Today, we are announcing the launch of AWS AppConfig Feature Flags, which will enable you to move faster and safer while releasing new features to your customers. Feature flags allow you to release features to your applications, independent of code deployments. Development teams often coordinate application feature releases along with a large-scale marketing event and are required to release features gradually to the users. Similarly, DevOps teams often respond to operational events by enabling existing functionality in their application. This launch enables Developers and DevOps teams to use AWS AppConfig to create and validate feature flag configuration data and deploy single or multiple features flags to their application in a monitored and controlled way. AWS AppConfig, a feature of AWS Systems Manager, is used as a best practice by thousands of teams within Amazon to deploy feature flags and application configuration changes to applications at run-time.

Today, we are announcing the launch of the Amazon Monitron Web App. The Web App joins the existing Amazon Monitron Android App and iOS App, giving customers more options for using Amazon Monitron. Customers can now use the Amazon Monitron Web App from their desktops, laptops or tablets to monitor equipment and receive reports on operating behavior and alerts to potential failures in those equipment. They can access the Web app in a browser by clicking on the Amazon Monitron project link that can be found on the Amazon Monitron console. To commission the sensors and gateways, users will still need the Amazon Monitron Android App or iOS App since the commissioning process requires their phone’s Near Field Communication (NFC) and Bluetooth (BT) capabilities.  

Amazon Elastic Container Service (Amazon ECS) now supports Amazon ECS Exec for workloads running on Windows operating systems. Amazon ECS Exec, launched in March 2021, makes it easier for customers to troubleshoot errors, collect diagnostic information, interact with processes in containers during development, or get “break-glass” access to containers to debug critical issues encountered in production.

Amazon S3 on Outposts now delivers strong read-after-write and list-after-write consistency for any storage request at no additional cost. 

AWS Glue DataBrew now provides customers the ability to mask Personally Identifiable Information (PII) data during data preparation. With just a few clicks, you can detect PII data as part of a data profiling job and gather statistics such as number of columns that may contain PII and potential categories, then use built-in data masking transformations including substitution, hashing, encryption, decryption, and more, all without writing any code. You can then use the cleaned and masked datasets downstream for analytics, reporting, and machine learning tasks. 

AWS Glue DataBrew now has native console integration with Amazon AppFlow, allowing users to connect to data from Salesforce, Zendesk, Slack, ServiceNow, and other Software-as-a-Service (SaaS) applications, as well as AWS services like Amazon S3 and Amazon Redshift. When creating a new dataset in DataBrew, you can now create a flow via Amazon AppFlow that loads data (by schedule, event, or on-demand) into Amazon S3. Once the flow has been established to Amazon S3, you can easily clean, normalize, and transform this data in DataBrew and join it with datasets from other data stores or SaaS applications. DataBrew also provides information about when your flow was last refreshed and allows you to trigger flows directly from the DataBrew console. Learn more about supported AppFlow sources and destinations here .

AWS Glue DataBrew users can now create data quality rules, which are customizable validation checks that define business requirements for specific data. You can create rules to check for duplicate values in certain columns, validate that one column does not match another, or define many more custom checks and conditions based on your specific data quality use cases. You can group rules for a given dataset into a ruleset for efficiency and apply these checks as part of a standard data profile job. Results are populated in a data quality dashboard and validation report, helping you to quickly view rule outcomes and determine whether your data is fit for use.

AWS Glue DataBrew customers are now able to create datasets by writing Structured Query Language (SQL) statements to retrieve data from Amazon Redshift and Snowflake using Java Database Connectivity (JDBC) connections. You can use a purpose-built query to select the data you want and limit the data returned from large tables before cleaning, normalizing, and transforming that data with DataBrew. For a list of supported input formats, please see the AWS Glue DataBrew input formats list .

Contact Lens for Amazon Connect has now been included on the list of AWS Services in Scope for the FedRAMP Moderate baseline. The security and compliance of Contact Lens is assessed as part of multiple AWS compliance programs. Contact Lens is compliant with PCI and SOC, while also being a HIPAA eligible service. For a list of AWS services in scope of specific compliance programs, see AWS Services in Scope by Compliance Program. For general information, see AWS Compliance Programs.

Today the EC is discussing its chip plans. According to a draft of a policy paper seen by Bloomberg, the EC “may envisage approving public support to fill possible funding gaps in the semiconductor ecosystem for the establishment in particular of European first-of-a-kind facilities.” First-of-a-kind facilities are thought to mean bleeding edge fabs which rules …

This story continues at EU Pondering Chip Plans Today

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