AWS Compute Blog

James Beswick

Author: James Beswick

James Beswick is a Principal Developer Advocate for the AWS Serverless Team. He works with AWS's developer customers to understand how serverless technologies can drastically change the way they think about building and running applications at massive scale with minimal administration overhead. Follow James Beswick on Twitter: Visit for more serverless content.

Create function page

Python 3.9 runtime now available in AWS Lambda

You can now create new functions or upgrade existing Python functions to Python 3.9. Lambda’s support of the Python 3.9 runtime enables you to take advantage of improved performance and new features in this version. Additionally, the Lambda service now runs the code before the handler, supports TLS 1.3, and provides enhanced logging for errors.

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Kinesis producers and consumers

Understanding data streaming concepts for serverless applications

In this post, I introduce some of the core streaming concepts for serverless applications. I explain some of the benefits of streaming architectures and how Kinesis works with producers and consumers. I compare different ways to ingest data, how streams are composed of shards, and how partition keys determine which shard is used. Finally, I explain the payload formats at the different stages of a streaming workload, how message ordering works with shards, and why idempotency is important to handle.

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Monitoring the Kinesis stream

Monitoring and troubleshooting serverless data analytics applications

In this post, I show how the existing settings in the Alleycat application are not sufficient for handling the expected amount of traffic. I walk through the metrics visualizations for Kinesis Data Streams, Lambda, and DynamoDB to find which quotas should be increased.

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Solution architecture

Building leaderboard functionality with serverless data analytics

In this post, I explain the all-time leaderboard logic in the Alleycat application. This is an asynchronous, eventually consistent process that checks batching of incoming records for new personal records. This uses Kinesis Data Firehose to provide a zero-administration way to deliver and process large batches of records continuously.

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