WTP: GitLab plugs flaws, DataDog gets paws on HashiCorp and more…

What's the point

GitLab has issued a critical security release for its Community and Enterprise Editions. One of the bugs fixed was an information disclosure issue that exposed project runner tokens to unauthorised users. The update also includes an update of Mattermost – the open source Chat platform which is integrated into GitLab – to v5.6.5, which addresses a further set of security issues.

DataDog bites into HashiCorp, Big Blue and more

DataDog has released a slew of new integrations over the last week. These include one for HashiCorp Vault, the secrets management tool, which will serve up alerts on changes, the status of Vault Clusters, and sniff out “secret sprawl”. Other additional integrations this week include one for Alibaba Cloud, which now covers services such as load balancers and managed databases. There is also one for IBM MQ Metrics – formerly WebSphere MQ – which if nothing else shows a new dog can learn old tricks.

Quarkus hits 0.12.0


Red Hat has announced Quarkus, the Kubernetes native Java framework it revealed earlier this month, has hit version 0.12.0 already. The update includes tweaks to the documentation, testing and metrics improvements, as well as “major changes” including the introduction of JSON-B and JSON-P extensions, support for Microsoft SQL Server.

New features hit Azure Boards, Pipelines

Microsoft has announced its latest Sprint updates for Azure Boards and Azure Pipelines. New features for Boards include the ability to navigate to work items directly from a GitHub comment, and GitHub Enterprise support for Azure Boards. Changes to Pipelines include support for Azure PowerShell Az module, the ability to approve Pipelines deployments from Slack, and 60 minutes of run time per pipeline job for Private projects.

AWS machine learners now certifiable

AWS has announced its Certified Machine Learning – Speciality certification for developers and data scientists who want to validate their ability to design, implement, deploy, and maintain ML solutions for given business problems. AWS said it would validate devs and other folks ability to select and justify the appropriate ML approach for a given business problem, and crucially – for AWS at least – identify the appropriate AWS service to use to implement a solution.

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