Title

AWS re:Invent 2023 - Powering self-service & near real-time analytics with Amazon Redshift (ANT211)

Summary

Naresh Chanani, Engineering Director for Redshift, introduces the session on self-service analytics with Amazon Redshift.
Kate Kim from FanDuel discusses how they overcame challenges using Redshift.
Debu Panda, Senior Manager, demonstrates the consumption layer and the integration of ML and analytics.
The session covers the importance of self-service analytics and the evolution towards near real-time analytics.
Redshift Serverless is highlighted for its simplicity and ability to meet customer requirements for performance, budget, and security.
New features like auto copy from S3 and Redshift Streaming Ingestion are introduced.
Zero ETL and data sharing capabilities are discussed to avoid unnecessary data movement.
Multi-cluster architectures, such as hub and spoke and data mesh, are explained.
Kate Kim shares FanDuel's journey from a single data warehouse to a multi-cluster architecture using Redshift features, resulting in improved performance and cost savings.
Debu Panda showcases Redshift ML for SQL users to build machine learning models and demonstrates the Query Editor V2 and SQL notebooks.
A live demo is presented, showing real-time data ingestion from Kinesis, machine learning model creation, and fraud detection using Redshift ML.

The shift towards self-service analytics is driven by the need for business analysts and data users to focus on outcomes without worrying about infrastructure.
Redshift Serverless has grown rapidly due to its ease of use and ability to automatically manage scaling, snapshots, and data security.
AWS emphasizes avoiding data movement for analytics, preferring data sharing and federation, and introduces features to simplify data ingestion pipelines.
The move from batch to near real-time analytics is critical for interactive use cases and AI applications, with Redshift Streaming Ingestion enabling low-latency, high-throughput data processing.
Multi-cluster architectures are becoming more common, with hub and spoke being used by many customers and data mesh by more advanced enterprises.
FanDuel's case study illustrates the benefits of transitioning to a multi-cluster architecture, including improved query efficiency, reduced costs, and increased revenue.
Redshift ML democratizes machine learning by allowing SQL users to create and deploy models without needing to know Python or other ML tools.
The Query Editor V2 and SQL notebooks enhance the user experience for data analysts, providing easy data loading, version control for SQL queries, and collaboration tools.
The live demo underscores the practical application of AWS analytics and Redshift features in a real-world scenario, demonstrating the power and flexibility of AWS services for real-time analytics and machine learning.