Amazon EMR Serverless now supports larger worker sizes to run more compute and memory intensive workloads
Amazon EMR Serverless now offers larger worker configurations of 32 vCPUs with up to 244 GB of memory, allowing you to run more compute and memory-intensive workloads. Previously, the largest worker configuration available on EMR Serverless was 16 vCPUs with up to 120 GB of memory. Larger workers can help you improve the runtime performance as well as cost profiles for your workloads. For shuffle-heavy workloads, larger workers reduce inefficient data transfers between executors. For jobs with data skew, larger workers reduce the chances of out-of-memory failures. For jobs that need to cache d
The continuous demand for processing larger and more complex datasets is driving cloud providers to enhance their serverless compute offerings.
This update allows businesses to run more demanding analytics workloads on serverless infrastructure, potentially lowering costs and improving performance for specific compute-intensive tasks.
EMR Serverless users can now leverage significantly larger worker configurations, enabling them to tackle more data-intensive analytics problems without managing underlying infrastructure.
- · AWS EMR Serverless users
- · Analytics/Big Data companies
- · Cloud-native enterprises
- · Organizations with legacy on-premise data analytics infrastructure
Increased adoption of serverless big data processing for more complex workloads due to enhanced capabilities.
Potential for further innovation in analytics applications as the compute constraints on serverless platforms are alleviated.
Reduced operational overhead for data engineering teams, allowing them to focus more on data insights and less on infrastructure management.
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Read at AWS What's New