Today we are announcing Amazon CloudWatch Logs data protection, a new set of capabilities for Amazon CloudWatch Logs that leverage pattern matching and machine learning (ML) to detect and protect sensitive log data in transit. While developers try to prevent logging sensitive information such as Social Security numbers, credit card details, email addresses, and passwords, sometimes it gets logged. Until today, customers relied on manual investigation or third-party solutions to detect and mitigate sensitive information from being logged. If sensitive data is not redacted during ingestion, it will be visible in plain text in the logs and in any downstream system that consumed those logs. Enforcing prevention across the organization is challenging, which is why quick detection and prevention of…
New – Amazon CloudWatch Cross-Account Observability
Deploying applications using multiple AWS accounts is a good practice to establish security and billing boundaries between teams and reduce the impact of operational events. When you adopt a multi-account strategy, you have to analyze telemetry data that is scattered across several accounts. To give you the flexibility to monitor all the components of your applications from a centralized view, we are introducing today Amazon CloudWatch cross-account observability, a new capability to search, analyze, and correlate cross-account telemetry data stored in CloudWatch such as metrics, logs, and traces. You can now set up a central monitoring AWS account and connect your other accounts as sources. Then, you can search, audit, and analyze logs across your applications to drill down into…
New – A Fully Managed Schema Conversion in AWS Database Migration Service
Since we launched AWS Database Migration Service (AWS DMS) in 2016, customers have securely migrated more than 800,000 databases to AWS with minimal downtime. AWS DMS supports migration between 20+ database and analytics engines, such as Oracle to Amazon Aurora MySQL, MySQL to Amazon Relational Database (Amazon RDS) MySQL, Microsoft SQL Server to Amazon Aurora PostgreSQL, MongoDB to Amazon DocumentDB, Oracle to Amazon Redshift, and to and from Amazon Simple Storage Service (Amazon S3). Specifically, the AWS Schema Conversion Tool (AWS SCT) makes heterogeneous database and data warehouse migrations predictable and can automatically convert the source schema and a majority of the database code objects, including views, stored procedures, and functions, to a format compatible with the target engine. For…
AWS Application Migration Service Major Updates – New Migration Servers Grouping, Updated Launch, and Post-Launch Template
Last year, we introduced the general availability of AWS Application Migration Service that simplifies and expedites your migration to AWS by automatically converting your source servers from physical, virtual, or cloud infrastructure to run natively on AWS. Since the GA launch, we have made improvements, adding features such as agentless replication, MAP 2.0 auto-tagging and support for optional post-launch modernization actions. Today we announce three major updates of Application Migration Service to support your migration projects of any size: New Migration Servers Grouping – You can group migration servers into “applications,” a group of servers that function together as a single application, and manage the migration stage in “waves,” a plan of migrations including grouping servers and applications. Updated Launch…