# athena Published articles for athena. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Athena spotlight: Black Duck on the importance of flagging zero-days at scale DevFeed: [Athena spotlight: Black Duck on the importance of flagging zero-days at scale]() Original publisher: [Read original article]() Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Vulnerabilities](), [Artificial Intelligence](), [Frontier AI](), [Frontier Model](), [Security](), [Open Source]() Tags: [ai](), [anthropic](), [athena](), [attacks](), [chainguard](), [code](), [container-images](), [frontier-ai](), [frontier-ai-models](), [frontier-model](), [management](), [mythos](), [open-source](), [osv](), [project-glasswing](), [scale](), [security](), [source](), [vulnerabilities]() ### AI overview The article explains how Black Duck and the Athena coalition address the growing volume of AI-discovered open source zero-day vulnerabilities. Athena members use frontier models to scan sandboxed applications, while Chainguard triages, validates, and remediates findings and shares artifacts and OSV data. Black Duck uses that feed to alert customers and provide mitigation and remediation guidance. ### Source excerpt AI can find zero-days faster than teams can fix them. See how Black Duck and Athena work together to turn findings into actionable protection. ## Expanding Athena DevFeed: [Expanding Athena]() Original publisher: [Read original article]() Published: 2026-07-07T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Vulnerabilities](), [open-source-security](), [Frontier AI](), [AI Models](), [Artificial Intelligence](), [Security](), [Maintainers](), [Parser](), [Library]() Tags: [ai](), [akrites](), [athena](), [chainguard-clearinghouse](), [chainguard-libraries](), [frontier-ai](), [library](), [maintainers](), [open-source](), [parsing](), [secure-open-source](), [secure-oss](), [security](), [vulnerabilities]() ### AI overview Chainguard describes Athena, an industry coalition coordinating the discovery, remediation, protection, disclosure, and upstream fixing of vulnerabilities in open source software. The article reports that Athena has processed more than 40,000 vulnerabilities and emphasizes that frontier AI models can identify latent flaws and chain lower-severity bugs into serious attacks. ### Source excerpt See how Athena is helping secure open source by coordinating AI-discovered vulnerabilities, partner protections, and upstream fixes at scale. ## Why Vulnerability Clearinghouses Alone Cannot Secure Open Source DevFeed: [Why Vulnerability Clearinghouses Alone Cannot Secure Open Source]() Original publisher: [Read original article]() Published: 2026-07-05T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [data](), [Vulnerabilities](), [Open Source](), [Security](), [NVD](), [Unix]() Tags: [akrites](), [athena](), [chainguard-libraries](), [data](), [ibm-red-hat-project-lightwell](), [nvd](), [open-source](), [press-release](), [secure-open-source](), [security](), [security-research](), [vulnerability-clearinghouse](), [vulnerability-data]() ### AI overview The article argues that vulnerability clearinghouses are primarily pools of data and are not the most important part of securing open source. It emphasizes actuation--turning findings into fixes--along with trusted builds and secure-by-design software. ### Source excerpt Clearinghouses alone won't secure open source. Learn why actuation, trusted builds, and secure-by-design software matter more than vulnerability data. ## Everything we announced during AI Readiness Innovation Week DevFeed: [Everything we announced during AI Readiness Innovation Week]() Original publisher: [Read original article]() Published: 2026-06-25T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Artificial Intelligence](), [ai-coding](), [supply-chain-security](), [chainguard](), [open-source-security](), [Vulnerabilities](), [Security](), [chainguard libraries](), [chainguard containers](), [CI/CD](), [Frontier AI](), [GitHub]() Tags: [ai](), [ai-readiness](), [athena](), [aws-kiro](), [chainguard](), [chainguard-containers](), [chainguard-libraries](), [ci-cd](), [containers](), [cursor](), [frontier-ai-models](), [gartner-magic-quadrant](), [github](), [innovation](), [open-source](), [security](), [software-supply-chain](), [software-supply-chain-security](), [speed](), [vulnerabilities]() ### AI overview Chainguard summarizes announcements from AI Readiness Innovation Week, covering supply-chain security advances for containers, libraries, CI/CD pipelines, AI agent skills, IDE integrations, partnerships, and an industry coalition. The article highlights Athena, a coalition designed to coordinate defense against vulnerabilities discovered by frontier AI models. ### Source excerpt Read about everything Chainguard announced during AI Readiness Innovation Week, including new features for Chainguard Libraries and Chainguard Containers. ## Streaming optimized data to S3 for analytics with Parquet DevFeed: [Streaming optimized data to S3 for analytics with Parquet]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2025-08-13T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [parquet](), [Redpanda-Connect](), [Amazon S3](), [data analytics](), [Streaming](), [Apache Spark](), [pandas](), [Serverless]() Tags: [analytics](), [analytics-systems-data-feed](), [apache-spark-and-athena-data-queries](), [athena](), [automating-data-pipelines-in-s3](), [building-dashboards-with-s3-data](), [compressing-data-with-parquet](), [data](), [data-pipeline](), [data-pipeline-for-analytics](), [event-driven-pipelines-with-s3](), [how-to](), [learn](), [pandas](), [parquet](), [parquet-files-in-amazon-s3](), [pipeline](), [real-time-data-streaming](), [redpanda-connect](), [redpanda-data-streaming](), [s3](), [serverless](), [setting-up-redpanda-observability](), [spark](), [streaming](), [streaming-data-to-s3-with-parquet](), [tutorial]() ### AI overview This tutorial explains how to use Redpanda Connect to continuously batch streaming data and write compressed Apache Parquet files to Amazon S3 for analytical workloads. It covers the benefits of Parquet and querying the resulting files with tools such as Pandas, Apache Spark, and Athena. ### Source excerpt Learn how to build a powerful data pipeline that feeds analytics systems from Redpanda using clean, compressed Parquet files in Amazon S3. ## How Cloud CIRCUS used ClickHouse to speed up and simplify log analytics DevFeed: [How Cloud CIRCUS used ClickHouse to speed up and simplify log analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-06-17T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Content Management System](), [Amazon S3](), [Amazon Web Services](), [Open Source](), [Software as a service]() Tags: [2025](), [analytics](), [athena](), [aws](), [clickhouse](), [cloud](), [cms](), [cost](), [customer](), [infrastructure](), [japan](), [logs](), [open-source](), [performance](), [pipeline](), [s3](), [switching]() ### AI overview Cloud CIRCUS migrated BlueMonkey's CloudFront log analytics from Amazon Athena to a self-managed ClickHouse environment. The move consolidated logs into a single table, improved query speed and automation, and reduced the time, complexity, and unpredictability of analytics costs as the SaaS company scaled across more than 2,000 customer websites. ### Source excerpt "By migrating from Athena to ClickHouse, we've been able to save both time and costs. Most importantly, we no longer have to worry about expenses every time we run a query, which is a huge relief." Kyurin Shu, infrastructure engineer ## Automating the Klarna Card Ownership Fees System using AWS Step Functions DevFeed: [Automating the Klarna Card Ownership Fees System using AWS Step Functions]() Original publisher: [Read original article]() Author: Michel Neumann Published: 2024-05-02T07:42:27Z Content type: article Language: en Sources: [Klarna Engineering]() Topics: [AWS Step Functions](), [AWS CloudFormation](), [Automation](), [jira]() Tags: [athena](), [automation](), [aws](), [aws-step-functions](), [cloudformation](), [databases](), [jira](), [klarna](), [maintenance](), [pull-requests](), [run](), [serverless](), [step-functions](), [workflow]() ### AI overview The article explains how Klarna automated its Klarna Card monthly fee-collection process with AWS Step Functions and CloudFormation. The automation replaced a manual, multi-day batch workflow with a scheduled process and reduced maintenance effort. ### Source excerpt This article outlines how my team and I applied automation using AWS Step Functions and CloudFormation on a system to charge the monthly fee for Klarna Cards, enabling us to transform a previously manual routine into a self-sufficient, scheduled workflow. The initiative significantly streamlined operations and reduced maintenance cost. Introduction In early 2023, Klarna introduced monthly fees for Klarna Cards in the US. In the Card & Banking domain, two teams, including myself as engineer, developed this system within a tight four-month deadline. Initially, the system required extensive manual operation, including a detailed checklist for engineers to follow to ensure successful executions. The teams launched, planning iterative improvements of that routine. Months passed by without any advancement in refining the operation process nor automating any part of it. To provide an overview of what needed to be done by the teams to run the batch jobs: Designating an engineer to lead the monthly process, coordinated using JIRA tickets Updating exemption lists and submitting pull requests to the code-base prior to initiating batch runs Ensuring data integrity by performing Athena queries across three different production databases within the AWS Console Manually initiating multiple batch jobs in a specified sequence with manual input of arguments in a live production setting Awaiting termination of the jobs and conducting a thorough review of the outcomes of the final batch jobs for each market Overall, this routine took around three business days per month and required two engineers to approve code changes and review the results of the batch runs. Considering new markets where fees may be rolled out towards, this workflow posed a significant challenge to maintaining high-quality standards and preventing potential incidents. Taking on The Challenge We recognized that continuing with our current process was unsustainable and bound to cause issues down the line. When the top ## Automate AWS Athena Partitions for Organization CloudTrail Logs and VPC Flow Logs DevFeed: [Automate AWS Athena Partitions for Organization CloudTrail Logs and VPC Flow Logs]() Original publisher: [Read original article]() Author: Aurelien Cerveaux Published: 2022-09-15T06:39:58Z Content type: tutorial Language: en Sources: [betclic-tech - Medium]() Topics: [AWS CloudTrail](), [Amazon Web Services](), [VPC Flow Logs](), [Amazon S3](), [AWS CloudFormation](), [DynamoDB](), [JSON]() Tags: [amazon-s3](), [athena](), [aws](), [aws-cloudtrail](), [cloud](), [cloudformation](), [cloudtrail](), [dynamodb](), [events](), [json](), [vpc-flow-logs]() ### AI overview A tutorial explains how to automate daily AWS Athena partition creation for organization-level CloudTrail and VPC Flow Logs. It describes using Amazon S3, AWS Glue Data Catalog, CloudFormation, SAM, Lambda, DynamoDB, and EventBridge to improve query efficiency and reduce scanned data and cost. ### Source excerpt This article's goal is to explain how to query efficiently, and at low cost with AWS Athena, AWS CloudTrail logs, or VPC Flow logs by automating the creation of Athena partitions daily. AWS CloudTrail AWS CloudTrail is a service that records all API Calls and Events for AWS Accounts, including details on calls made to your AWS Services and the Console as well. Activated at the Organization level, CloudTrail logs help you in several cases: Simplified Compliance Summarizing Accesses Security Analysis and Troubleshooting Monitoring Users and Resources Activities Governance and Investigation CloudTrail Logs are delivered in an S3 Bucket in JSON format through many small files that make these difficult to operate and request. VPC Flow Logs VPC Flow Logs is a feature that allows you to capture traffic information going to and from your network interfaces in your VPC. It helps you to: Analyze and troubleshoot network issues Determine the direction of your traffic Monitor the traffic Like CloudTrail Logs, Flow Logs are published in an S3 Bucket for all accounts in the Organization. AWS Athena AWS Athena is a query service that permits you to analyze data directly in an Amazon S3. You can create tables in Athena which use AWS Glue Data Catalog and are based on an S3 location. To query efficiently and with less scanned data and optimize the cost, we need to create some partitions. To know more about Tables, Databases, and Catalog in Athena. Automate Partitions Creation In our case, we are using CloudTrail Logs and VPC Flow Logs to detail the solution and the source code. All is done in CloudFormation and using SAM to deploy the Lambda. Global Overview We have several parts to automate the creation of our partitions. A common part containing: The Lambda is used to create partitions concerning different log types A DynamoDB table used to store partition names A specific part for each log type contains: The Glue Table with the proper structure An EventBridge Rule to trigger the