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Use Sidecar Pattern in AWS Lambda To Optimize Your Microservices Architecture

Dzone - DevOps

Microservices architecture is becoming increasingly popular as it enables organizations to build complex, scalable applications by breaking them down into smaller, independent services. Each microservice performs a specific function within the application and can be developed, deployed, and scaled independently.

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Automate the process to change image backgrounds using Amazon Bedrock and AWS Step Functions

AWS Machine Learning - AI

The DynamoDB update triggers an AWS Lambda function, which starts a Step Functions workflow. The Step Functions workflow invokes a Lambda function to generate a status report. Image processing workflow When the DynamoDB table is updated, DynamoDB Streams triggers a Lambda function to start a new Step Functions workflow.

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Accenture creates a regulatory document authoring solution using AWS generative AI services

AWS Machine Learning - AI

The WebSocket triggers an AWS Lambda function, which creates a record in Amazon DynamoDB. Another Lambda function gets triggered with a new message in the SQS queue. The Lambda function reads the job ID and invokes an AWS Step Functions workflow for processing data files. The response data is stored in DynamoDB.

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Dynamic Data Processing Using Serverless Java With Quarkus on AWS Lambda (Part 1)

Dzone - DevOps

This was not only about rewriting applications, but the backend data stores were also redesigned in terms of dynamic scalability , high performance, and flexibility for event-driven architecture.

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Optimizing Medication Management: How AWS ETL Transforms Healthcare Data for a Leading PBM

Perficient

Scalability Demands As the volume of data grows, the systems have to handle & manage the data without compromising on performance. S3 provides availability, security, and scalability, all of which come at a significantly low cost. PySpark helps in performing fast distributed transformations on large data sets.

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Fundamentals of Data Engineering

Xebia

Building architectures that optimize performance and cost at a high level is no longer enough. Understand the cultural foundations of Agile, DevOps, and DataOps. Architect for scalability. First, scalable systems can scale up to handle significant quantities of data. Plan for failure. Architecture is leadership.

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Can’t-miss sessions for AWS Summit Chicago

Stackery

Performing Serverless Analytics in AWS Glue. Shah of AWS gives a tour of the features and how it enables you to run arbitrary Python or Spark in a scalable environment. A Culture of Rapid Innovation with DevOps, Microservices, and Serverless. Scalable Serverless Architectures Using Event-Driven Design.

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