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 first AWS Databases & Analytics Day and see firsthand how AWS can help your organization build the next generation data foundation in the era of AI. 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 cover databases and analytics use cases, innovative features, integrations, and analytics offerings to help you capitalize on your data for decision making.
You will increase your technical skills with deep dives and demos across end-to-end Database, Analytics and Big Data. The event will cover:
* GenBI with Amazon QuickSight
* Amazon Aurora
* Amazon RDS
* Amazon DynamoDB
* Amazon ElastiCache
* Amazon OpenSearch Service
* Amazon Redshift
* AWS Glue...and more.
We will delve into using generative BI capabilities to quickly 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.
This event is for developers, DBAs, and Data Architects playing a critical role within their organization to build complex modern applications. You’ll get the opportunity to dive deep on AWS Databases & Analytics offerings through in-depth presentations with experts on-hand to help demo and test out our services. We expect attendees to have working knowledge of relevant AWS services (Level 200+) with the familiarity of using AWS Console and CLI.
Specifically, L300 sessions assume that 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.
We have three specific tracks with tailored content to advance your learning: 1/ Databases, 2/ Analytics & Big Data, and 3/ Executive Track. The agenda by track is listed below, and we cap off this event with a complimentary happy hour!
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.
Amazon DynamoDB is a popular choice for modern applications, as it is a serverless database that provides single-digit millisecond performance at any scale. Optimizing your usage of DynamoDB requires a different approach to data modeling than traditional relational databases. In this session, we show you advanced techniques to get the most out of DynamoDB. Learn how to “think in DynamoDB” by learning the DynamoDB foundations and principles for data modeling. Further, learn practical strategies and DynamoDB features to handle difficult use cases in your application.
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.
Do you need simplified storage for your database cloud migration or to swiftly test database operations prior to executing a migration against your production database? Join this interactive presentation that demonstrates some of the advanced snapshot and cloning features of Amazon FSx. Learn how they can reduce your RPO/RTO from hours to seconds, no matter the size of your database. Also, learn how to use zero-space cloning features to instantly provide each of your developers with their own individual copies of your database.
Amazon RDS is a fully managed relational database service that automates time-consuming database administration tasks. Amazon RDS Custom offers additional flexibility and control of the underlying operating system and database environment, ideal for applications that require customizations. In this session, learn about new features and best practices, including migration via RMAN Transportable Tablespaces, options for multi-tenancy, deployment of Oracle E-Business Suite, customer-supplied licenses (that is, Bring Your Own Media licensing) for SQL Server, Active Directory integration, and more.
Database caching improves performance and can significantly reduce your cloud spend. Join us to discover how you save up to 50%+ in database cost and gain up to 80x faster query performance using Amazon ElastiCache with either self-managed relational databases on EC2 or RDS, freeing up investment for generative AI applications while making your data infrastructure more performant. With ElastiCache Serverless, you can get started in under a minute with a distributed in-memory cache that instantly scales to support unpredictable application traffic patterns and the low latency and high throughput needs of generative AI. In this session, learn about the virtues of Redis, the financial advantages of caching relational databases, and how to identify which of your workloads would benefit the most from caching.
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.
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.
To stay competitive, allowing data citizens across your organization to see near real-time analytics without worrying about data infrastructure management is crucial for your business. In this session, learn how your data users can get to near real-time insights on streaming data with Amazon Redshift and AWS streaming data services. Explore a solution using flexible querying tools and a serverless architecture, which brings intelligent automation and scaling capabilities, and maintains consistently high performance for even the most demanding and volatile workloads.
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.
AWS Glue is a serverless data integration service that makes it 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-premises ETL workloads to AWS Glue. In this session ,we will discuss the benefits of ETL Modernization with AWS Glue, including customer success stories.
n this session, we highlight practical and actionable mechanisms that technology leaders can use to manage complex change and drive a data migration strategy with AWS that achieves business-visible outcomes, ensures the greatest return on investment, and puts them 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.
