Advanced Serverless Architectural Patterns on AWS
summary
Claims that serverless removes servers and scale to zero while charging by 100ms and that cold starts are a few hundred milliseconds via Firecracker microVMs. Uses AWS Lambda, API Gateway, CloudFront/Lambda@Edge, DynamoDB, Kinesis Firehose/Streams, Step Functions, Rekognition/Transcribe/Comprehend and Athena. Shows an Athena query: 170GB → 44s → $0.85 and a Lambda power‑tuning tool to pick memory (128MB–3GB) for cost/perf. Describes a working pipeline: S3 triggers → Step Functions orchestration → parallel Rekognition/Transcribe/MediaConvert → store in Elasticsearch/DynamoDB, deployable via CloudFormation.
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chapters (31)
- 0:00Start
- 1:47About me
- 2:24Agenda
- 7:53Compute Spectrum
- 9:58Serverless means...
- 12:11Lambda: The execution lifecycle
- 13:19Tune your function's resources
- 15:40AWS Serverless Application Model (SAM)
- 16:27AWS code services
- 17:20Web application (1)
- 19:46Web application (2)
- 23:32Choose the right API endpoint type
- 23:54Serverless web app security
- 26:40Lambda authorizers
- 29:59Streaming data ingestion (HTTP)
- 30:17Streaming data ingestion (at the edge)
- 31:05Kinesis Data Streams and Lambda
- 32:03Fan-out pattern
- 33:00Real-time analytics
- 34:19Kinesis Data Analytics
- 35:10Data lake characteristics
- 36:28Serverless data lake
- 37:02How to "serverlessly" query your data lake
- 37:45Athena - Just SQL (presto)
- 39:30Athena best practices
- 40:37THE AWS ML STACK
- 41:55Image processing with Amazon Rekognition Image
- 43:16Media analysis solution
- 44:44Amazon Connect
- 45:02Intelligent call center chatbot
- 46:05Call center analytics
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