With AWS Glue DataBrew, you can now visually detect outliers in data from your data lake, data warehouses, and other JDBC-accessible data sources. You can further handle outliers by replacing, removing, rescaling, or flagging them using mathematical and algorithmic methods such as z-score (to find the difference from mean value and divide it by the standard deviation), modified z-score (to calculate the difference from median absolute deviation), interquartile ranges (to calculate values between the first quartile and the third quartile) and one or more transformations such as creating a flag column, applying window functions, or choose from over 250+ other transformations.  

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. Using AWS Glue Workflows , you can orchestrate and execute a complex multi-job, multi-crawler data-integration workflow. AWS Glue custom blueprints make it easy for data engineers to create repeatable AWS Glue workflows.

You can now use the Amazon Redshift Data API in the AWS GovCloud (US) Regions to access Amazon Redshift. The Redshift Data API simplifies data access, ingest, and egress from languages supported with AWS SDK such as Python, Go, Java, Node.js, PHP, Ruby, and C++ so you can focus on building applications versus managing infrastructure.

AWS Config now supports the ability to capture and view the compliance history of AWS Config conformance packs. You can see how the overall compliance status of a conformance pack changed over time, and which rules within a conformance pack impacted the status change. You can aggregate conformance pack compliance data from multiple accounts and AWS Regions using AWS Config aggregators to get a centralized view of your compliance regimes and operational best practices. You can maintain up to 7 years of history. You can also run AWS Config advanced queries on this data for more details about your conformance pack compliance.

You can now configure each path segment of an API Gateway custom domain name to route requests to different APIs. Using multi-level base path mappings, you can implement path-based API versioning and migrate API traffic between APIs according to request paths with many segments.

Today, we are happy to announce that Amazon Fraud Detector now supports batch fraud predictions. Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Until today, Fraud Detector supported real-time fraud predictions via the GetEventPrediction API, which is ideal for low-latency synchronous fraud prediction use cases but requires customers to integrate an API and make an API call for every event they want to evaluate. Now, customers who have non real-time fraud prediction use cases can get fraud predictions for a large number of events in one go, or on an hourly or daily basis, using the new batch prediction feature without needing to write any code.  

When creating datasets in AWS Glue DataBrew from the Amazon S3 data lake, you can now create dynamic datasets to schedule data preparation on new incoming Amazon S3 files or apply transformations on filtered or conditionally chosen files or folders in S3. You can create a dynamic S3 path to choose files based on a time-window or time of last file update, and defining custom parameters to replace string, number, or date-based values in your S3 file path with filter conditions such as begins with, ends with, contains, does not contain, less than, greater than, before, and others. Custom parameter names can be included as columns in your datasets and the revised schema will be used for jobs running on dynamic datasets. With parameterized S3 paths and/or files, users can schedule to apply existing recipes to run on selected dynamic datasets.

Following the announcement of updates in MySQL database versions 5.6 , 5.7 , and 8.0 , we have updated Amazon Relational Database Service (Amazon RDS) for MySQL to support MySQL minor versions 5.6.51, 5.7.33, and 8.0.23. We recommend that customers upgrade to any of the latest minor versions to fix known security vulnerabilities in prior versions of MySQL, and to benefit from the numerous bug fixes, performance improvements, and new functionality added by the MySQL community. Learn more about upgrading your database instances in the Amazon RDS User Guide , including automatic minor version upgrades.

Starting today, AWS Gateway Load Balancer is available in the Canada (Central), Asia Pacific (Seoul) and Asia Pacific (Osaka) regions. With this launch, AWS Gateway Load Balancer is now available in 21 regions.

AWS Snowball Edge Compute Optimized is now available in the AWS Asia Pacific (Singapore) Region. Snowball Edge Compute Optimized is a secure, rugged device that brings AWS computing and storage capabilities, such as Amazon EC2, Amazon EBS, Amazon S3, AWS IoT Greengrass, AWS Lambda functions, and AWS IAM to your edge environments for machine learning, data analytics, processing, and local storage. You can use Snowball Edge devices in environments with intermittent connectivity (like manufacturing, industrial, and transportation) or in extremely remote locations (like military or maritime operations). These devices may also be rack mounted and clustered together to build larger installations.