building a chatbot for a regulated scenario, such as financial services. Gain insights on data enrichment and cataloging tools, and expose your chatbot to customer interaction with Amazon Q Business. You must bring your laptop to participate. This workshop is intended for AWS Partners.
ANT404 | Migrating from self-managed Apache Kafka to Amazon MSK
Migrating between Apache Kafka clusters while ensuring minimal downtime can be challenging when replicating large amounts of data. This workshop demonstrates a proven method for building a resilient migration pipeline to move data from an Apache Kafka cluster to Amazon MSK. Gain hands-on experience with practical strategies and code examples to simplify real-world replication and migration use cases. You must bring your laptop to participate.
ANT311 | Prepare your data for generative AI
Generative AI applications need data from diverse data sources and, for an optimal experience, require the data to be reliable, trustworthy, and well-governed. Join this hands-on workshop to explore a robust data foundation on AWS for an end-to-end generative AI experience. Learn how to prepare your data for generative AI with batch and real-time data pipelines, high data quality, vector data management for custom needs, and integrated data governance. You must bring your laptop to participate.
Closing comments
I hope you find this guide helpful and I look forward to connecting with you at re:Invent during the hallway track. For those attending in person, I suggest prioritizing participation in workshops and chalk talks as they won't be available online later. Make sure to wear comfortable shoes due to the extensive walking, and stay hydrated as the event takes place in a desert climate. The concluding conference event, re:Play, is an exciting party with live music, so consider bringing earplugs for noise control.