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Run Apache Spark Structured Streaming jobs at scale on Amazon EMR Serverless
- Spark Structured Streaming simplifies streaming data processing by providing a high-level API that supports batch-processing-like jobs. Businesses can scale up and down their computing infrastructure as needed with Amazon EMR Serverless to enable Spark Structured Streaming to handle streaming data.
- Amazon EMR Serverless provides fine-grained scaling to allow for optimal throughput and cost optimization. Fine-Grained Scaling is needed for real-world predicaments where data volumes are unpredictable, and workloads have sudden spikes.
- Enhanced Fan-Out support is available on Amazon Kinesis connector, which is pre-packaged in Amazon EMR Serverless. Enhanced Fan-Out provides each consumer with dedicated throughput of 2 MBps per shard, allowing for faster, more efficient data processing, which boosts the overall performance of streaming jobs on EMR Serverless
- Amazon EMR Serverless ensures resiliency in streaming jobs by leveraging automatic recovery and fault-tolerant architectures. Automatic event retry is also available with EMR Serverless for tackling transient runtime failures.
- EMR Serverless provides robust log management and enhanced monitoring for streaming jobs. The platform is integrated with Amazon Managed Service for Prometheus, enabling detailed engine metrics to be monitored, analyzed, and optimized.
- EMR Serverless supports Kinesis Data Streams, Amazon MSK, and self-managed Apache Kafka clusters as input data sources to keep up with diverse data processing pipelines accurately.
- Using Spark Structured Streaming on EMR Serverless is an efficient and cost-effective solution for real-time data processing. With the ease of integration with AWS services and automated resiliency features, it provides high availability and reliability, minimizing downtime and data loss.
- Anubhav Awasthi, Kshitija Dound, and Paul Min are AWS Solutions Architects who have co-authored this article.
- Organizations may try out Spark Structured Streaming on EMR Serverless and optimize it for their specific needs using the advanced monitoring tools. Comment with questions regarding use cases.
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