# Amazon S3 Published articles for Amazon S3. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale DevFeed: [Abnormal AI: Amazon Bedrock AgentCore for agentic email security at scale]() Original publisher: [Read original article]() Author: Aswin Vasudevan Published: 2026-09-14T21:22:45Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [threat detection](), [Amazon Bedrock](), [Security](), [Serverless](), [API](), [Amazon Web Services](), [AWS CloudTrail](), [Amazon CloudWatch](), [Amazon S3]() Tags: [advanced-300](), [agentic](), [agents](), [amazon-bedrock-agentcore](), [amazon-cloudwatch](), [amazon-s3](), [analysis](), [api](), [aws](), [aws-cloudtrail](), [code](), [customer-solutions](), [data](), [data-processing](), [security](), [serverless](), [threat-detection]() ### AI overview Abnormal AI uses Amazon Bedrock AgentCore Code Interpreter as an ephemeral, serverless compute scratch pad for real-time inline email threat detection. The article describes its sandbox isolation, networking and file-handling options, preloaded Python capabilities, observability integrations, and use at billion-message scale. ### Source excerpt Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production. ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows DevFeed: [Netflix Reworks Conductor for 420 Million Monthly Workflow Executions and 10X Larger Workflows]() Original publisher: [Read original article]() Author: Leela Kumili Published: 2026-09-11T14:17:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Orchestration](), [Amazon S3](), [Kafka](), [Apache Iceberg]() Tags: [amazon-s3](), [apache-iceberg](), [apache-kafka](), [architecture-design](), [asynchronous-architecture](), [cassandra](), [cloud-architecture](), [concurrency](), [development](), [devops](), [distributed-systems](), [elasticsearch](), [java-operator-sdk](), [kafka](), [latency](), [microservices](), [netflix](), [netflix-conductor-4-workflow](), [news](), [orchestration](), [s3](), [scalability](), [windows-workflow-foundation](), [workflow](), [workflow-bpm](), [workflow-foundation]() ### AI overview Netflix reworked Conductor 4.0 to scale workflow orchestration to roughly 200,000 definitions and 420 million monthly executions. The redesign raises supported workflow size to 30,000 tasks and reports a roughly 40% reduction in p99 evaluation latency by loading only task data needed for each decision. ### Source excerpt Netflix has reworked its Conductor workflow orchestration engine to handle larger workloads, increasing supported workflow size from about 2,500 to 30,000 tasks and reducing p99 workflow evaluation latency by about 40%. Conductor 4.0 separates workflow metadata from task data, moves evaluation to asynchronous processing, and introduces dynamic worker allocation and concurrency controls. By Leela Kumili ## Reduce inference cold starts on Amazon SageMaker HyperPod with model caching DevFeed: [Reduce inference cold starts on Amazon SageMaker HyperPod with model caching]() Original publisher: [Read original article]() Author: Kareem Syed-Mohammed Published: 2026-09-10T21:37:49Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Caching](), [Inference](), [model-deployment](), [Large Language Model](), [autoscaling](), [Kubernetes](), [Amazon S3]() Tags: [amazon](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [autoscaling](), [benchmark](), [caching](), [expert-400](), [inference](), [kubernetes](), [llm]() ### AI overview Amazon SageMaker HyperPod model caching preloads model weights and container images onto cluster nodes, reducing inference-pod cold starts by serving assets from local NVMe storage. ### Source excerpt Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it. ## Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate DevFeed: [Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate]() Original publisher: [Read original article]() Author: Chaytanya Kumar Published: 2026-09-10T16:08:57Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Automation](), [Amazon S3](), [CSV](), [Amazon Web Services](), [datasets]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-simple-storage-service-s3](), [automation](), [aws](), [data](), [how-to](), [technical-how-to](), [tutorial]() ### AI overview A tutorial for automating RFI questionnaire processing with Amazon Quick Automate. It reads multi-tab workbooks from Amazon S3, extracts and structures questions, and writes CSV output to Amazon S3. ### Source excerpt Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 -- cutting development from days to hours. ## Customize Amazon API Gateway destinations for execution logs DevFeed: [Customize Amazon API Gateway destinations for execution logs]() Original publisher: [Read original article]() Author: Giedrius Praspaliauskas Published: 2026-09-09T22:35:42Z Content type: tutorial Language: en Sources: [AWS Compute Blog]() Topics: [Amazon API Gateway](), [Amazon CloudWatch Logs](), [log management](), [Amazon S3](), [SIEM, Security, Observability]() Tags: [amazon-api-gateway](), [amazon-cloudwatch-logs](), [amazon-s3](), [announcements](), [intermediate-200](), [logs](), [observability](), [rest-api]() ### AI overview This tutorial explains how to route Amazon API Gateway REST API execution logs to CloudWatch Logs, Amazon S3, or Amazon Data Firehose. It contrasts execution logs with access logs and describes their use for diagnosing authorization, validation, integration, mapping, and error-handling behavior. ### Source excerpt Amazon API Gateway execution logs help you trace request processing step by step through your REST API stages. They capture authorization results, integration latency, mapping template output, and error details that are otherwise invisible at the API surface. When a production request fails in a way the access log cannot explain, the execution log is [...] ## Validating multi-Region DR for Terraform Enterprise with AWS FIS DevFeed: [Validating multi-Region DR for Terraform Enterprise with AWS FIS]() Original publisher: [Read original article]() Author: Frenil Randeria Published: 2026-09-09T21:05:02Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Terraform](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [Amazon S3](), [Amazon EC2](), [Databases]() Tags: [advanced-300](), [amazon-aurora](), [amazon-ec2](), [amazon-s3](), [aws](), [aws-fault-injection-service-fis](), [disaster-recovery](), [postgresql](), [technical-how-to](), [terraform](), [validation]() ### AI overview This article explains how to validate customer-operated multi-Region disaster recovery for Terraform Enterprise on AWS using three-phase AWS FIS experiments. It covers failover and failback testing, hidden dependency discovery, and reported recovery times of 12-14 minutes. ### Source excerpt Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid. ## AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) DevFeed: [AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-31T14:45:25Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [data](), [Amazon S3](), [Amazon Redshift](), [Node.js]() Tags: [acquisition](), [amazon-ec2](), [amazon-elastic-container-service](), [amazon-gamelift](), [amazon-redshift](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [analytics](), [announcements](), [aws](), [aws-fargate](), [aws-glue](), [aws-iot-core](), [aws-lambda](), [database](), [duckdb](), [json](), [news](), [node-js](), [open-source](), [parquet](), [python](), [sql](), [week-in-review]() ### AI overview AWS weekly roundup covering the planned acquisition of DuckLabs, the company behind DuckDB, alongside Amazon ECS recovery updates and AWS Lambda preview runtimes for Node.js 26 and Python 3.15. ### Source excerpt The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation [...] ## AWS Network Firewall now supports rule hit count DevFeed: [AWS Network Firewall now supports rule hit count]() Original publisher: [Read original article]() Author: Preetkumar Shah Published: 2026-08-20T18:40:20Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Firewall](), [Amazon Web Services](), [Amazon CloudWatch Logs](), [Network](), [Security](), [Amazon S3](), [log management](), [Incident response]() Tags: [amazon-cloudwatch](), [amazon-cloudwatch-logs](), [amazon-s3](), [announcements](), [aws](), [aws-network-firewall](), [compliance](), [firewall](), [incident](), [incident-response](), [intermediate-200](), [logs](), [network](), [s3](), [security](), [security-blog](), [security-identity-compliance](), [storage]() ### AI overview AWS Network Firewall now provides rule hit counts for stateful rules, using alert-log data to show how often rules match network traffic. The feature helps teams identify unused rules, support incident response, and demonstrate security-control effectiveness for compliance. ### Source excerpt As firewall rule sets grow in complexity, security teams face a common challenge: manual log analysis is used to determine which rules are actively matching traffic and which are consuming capacity without being triggered. This lack of visibility creates operational and compliance gaps. Organizations with governance policies that require removal of dormant rules after a [...] ## Propagate user authorization context in AI agents with Amazon Bedrock AgentCore DevFeed: [Propagate user authorization context in AI agents with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Anshu Bathla Published: 2026-08-19T17:24:15Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Amazon Bedrock AgentCore](), [AI Agent](), [Authorization](), [AWS Identity and Access Management (IAM)](), [Amazon Bedrock Knowledge Bases](), [Amazon DynamoDB](), [Amazon S3](), [prompt injection](), [Amazon Web Services](), [Software as a service]() Tags: [access-control](), [advanced-300](), [ai-agents](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-bedrock-knowledge-bases](), [amazon-dynamodb](), [amazon-s3](), [artificial-intelligence](), [authorization](), [aws](), [prompt-injection](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview This article explains how to propagate user authorization context through AI agents built with Amazon Bedrock AgentCore. It presents a pattern for enforcing least-privilege access in downstream data services and infrastructure, so agents can access DynamoDB, Bedrock Knowledge Bases, S3-backed documents, and other sources only within the requesting user's permissions. ### Source excerpt Many teams now deploy AI agents that pull from Amazon DynamoDB tables, document repositories, software as a service (SaaS) platforms, and internal knowledge bases to answer questions and automate workflows. A key risk in these deployments is that the agent has no awareness of who's asking, so it might return data the user shouldn't see. [...] ## Audit Log Drains now support Datadog, Splunk, and Panther DevFeed: [Audit Log Drains now support Datadog, Splunk, and Panther]() Original publisher: [Read original article]() Author: Nate McGrady Published: 2026-08-07T04:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [SIEM, Security](), [Streaming](), [Amazon S3](), [SIEM, Security, Observability]() Tags: [amazon-s3](), [datadog](), [documentation](), [enterprise](), [integration](), [logs](), [migration](), [migration-guide](), [s3](), [streaming]() ### AI overview Vercel Audit Log Drains can stream team audit events and metadata to Datadog, Splunk, or Panther, in addition to custom HTTPS endpoints and Amazon S3. The feature is available on Enterprise plans and replaces Custom SIEM Log Streaming. ### Source excerpt Audit Log Drains now stream your team's audit events into Datadog, Splunk, and Panther, joining the existing custom HTTPS endpoint and Amazon S3 destinations. An Audit Log Drain forwards every event from your team's Activity Log, plus additional audit metadata, to the destination you choose. They're available on Enterprise plans. To create one, go to Drains in your team settings. Click Add Drain, choose Audit Log as the data type, and pick a destination. Audit Log Drains replace Custom SIEM Log Streaming. If you already stream audit logs to a SIEM, follow the migration guide to move your integration over. Learn more about Drains in the documentation. Read more ## A guided tour of Terraform state, hosted modules, and HCL in Pulumi DevFeed: [A guided tour of Terraform state, hosted modules, and HCL in Pulumi]() Original publisher: [Read original article]() Author: Christian Nunciato Published: 2026-08-04T00:00:00Z Content type: tutorial Language: en Sources: [Pulumi]() Topics: [Terraform](), [opentofu](), [Amazon S3](), [Command-line interface](), [Cloud](), [GitHub](), [Template]() Tags: [amazon-s3](), [aws](), [cli](), [github](), [hcl](), [how-to](), [infrastructure-as-code](), [modules](), [opentofu](), [pulumi-cloud](), [registry](), [s3](), [terraform](), [tutorials]() ### AI overview A hands-on walkthrough shows how to move a Terraform project that provisions an Amazon S3 bucket into Pulumi Cloud. It covers Pulumi Cloud as a Terraform state backend, hosted Terraform modules, and HCL authoring support, with references to OpenTofu and AWS. ### Source excerpt Today's big release contains a whole new set of features designed for seamless interoperability with the Terraform and OpenTofu ecosystems, and there's a lot there -- so much that it can be tough to get your head around all of it. But it generally falls into three major categories: Support for Pulumi Cloud as a Terraform state backend, including remote execution with human approvals A Terraform module registry in Pulumi Cloud that lets you publish, document, and share your modules even across language boundaries First-class support for HCL as an authoring language in the Pulumi engine To make this release a little easier to appreciate holistically, I've put together a quick end-to-end walkthrough that doesn't quite cover everything, but does cover the big stuff, and should give you a sense of how it all comes together. We'll start with a simple Terraform project that you'll deploy to AWS, and then one step at a time, bring it into Pulumi Cloud and kick the tires on each of these new features as we go. It'll take a bit, but all you'll need are a free Pulumi account and the ability to deploy an S3 bucket to AWS. We've got a bunch to cover, so let's jump right in. Start with a Terraform project Our tour begins with a tiny Terraform project that provisions a single Amazon S3 bucket using a locally defined module that we'll publish later. The project is available on GitHub as a template, and the easiest way to use it is with the GitHub CLI: $ gh repo create my-tf-project \ --template cnunciato/simple-tf-template \ --public \ --clone && cd my-tf-project We'll use the local Terraform backend to start. Set your AWS credentials (preferably with environment variables), then deploy the project with Terraform or OpenTofu. (This walkthrough uses the terraform CLI, but you can swap in tofu if that's your preference.) $ terraform init && terraform apply ... Apply complete! Resources: 2 added, 0 changed, 0 destroyed. Outputs: bucket_arn = "arn:aws:s3:::my-tf-project-bucket-14d19ece" ## Massively parallel Postgres backups DevFeed: [Massively parallel Postgres backups]() Original publisher: [Read original article]() Author: Ben Dicken Published: 2026-07-31T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [neki](), [cloud-infrastructure](), [Amazon S3](), [Orchestration](), [Server](), [MySQL](), [Terraform]() Tags: [amazon-s3](), [backup](), [building](), [databases](), [engineering](), [infrastructure](), [mysql](), [neki](), [orchestration](), [postgres](), [scale](), [servers](), [storage]() ### AI overview PlanetScale describes how Neki, its sharded Postgres system, performs consistent encrypted backups at petabyte scale. The approach combines filesystem backups, archived WAL replay, object storage such as Amazon S3, and massive parallelism to back up databases without affecting production queries. ### Source excerpt PlanetScale backs up petabyte-scale sharded Postgres databases in hours using parallel infrastructure, object storage, and WAL replay. ## AWS Weekly Roundup: Local Zone in Athens, Claude Opus 5 on AWS, Lambda durable execution for .NET, and more (July 27, 2026) DevFeed: [AWS Weekly Roundup: Local Zone in Athens, Claude Opus 5 on AWS, Lambda durable execution for .NET, and more (July 27, 2026)]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-07-27T14:54:41Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Amazon Web Services](), [AWS Local Zones](), [Latency](), [Amazon Bedrock](), [Amazon EC2](), [Amazon S3](), [Claude](), [cloud-infrastructure](), [Amazon Elastic Container Service](), [anthropic](), [data-governance]() Tags: [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-connect](), [amazon-ec2](), [amazon-s3](), [amazon-sagemaker-ai](), [announcements](), [anthropic](), [aws](), [aws-lambda](), [aws-local-zones](), [claude](), [data-governance](), [latency](), [news](), [week-in-review]() ### AI overview An AWS weekly roundup covering a new Local Zone in Athens, Greece, and selected AWS launches and updates, including Claude Opus 5 on Amazon Bedrock. The article highlights local data processing, data residency, low latency, and infrastructure for regional workloads. ### Source excerpt Last week I had the privilege of spending three days in São Paulo with technical builders from across Latin America, brought together for a regional tech event full of deep-dive sessions, hands-on workshops, and conversations with customers and partners. What struck me most wasn't any single session, it was the energy of a technical community [...] ## Serverless ICYMI Q2 2026 DevFeed: [Serverless ICYMI Q2 2026]() Original publisher: [Read original article]() Author: Julian Wood Published: 2026-07-20T16:40:00Z Content type: article Language: en Sources: [AWS Compute Blog]() Topics: [Serverless](), [Amazon Web Services](), [AWS Lambda](), [Firecracker](), [Amazon S3](), [Filesystems](), [Linux](), [virtualization](), [mount](), [Latency]() Tags: [2026](), [amazon-eventbridge](), [amazon-s3](), [amazon-simple-storage-service-s3](), [aws](), [aws-lambda](), [aws-step-functions](), [ci-cd](), [firecracker](), [latency](), [linux](), [mount](), [serverless](), [virtualization]() ### AI overview A Q2 2026 recap of AWS serverless launches and resources, focusing on AWS Lambda MicroVMs and Amazon S3 Files integration with Lambda. ### Source excerpt In this 33rd quarterly recap post, discover the most impactful AWS serverless launches, features, and resources from Q2 2026 that you might have missed. Stay current with the latest serverless innovations that can improve your applications. In case you missed our last ICYMI, read about what happened in Q1 2026. AWS Lambda MicroVMs AWS Lambda [...] ## AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) DevFeed: [AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-07-20T16:37:34Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [AI-assisted coding](), [Amazon S3](), [cursor](), [GitHub Copilot]() Tags: [ai-agent](), [amazon-bedrock](), [amazon-cognito](), [amazon-dynamodb](), [amazon-s3](), [amazon-simple-storage-service-s3](), [aws](), [aws-lambda](), [kiro](), [mcp](), [news](), [openai](), [serverless](), [strands-agents](), [week-in-review]() ### AI overview AWS weekly roundup covering a one-click Lambda prompt for configuring coding agents with serverless skills and an MCP server, OpenAI GPT-5.6 models on Amazon Bedrock, and same-day Amazon S3 storage-class transitions. ### Source excerpt Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul. My team regularly visits countries across the Asia-Pacific region, listens to feedback [...] ## Introducing self-managed Amazon S3 buckets for AWS Lambda function code DevFeed: [Introducing self-managed Amazon S3 buckets for AWS Lambda function code]() Original publisher: [Read original article]() Author: Doug Perkes Published: 2026-07-17T10:49:14Z Content type: release Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [Amazon S3](), [Deployment](), [Security](), [Replication](), [CI/CD](), [AWS Organizations]() Tags: [amazon-s3](), [amazon-simple-storage-service-s3](), [audit-trail](), [aws](), [aws-lambda](), [aws-organizations](), [ci-cd](), [compliance](), [deployment](), [disaster-recovery](), [encryption](), [replication](), [security](), [uncategorized]() ### AI overview AWS Lambda now supports self-managed Amazon S3 buckets for function deployment packages. Lambda reads code directly from the customer-controlled bucket, removing deployment-package copies from the Lambda code storage quota and giving teams control over encryption, access policies, compliance, lifecycle management, audit trails, and cross-Region replication. ### Source excerpt If you manage Lambda functions at scale, you've likely hit the 75 GB code storage limit or explained to your security team why deployment artifacts live in an S3 bucket you don't control. Today, we're announcing self-managed Amazon S3 buckets for AWS Lambda deployment packages. Lambda reads your code directly from your bucket, eliminating quota [...] ## Tableflow: Turn Kafka Topics into Iceberg Tables DevFeed: [Tableflow: Turn Kafka Topics into Iceberg Tables]() Original publisher: [Read original article]() Author: Mohtasham Sayeed Mohiuddin Published: 2026-07-10T15:36:14Z Content type: tutorial Language: en Sources: [Confluent: Data in motion]() Topics: [Apache Iceberg](), [Kafka](), [schema-evolution](), [Confluent Cloud](), [parquet](), [Amazon S3](), [Streaming](), [AWS Glue](), [Amazon Redshift](), [BigQuery]() Tags: [amazon-redshift](), [amazon-s3](), [apache-iceberg](), [aws-glue](), [bigquery](), [confluent-cloud](), [kafka](), [parquet](), [schema-evolution](), [streaming](), [technologies](), [tutorial]() ### AI overview This tutorial explains how Confluent Cloud Tableflow continuously materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables. It covers automatic schema handling, type conversion, schema evolution, Parquet conversion, catalog publishing, and table maintenance for querying streaming data with analytics engines and warehouses. ### Source excerpt Learn how Confluent Tableflow turns Kafka topics into Iceberg tables for zero-ETL analytics with automatic schema evolution and open catalog access. ## Graph-Shaped Shared Memory for AI Agents DevFeed: [Graph-Shaped Shared Memory for AI Agents]() Original publisher: [Read original article]() Author: Neo Kim Published: 2026-07-07T11:32:10Z Content type: tutorial Language: en Sources: [System Design Newsletter]() Topics: [AI Agent](), [Graphs](), [graph-database](), [Amazon S3](), [Retrieval Augmented Generation (RAG)](), [Open Source]() Tags: [ai-agent](), [ai-agents](), [amazon-s3](), [graph](), [graph-database](), [open-source](), [rag]() ### AI overview A guide to shared memory for multiple AI agents, using Omnigraph as a case study. It explains why shared folders and vector databases can fail, how graph-shaped memory represents knowledge, and how transactions, versioning, and combined retrieval methods can help agents share context. ### Source excerpt #160: A full guide to graph shaped memory for AI agents ## Expanded Audit Log coverage, now delivered through Vercel Drains DevFeed: [Expanded Audit Log coverage, now delivered through Vercel Drains]() Original publisher: [Read original article]() Author: Luka Hartwig Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Amazon S3](), [Security](), [SIEM, Security](), [Architecture & Design]() Tags: [amazon-s3](), [compliance](), [enterprise](), [http](), [logs](), [pricing](), [s3](), [security](), [vercel](), [workflow]() ### AI overview Vercel has expanded Audit Log coverage to more than 400 unique team activity events. Enterprise teams can export these events through Audit Log Drains to custom HTTP endpoints or Amazon S3, replacing Custom SIEM Log Streaming and its separate paid add-on. Audit Log Drains use standard Drains pricing of $0.50 per GB. ### Source excerpt Audit Logs now capture 400+ unique team activity events, giving teams broader coverage for security reviews, compliance workflows, and investigations. With Vercel Drains support, teams can export those events to custom HTTP endpoints or Amazon S3, replacing Custom SIEM Log Streaming with a flexible Drains-based workflow. Audit Log Drains are available for all Enterprise teams, replacing Custom SIEM Log Streaming. They are priced at $0.50/GB through standard Drains pricing, replacing a separate paid add-on. Try it out or learn more about Audit Log Drains and draining Audit Logs to S3. Read more ## Reduce CDN log costs with searchable archives DevFeed: [Reduce CDN log costs with searchable archives]() Original publisher: [Read original article]() Author: Rufina Mariam Published: 2026-06-26T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [archive search](), [telemetry](), [incident](), [Amazon S3]() Tags: [amazon-s3](), [archive-search](), [cdn](), [cost](), [devsecops](), [incident](), [log-management](), [logs](), [observability-pipelines](), [storage]() ### AI overview The article presents a cost-conscious approach to retaining high-volume CDN logs: route raw logs to object storage with Observability Pipelines, retain key signals in Datadog, and use Archive Search for historical investigations. ### Source excerpt Route high-volume CDN logs to low-cost object storage with Observability Pipelines and search them with Archive Search--without a second tool. ## ClickHouse achieves AWS Retail Competency DevFeed: [ClickHouse achieves AWS Retail Competency]() Original publisher: [Read original article]() Author: Aditya Chidurala Published: 2026-06-12T21:17:44Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Amazon Web Services](), [real-time](), [data](), [Amazon S3](), [Compression](), [Kafka](), [Microservices](), [observability](), [Traces](), [Cloud](), [networking]() Tags: [amazon-s3](), [aws](), [batch](), [business](), [clickhouse](), [cloud](), [compression](), [data](), [e-commerce](), [kafka](), [microservices](), [observability](), [personalization](), [real-time](), [retail](), [s3](), [storage](), [traces]() ### AI overview ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, recognizing its validated expertise in real-time retail analytics and customer success on AWS. ### Source excerpt ClickHouse has achieved the AWS Retail Competency, joining a select group of AWS Partners recognized for deep expertise in helping retailers turn live operational data into real-time decisions. ## Investigate logs across your entire stack with Federated Logs DevFeed: [Investigate logs across your entire stack with Federated Logs]() Original publisher: [Read original article]() Author: Rufina Mariam Published: 2026-06-09T00:00:00Z Content type: tutorial Language: en Sources: [Datadog | The Monitor blog]() Topics: [log management](), [observability](), [telemetry](), [databricks](), [clickhouse](), [data](), [SIEM, Security, Observability]() Tags: [amazon](), [amazon-s3](), [analytics](), [clickhouse](), [data](), [databricks](), [deployment](), [feature](), [log-management](), [logs](), [observability](), [observability-pipelines](), [telemetry]() ### AI overview Learn how Datadog Federated Logs lets teams query logs across Datadog and external data stores such as Databricks and ClickHouse from the Log Explorer. The article demonstrates how a single query interface can help investigate payment failures that trace back to an AI fraud detection model. ### Source excerpt Learn how to use Federated Logs to investigate logs across Datadog, Databricks, ClickHouse, Amazon S3, and Snowflake. [Next page]()