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awsMar 30, 20263 min readCurated summary

AWS Weekly Roundup: AWS AI/ML Scholars program, Agent Plugin for AWS Serverless, and more (March 30, 2026) | Amazon Web Services

The March 30, 2026 AWS Weekly Roundup highlights the new AWS AI & ML Scholars program, which will offer free generative AI education to up to 100,000 people and fully funded Udacity Nanodegrees to 4,500 top participants. It also emphasizes new tools for serverless development, SageMaker IDE integration, expanded Lambda Managed Instance capacity, and streaming speech synthesis. AWS Summit and Community Day events are also scheduled worldwide. ## AWS AI & ML Scholars Program - Open to anyone aged 18 or older, with no prior AI or machine learning experience required. - Includes: - A foundational generative AI Challenge phase. - A fully funded three-month Udacity Nanodegree for the top 4,500 performers. - Applications close June 24, 2026. ## Serverless and Database Improvements - **Aurora PostgreSQL express configuration** enables serverless databases to be created and connected in seconds using preconfigured defaults. - **Aurora PostgreSQL is now part of the AWS Free Tier**, with eligible new customers receiving AWS credits. - The **Agent Plugin for AWS Serverless** adds skills, sub-agents, and Model Context Protocol servers to AI coding assistants such as Kiro, Claude Code, and Cursor. - It supports building, deploying, troubleshooting, and managing production-ready serverless applications. - The **Aurora DSQL Connector for Ruby** automatically generates authentication tokens for each connection while remaining compatible with the `pg` gem. ## SageMaker and AWS Console Updates - **SageMaker Studio** now supports remote connections from Kiro and Cursor, combining those IDEs’ coding workflows with SageMaker’s scalable compute. - The AWS Management Console now supports visual customization, including account colors and hiding unused regions or services to reduce interface clutter. ## Expanded Lambda Managed Instance Capacity - The file descriptor limit has increased from 1,024 to 4,096, supporting higher-concurrency and file-intensive workloads. - Functions can now use up to: - 32 GB of memory - 16 vCPUs - Users can select memory-to-vCPU ratios of 2:1, 4:1, or 8:1 for workloads such as data processing, media transcoding, and scientific simulations. ## Conversational Speech with Amazon Polly - Polly’s new Bidirectional Streaming API supports incremental text-to-speech generation. - Audio synthesis can begin before an LLM or other application has produced the complete response, making it better suited to conversational AI. ## Upcoming AWS Events - AWS Summits are free, in-person events covering cloud, AI, best practices, and networking. - Upcoming locations include Paris, London, Bengaluru, Singapore, Tel Aviv, and Stockholm. - AWS Community Days in San Francisco and Romania will feature community-led talks, workshops, and hands-on labs. AWS developers can follow the AWS News Blog and “What’s New with AWS” for additional announcements, while the AWS Builder Center and Events and Webinars pages provide opportunities for learning and community participation.

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Lambda AI Database Design PostgreSQL+4

datadogNov 1, 20182 min readCurated summary

Improving trust with Datadog Log Management

Datadog handles hundreds of thousands of emails daily and uses Amazon SES for critical messages such as password resets. Because SES and CloudWatch did not provide sufficiently accessible, support-friendly event data, Datadog built a serverless pipeline that forwards SES events to Datadog Log Management. This provides low-maintenance delivery infrastructure, searchable email metrics, and monitoring for failures. ## Exporting Amazon SES Events - SES configuration sets define which email events to capture: - Send - Reject - Bounce - Complaint - Delivery - Open - Click - Events are published to an Amazon SNS topic. - SNS invokes an AWS Lambda function for every event. - The Lambda forwards the event to Datadog Logs using the Datadog API key. - Terraform provisions the SNS topic, SES configuration set, event destination, IAM role, and Lambda function. - The example uses Python 2.7 and stores the API key as a Lambda environment variable; production systems should encrypt the key. - This architecture avoids maintaining a custom email service while preserving visibility into email processing. ## Making SES Events Searchable in Datadog - SES events arrive in Datadog as JSON. - Datadog’s existing AWS integration pipeline processes the logs automatically. - Important fields can be converted into facets directly from a log entry. - Datadog uses fields such as the email event type and subject to quickly search for specific password reset activity. ## Monitoring and Operational Benefits - Support teams can verify whether a recipient received or interacted with a password reset email. - The entire delivery and logging pipeline is serverless and requires minimal maintenance. - Monitors can be configured on the logs to alert normal escalation channels when any part of the pipeline fails. - The solution combines the reliability of Amazon SES with Datadog’s observability and search capabilities. Overall, routing SES events through SNS and Lambda into Datadog Log Management is a practical way to create a trusted, observable password-reset email system without operating a separate mail infrastructure.

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Lambda Database Design Datadog Log Management+4

datadogAug 15, 20173 min readCurated summary

Improving cloud security visibility with ChatOps

Datadog built a largely serverless AWS security monitoring pipeline to detect suspicious API activity across more than 15 AWS accounts. Rather than process every CloudTrail event equally, it categorizes actions as log, notify, or alert, reducing false positives while preserving broad visibility. The system centralizes events, batches activity, and uses automated workflows to verify engineers’ actions or escalate potential compromises. ## The Security Monitoring Challenge - Datadog manages extensive AWS infrastructure across multiple accounts and nearly 200 geographically distributed engineers. - Every console or CLI operation generates an AWS API call, creating a high-volume CloudTrail data stream. - The monitoring system must detect malicious activity while also catching accidental exposure caused by configuration mistakes. - Processing every event manually would require an expensive, constantly staffed Security Operations Center. ## Prioritizing Relevant API Calls - Datadog maintains a focused list of security-relevant AWS API calls and assigns each to one of three categories: - **Log:** Lower-risk events retained for investigation, such as `CreateGroup` or `UpdateUser`. - **Notify:** Events that require the initiating engineer to confirm their identity and intent, such as `CreateUser` or `PutUserPolicy`. - **Alert:** Rare, dangerous, or clearly misconfigured actions sent directly to the security team. - A representative alert is `AuthorizeSecurityGroupIngress` with `0.0.0.0/0`, which exposes an EC2 security group to the entire Internet. - User verification reduces false positives and helps identify compromised AWS credentials. ## Cross-Account Event Pipeline - CloudTrail records API activity in each AWS account. - CloudWatch Event Rules filter for the selected API calls and publish matching events to SNS. - SNS forwards events across accounts to an SQS queue in a dedicated security AWS account. - Centralization is necessary because CloudWatch could not directly send events cross-account to SQS. - The queue also supports batching, which is important when Terraform generates many AWS changes in a short period. - A CloudWatch rule triggers a Lambda function every two minutes to drain the SQS queue and forward events to the security orchestration layer. ## Automated Decision-Making with Komand - Datadog uses Komand, a security orchestration and automation platform, to construct workflows from built-in and custom plugins. - A custom decision plugin evaluates: - The calling user - The event’s age - Request parameters and their content - Other contextual details - Based on the analysis, the workflow silently logs the event, notifies the engineer, or pages the security team through PagerDuty. ## Engineer Verification and Escalation - For notification-level events, the engineer receives an interactive Slack message containing API call details. - Confirming the action triggers a Duo push for second-factor identity verification. - If the engineer denies the action or fails to respond promptly, the workflow alerts the security team. - Komand coordinates the Slack, Duo, PagerDuty, and custom integration logic in one centralized workflow. ## Visibility and Continuous Improvement - Every workflow execution is logged and sent to Elasticsearch. - The resulting data helps Datadog visualize security events, measure detection effectiveness, identify behavioral trends, and improve alerting. - The pipeline is designed to provide actionable security intelligence without overwhelming engineers or security personnel. Datadog’s approach combines selective event filtering, cross-account centralization, batching, and automated identity verification. Organizations facing similar AWS-scale monitoring challenges can use the same principles to reduce alert fatigue while maintaining strong detection and response capabilities.

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Lambda AWS Slack Terraform+4