Architecture patterns to optimize Amazon Redshift performance at scale
Big Data Blog
This article discusses five architecture patterns to optimize Amazon Redshift performance at scale:
- Amazon Redshift Serverless: Automatically provision and scale data warehouse capacity without managing infrastructure
- Amazon Redshift Data Sharing: Securely share live data between separate Redshift data warehouses without moving data
- Amazon Redshift Spectrum: Query data directly in Amazon S3 without loading into Redshift tables
- Zero-ETL Integration: Unify data across databases and data warehouses with near real-time analytics
- Streaming Data Ingestion: Ingest streaming data from Kinesis or Apache Kafka for near real-time analytics
These patterns help organizations dynamically scale their data warehouses, accommodate different workloads, and optimize price performance across various data sources and volumes.
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