Mackenzie is the Global Startup Evangelist at AWS. His days are spent traveling the globe to meet startups, share their stories, and connect engineering teams together. Every day there are a large number of startups launching on AWS across every imaginable industry. It’s Mackenzie’s mission to find stories of startups that are helping to improve the world and share these stories with a wide audience.
Join us at the AWS Databases & Analytics Day and see firsthand how AWS can help your organization plan and build the next generation data foundation in the era of AI. We have three specific tracks with tailored content to advance your learning: 1/ Databases, 2/ Analytics & Big Data, and 3/ Executive Track.
In the Executive track, you’ll learn from AWS Data experts on best practices for creating and implementing a modern data strategy, data foundation, and data governance model to scale your data, analytics, AI/ML, and generative AI innovations across your organization.
In the technical tracks, you’ll learn from leading AWS experts who will dive deep into the AWS Databases & Analytics services that are powering data ecosystems for thousands of customers. We will delve into using generative BI capabilities to create compelling stories in Amazon QuickSight, show how you can leverage our vector databases for generative AI applications, integrate data with zero-ETL capabilities for analytics and machine learning use cases, and build highly performant and resilient applications with Amazon Aurora and so much more! AWS customer spotlights will enable you to learn from other customers on their experiences and guidance using managed AWS Databases & Analytics services.
Register to immerse yourself in the future of data and AI, and connect with hundreds of data innovators like yourself eager to share their insights.
The Executive track is targeted for CIOs, CTOs, CDOs, CDAOs, and senior data and analytics leaders looking to establish a data strategy and cloud-based data foundation within their organization to drive transformational business value.
The technical tracks are for developers, DBAs, and Data Architects playing a critical role within their organization to build complex modern applications. We expect attendees to have working knowledge of relevant AWS services (Level 300+) with the familiarity of using AWS Console and CLI.
Specifically, L300 sessions assume the audience is familiar with the topic but may or may not have direct experience implementing a similar solution. L400 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. Presenters in these sessions dive into code, cover advanced tricks, and explore future developments in the technology.
The agenda by track is listed below, and we cap off this event with a complimentary happy hour!
PostgreSQL makes it easier to store and query vector data for AI/ML use cases with the pgvector extension. Learning best practices for vector search will help you deliver a high-performance experience to your customers. In this session, learn how to store data from Amazon Bedrock in an Amazon Aurora PostgreSQL and learn what SQL queries and tuning parameters optimize the performance of your application when working with AI/ML data, vector data types, exact and approximate nearest neighbor search algorithms, and vector-optimized indexing.
In this session, we will deep dive into AWS NoSQL database services that power mission-critical workloads for Amazon.com and customers alike. We will showcase the evolution of application architecture and data patterns needed for generative AI applications, and a demo of how vector search can optimize and enhance performance of generative AI workloads with low query latency performance and high accuracy, highlighting DocumentDB vector search and MemoryDB vector search.
Amazon Aurora is a fully managed relational database designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Aurora provides managed high availability (HA) and disaster recovery (DR) capabilities in and across AWS Regions. In this session, explore the Aurora HA and DR capabilities and discover design patterns that enable the development of resilient applications. Learn how to establish in-Region and cross-Region HA and DR utilizing Aurora features, including Multi-AZ deployments, Amazon Aurora Global Database, and Amazon RDS Proxy, and how to reduce failover times with a JDBC driver.
With an innovative architecture that decouples compute from storage and advanced features like Global Database and low-latency read replicas, Amazon Aurora reimagines what it means to be a relational database. Aurora is a modern database service offering unparalleled performance and high availability at scale with full open source MySQL and PostgreSQL compatibility. In this session, dive deep into the most exciting new features Aurora offers, including Aurora I/O-Optimized, Aurora zero-ETL integration with Amazon Redshift, and Aurora Serverless v2. Learn how the addition of the pgvector extension allows for the storage of vector embeddings and support of vector similarity searches for generative AI.
In the rapidly advancing domain of generative artificial intelligence (AI), the necessity for instantaneous data access and processing is paramount. This session delves into the critical enhancements that in-memory data stores, specifically Amazon ElastiCache and Amazon MemoryDB, bring to generative AI applications. Amazon ElastiCache Serverless provides an efficient caching solution enabling scalable and rapid data access providing memory which is a key element in application built on LLM. Amazon MemoryDB offers fastest vector database experience on AWS. Participants will gain insights into the architecture, performance metrics, and real-world applications of how these in-memory services can save cost while improving performance.
Amazon RDS is a fully managed database service that helps you launch an optimally configured, more secure, and highly available database with just a few clicks. It manages database administration tasks so you can focus on your applications. In this session, you will learn about the latest commercial and open source innovations. Recent launches such as Amazon RDS for Db2 support, Amazon RDS Custom for SQL Server, Bring Your Own Media with Amazon RDS Custom for SQL Server, vector database capabilities to support your generative AI applications, and zero ETL integration between Amazon RDS MySQL and Amazon Redshift will be covered.
Leveraging the power of OpenSearch Service’s vector engine, AWS customers are delivering feature rich search experiences for their customers. OpenSearch Service provides multi-modal search, semantic search, and hybrid search capabilities. In addition, with the scale and performance of OpenSearch Service, it is ideally suited for Retrieval Augmented Generation (RAG) for generative AI ensuring chatbots and interactive AI applications deliver accurate responses. Join this session to learn how to implement next generation search techniques using a proven solution – Amazon OpenSearch Service.
Streaming data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (kilobytes). Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, ecommerce purchases, in-game player activity, information from social networks, financial trading floors, or geospatial services, and telemetry from connected devices or instrumentation in data centers. The importance of streaming data is increasing with the emergence of generative AI as customers seek to feed data from their streaming workloads to pre-train foundational models (FMs) and also derive real-time insights and improve real-time customer engagement.
You can infuse generative AI into how your business users interact with data. In this session, learn how generative BI capabilities in Amazon QuickSight allow business analysts to author dashboards using natural language and how business users can easily dive deep into data by simply asking questions. Discover how business users can also use generative BI capabilities to quickly create compelling stories to drive decision-making, while developers can integrate these capabilities into applications to differentiate and monetize data like never before.
AWS Glue is a serverless data integration service for easy to discover, prepare, and combine data for analytics, machine learning, and application development. As customers are making their cloud journey, they want to migrate and modernize their legacy on-premise ETL workloads to AWS Glue. In this session we will discuss the benefits of ETL Modernization with AWS Glue, including customer success stories.
Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads. Customers use Amazon Redshift as a key component of their data architecture to drive use cases from typical dashboarding to self-service analytics, real-time analytics, machine learning (ML), data sharing and monetization, and more. This session will discuss the benefits of data warehouse modernization with Amazon Redshift, including customer case studies.
Transactional data lakes are gaining popularity in modern data platforms as they enable a variety of use cases such as Change Data Capture (CDC) and compliance to regulations like GDPR that were previously difficult and time-consuming to achieve in a traditional data lake with immutable objects. AWS supports open-table formats like Apache Hudi and Apache Iceberg that allow customers to combine analytical operations like record-level insert, update, delete, and time travel queries with the flexibility of Amazon S3 data lakes. In this session, learn how to build a transactional data lake with open-table formats on AWS and process and consume data at scale with AWS analytics services such as Amazon EMR and Amazon Athena.
We'll discuss the various aspects of how mindset, people, process, and technology all contribute to building a successful modern data strategy that achieves business-visible outcomes, ensures the greatest return on investment, and puts technology leaders in the best position to utilize generative AI capabilities on AWS.
Data governance with AWS helps organizations accelerate data-driven decisions by connecting the right people and applications to securely and safely find, access, and share the right data when they need it. Attend this session to learn how you can curate data by automating data integration and data quality to limit the proliferation of data, to discover and understand your data with centralized catalogs that boost data literacy, and to protect your data with precise permissions to share data with confidence. In this customer panel, learn how AWS customers have implemented data governance and how they are meeting new trends like generative AI.