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.
This is a builder focused event that includes presentations, demos, and hands-on workshops. This event is focused on four key areas – Aurora Operational Best Practices and New Features, Aurora Serverless v2, GenAI and PostgreSQL Vector Database, and Babelfish for Aurora PostgreSQL.
Why attend?
Learn the benefits of modernization with Aurora PostgreSQL and MySQL. Explore Aurora operational best practices with new features and cost optimization options. Build event-driven applications using Amazon Aurora Serverlessv2. Enhance MySQL workloads with Analytics capabilities using Amazon Aurora Zero-ETL to Amazon Redshift. Understand how to use vector databases with Large Language Models (LLMs) and Generative AI(GenAI) with PostgeSQL pgvector. Experience how open source Babelfish accelerates modernization of SQL Server applications to Aurora PostgreSQL.
Please register with your corporate email. Personal emails will not be accepted for registration. Thank you!
Amazon Aurora (Aurora) is a fully managed relational database engine that's compatible with MySQL and PostgreSQL. Aurora combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases.
Explore the benefits of some of the most exciting new features from Aurora: Serverless v2, Database Clonining, HA/DR, Zero-ETL Integration, and Blue/Green Deployments.
Learn how to enable cost optimization best practices using the new features including Graviton-3, I/O Optimized, Serverless v2, and more when scaling with Aurora.
Event-driven architecture is a software architecture promoting the production, detection, and consumption of events, as well as the reaction to them. It provides increased availability, elasticity, scalability, and cost optimization by promoting microservices, which are small, specialized services performing a narrow set of functions that can independently scale to meet demand.
Learn how to build an event-driven serverless application using Amazon Aurora Serverless v2 as a relational, persistent database (MySQL and PostgreSQL) supporting your microservices. You will also observe how Aurora Serverless v2 can seamlessly scale and keep up with the workload.
Modernize your AI/ML Journey with Amazon Aurora: Build AI-powered apps on Amazon Aurora PostgreSQL with pgvector, LangChain & LLMs
Amazon Aurora PostgreSQL-Compatible Edition supports the pgvector extension, bringing the power of vector similarity search and storing embeddings in your Aurora PostgreSQL databases. A common application of Generative AI and large language models (LLMs) in an enterprise setting is to answer questions based on the enterprise’s knowledge corpus. Document Question Answering (DQA) is a crucial task in Natural Language Processing (NLP), aiming to develop automated systems capable of understanding and extracting relevant information from documents to answer user queries.
With recent advancements in LLMs and innovative tools and technologies such as LangChain, a new integrated approach to DQA called Retrieval Augmented Generation (RAG) has emerged. This integrated approach combines the power of LLMs for natural language understanding and generation, LangChain for document processing, and pgvector for efficient vector storage and retrieval.In this session, we explore the integration of these cutting-edge technologies and discuss how they collectively enhance DQA systems via the RAG approach.
We delve into the working principles of LangChain, LLMs, and pgvector, and demonstrate their collaborative potential in solving DQA challenges. We will highlight the benefits and implications of this integrated approach, paving the way for improved information retrieval and interactive document exploration. In addition, you will also learn how pgvector allows you to store & search vector embeddings for exact and approximate nearest neighbor.
Option 1: Enhance your MySQL workload with Analytics capabilities using Amazon Aurora Zero-ETL to Amazon Redshift
Data has gravity. Companies around the world have diverse analytical needs. That is why Amazon Aurora has built a Zero-ETL feature to help customers perform ETL to seamlessly migrate your data to Redshift for analytical and ML purposes. In this session, we will discuss how to leverage Zero ETL to help your business handle data analytical needs without the heavy lifting of building an ETL process.
We will demonstrate how Zero-ETL works and provide you with some best practices on how to use Zero-ETL with the help of Database Specialists.
Explore how open source Babelfish accelerates the migration and modernization of SQL Server applications to Aurora PostgreSQL with minimal code changes.
Babelfish for Aurora PostgreSQL includes support for SQL Server wire-protocol and T-SQL, which is the query language used in Microsoft SQL Server. Developers can use Babelfish to run their existing SQL Server applications on Amazon Aurora PostgreSQL-Compatible Edition without having to switch the database drivers or completely rewrite their queries.
Run Babelfish COMPASS Assessment Tool in minutes, analyze the SQL Server workloads with COMPASS Assessment report, identify Babelfish for Aurora PostgreSQL candidates, and get in-depth guidance from AWS. Experience the new Babelfish Modernization Workshop V3 for Customers and Partners with hands-on lab for builders. The workshop is designed to accelerate successful deployments of Babelfish PoCs with real-world applications.
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