8/8/2024, 12:00:00 AM ~ 8/9/2024, 12:00:00 AM (UTC)

Recent Announcements

Amazon Cognito enhances Advanced Security Features (ASF) to detect additional risks and to cover custom authentication flows

Amazon Cognito enhances Advanced Security Features (ASF) to detect additional risk factors and cover custom authentication flows. ASF now identifies risks such as impossible travel, where a user signs in from two different locations in a time period implausible for travel between them. Additionally, ASF now detects risks in custom authentication flows. Customers can improve the security of applications that use custom authentication factors by enabling risk detection and adaptive authentication.\n Amazon Cognito simplifies the process of adding authentication, authorization, and user management to your web and mobile apps. The service provides authentication for applications with millions of users and supports sign-in with social identity providers such as Apple, Facebook, Google, and Amazon, and enterprise identity providers via standards such as SAML 2.0 and OpenID Connect. This new feature is now available as part of Cognito advanced security features in all AWS Regions, except AWS GovCloud (US) Regions. To get started, see the following resources:

Amazon Cognito threat protection using Advanced Security Features (ASF)

Amazon Cognito advanced security features pricing

Amazon EC2 C7i-flex instances are now available in US West (Oregon) Region

Starting today, Amazon Elastic Compute Cloud (Amazon EC2) C7i-flex instances that deliver up to 19% better price performance compared to C6i instances, are available in US West (Oregon) region. C7i-flex instances expand the EC2 Flex instances portfolio to provide the easiest way for you to get price performance benefits for a majority of compute intensive workloads. The new instances are powered by the 4th generation Intel Xeon Scalable custom processors (Sapphire Rapids) that are available only on AWS, and offer 5% lower prices compared to C7i.\n C7i-flex instances offer the most common sizes, from large to 8xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. With C7i-flex instances, you can seamlessly run web and application servers, databases, caches, Apache Kafka, and Elasticsearch, and more. For compute-intensive workloads that need larger instance sizes (up to 192 vCPUs and 384 GiB memory) or continuous high CPU usage, you can leverage C7i instances. C7i-flex instances are available in the following AWS Regions: US East (Ohio), US West (N. California, Oregon), Europe (Ireland, London, Paris, Spain, Stockholm), Canada (Central), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and South America (São Paulo). To learn more, visit Amazon EC2 C7i-flex instances.

AWS announces private IPv6 addressing for VPCs and subnets

AWS announces the general availability of private IPv6 addressing for VPCs and subnets with Amazon VPC IP Address Manager (IPAM). On AWS, private IPv6 addresses can take the form of Unique Local IPv6 Unicast Addresses (ULA) and Global Unicast Addresses (GUA), and can only be used for private access. These IPv6 addresses are not and cannot be advertised to the internet by AWS. Within IPAM, customers can configure private IPv6 addresses in a private scope, provision ULA and GUA, and use them to create VPCs and subnets for private access.\n Customers want private IPv6 address for the innate security boost it offers as resources using private IPv6 address cannot access the internet directly. It also provides assurance for compliance as customers can demonstrate that their resources with private IPv6 addresses are not internet accessible through a quick audit. These customers have no intention of directly routing traffic from these resources to the internet via AWS, and instead use proxies or network appliances for selective internet access via AWS, or route traffic through their on-premise network where the IPv6 address range is advertised to the internet. For such use cases, private IPv6 addressing helps simplify IP addressing and VPC network configuration in IPv6. Private IPv6 addressing for VPCs and subnets is now available in all AWS commercial regions and AWS GovCloud (US) regions, in both Free Tier and Advanced Tier VPC IPAM. To learn more about IPv6 addressing type, see the blog and VPC documentation, and to get started, please see the IPAM documentation page.

Amazon WorkSpaces Thin Client now supports Amazon WorkSpaces Pools

AWS today announced support for Amazon WorkSpaces Pools on Amazon WorkSpaces Thin Client. With this launch, Amazon WorkSpaces Thin Client customers have the flexibility to choose between Amazon WorkSpaces Personal, a fully persistent virtual desktop, and WorkSpaces Pools, a cost-effective, non-persistent virtual desktop, with support for existing Microsoft 365 Apps for enterprise licenses to help reduce cost and optimize agility.\n WorkSpaces Pools also simplifies management across a customer’s WorkSpaces environments by providing a single cloud-based tool set to manage the various desktop hardware configurations, storage, and applications for the user, including the ability to manage their existing Microsoft 365 Apps for enterprise. Administrators use AWS Application AutoScaling to automatically scale a pool of virtual desktops based on real-time usage metrics or predefined schedules. WorkSpaces Pools offers pay-as-you-go hourly pricing, helping to optimize costs. Amazon WorkSpaces Pools on WorkSpaces Thin Client is available in all AWS Regions where WorkSpaces Thin Client is available. To get started with WorkSpaces Pools on Amazon WorkSpaces Thin Client, log on to AWS Management Console, navigate to the WorkSpaces Thin Client service and follow the Amazon WorkSpaces Thin Client administration guide.

Amazon RDS for Db2 supports loading data from Amazon S3

Amazon Relational Database Service (Amazon RDS) for Db2 now supports loading data from Amazon S3 into Db2 databases.\n With Amazon RDS for Db2, you can use a Db2 client to load data into your Db2 databases using locally stored files on the client machine. However, the load cannot be performed if the locally stored data file includes large data objects such as Binary Long Objects (BLOBs), Character Large Objects (CLOBs), Extended Markup Language (XML), or Javascript Object Notation (JSON) greater than 32 KB. With this launch, you can now transfer such data files with large data objects to an S3 bucket and load it directly into Db2 databases on RDS. Refer RDS for Db2 documentation for the steps involved in setting up access to your S3 bucket and loading the data in Db2 databases on RDS. Amazon RDS makes it simple to set up, operate, and scale Db2 deployments in the cloud. Review the Amazon RDS for Db2 pricing page for pricing and regional availability.

Amazon Aurora supports PostgreSQL 16.3, 15.7, 14.12, 13.15, and 12.19

Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL versions 16.3, 15.7, 14.12, 13.15, and 12.19. These releases contain product improvements and bug fixes made by the PostgreSQL community, along with Aurora-specific improvements. Databases now startup faster after upgrades and restarts. Version 16.3 with IO-Optimized configuration includes performance enhancements that improve write throughput for 8xl and larger instances. These releases also contain Babelfish’s new features and improvements such as support for group AD, logical replication, Blue/Green Deployments, and LIKE operator for AI collations. As a reminder, Amazon Aurora PostgreSQL 12 support ends on Feb 29, 2025. Upgrade to a newer major version.\n You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more. These releases are available in all commercial AWS regions and AWS GovCloud (US) Regions, except China regions. For a full feature parity list, head to our feature parity page, and to see all regions that support Amazon Aurora head to our region page. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.

Announcing pgvector 0.7.0 support in Aurora PostgreSQL

Amazon Aurora PostgreSQL-Compatible Edition now supports pgvector 0.7.0, an open-source extension for PostgreSQL for storing vector embeddings in your database. pgvector provides vector similarity search capabilities enabling Aurora usage for semantic search and retrieval-augemented generation (RAG) in generative artificial intelligence (AI) applications.\n pgvector 0.7.0 adds parallelism to improve the Hierarchical Navigable Small Worlds (HNSW) index build time in Aurora. pgvector 0.7.0 adds two new vector data types: halfvec for storing dimensions as 2-byte floats, and sparsevec for storing up to 1,000 nonzero dimensions, and now supports indexing binary vectors using the PostgreSQL-native bit type. These additions let you use scalar and binary quantization for the vector data type using PostgreSQL expression indexes, which reduces index storage size and lowers index build time. Quantization also lets you increase the maximum dimensions of vectors you can index: 4,000 for halfvec and 64,000 for binary vectors. pgvector 0.7.0 is available in Amazon Aurora clusters running PostgreSQL 16.3, 15.7, 14.12, 13.15, and 12.19 and higher in all applicable AWS Regions except China regions, but including the AWS GovCloud (US) Regions. You can initiate a minor version upgrade by modifying your DB cluster. Please review the Aurora documentation to learn more. Amazon Aurora is designed for unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. It provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and integrations with other AWS services. To get started with Amazon Aurora, take a look at our getting started page.

AWS Glue Data Catalog views are now GA with Amazon Athena and Amazon Redshift

Today, AWS announces the general availability of AWS Glue Data Catalog views for Athena and Redshift. AWS Glue Data Catalog views are a new capability that allows customers to create, grant permissions on, and query multi-engine SQL views in AWS Glue Data Catalog from Amazon Athena and Amazon Redshift. With AWS Glue Data Catalog views you can create, share, and query views across AWS regions, accounts, and organizations.\n AWS Glue Data Catalog views allow customers to create views that can be queried from multiple engines without requiring consumers to have access to the tables referenced in the view. Administrators can use AWS Glue Data Catalog views to represent data restrictions and control what underlying data users can access using the rich SQL dialects provided by Amazon Redshift and Amazon Athena. Access to Glue Data Catalog views is managed with AWS Lake Formation permissions such as named resource grants, data filters, and lake formation tags. To enable easy auditing, all requests are logged in AWS Cloud Trail. AWS Glue Data Catalog Multi-Engine views are generally available in commercial AWS Regions where AWS Lake Formation, AWS Glue Data Catalog, Amazon Redshift, and Amazon Athena are available. To get started with this feature, refer to the below:

Blog

Lake Formation views Docs

Redshift views Docs

Athena views Docs

PostgreSQL 17 Beta 3 is now available in Amazon RDS Database preview environment

Amazon RDS for PostgreSQL 17 Beta 3 is now available in the Amazon RDS Database Preview Environment, allowing you to evaluate the pre-release of PostgreSQL 17 on Amazon RDS for PostgreSQL. You can deploy PostgreSQL 17 Beta 3 in the Amazon RDS Database Preview Environment that has the benefits of a fully managed database.\n PostgreSQL 17 includes updates to vacuuming that reduces memory usage, improves time to finish vacuuming, and shows progress of vacuuming indexes. With PostgreSQL 17, you no longer need to drop logical replication slots when performing a major version upgrade. PostgreSQL 17 continues to build on the SQL/JSON standard, adding support for JSON_TABLE features that can convert JSON to a standard PostgreSQL table. The MERGE command now supports the RETURNING clause, letting you further work with modified rows. PostgreSQL 17 also includes general improvements to query performance and adds more flexibility to partition management with the ability to SPLIT/MERGE partitions. Please refer to the PostgreSQL community announcement for more details. Amazon RDS Database Preview Environment database instances are retained for a maximum period of 60 days and are automatically deleted after the retention period. Amazon RDS database snapshots that are created in the preview environment can only be used to create or restore database instances within the Preview Environment. You can use the PostgreSQL dump and load functionality to import or export your databases from the Preview Environment. Amazon RDS Database Preview Environment database instances are priced as per the pricing in the US East (Ohio) Region.

AWS Snowball Edge Storage Optimized 210TB device offers lower capacity pricing option

AWS Snowball Edge Storage Optimized 210TB device now offers a 100TB pricing option for data migration. With this offering, the AWS Snowball Edge Storage Optimized 210TB device supports two pricing options for data migration: less than 100TB, and from 100TB to 210TB pricing. In addition, the AWS Snowball Edge Storage Optimized 210 device is now available in the following additional regions: Africa (Cape Town), Asia Pacific (Jakarta), Canada (Central), Europe (Stockholm), and Europe (Milan).\n For the majority of data migration workloads, customers should use AWS DataSync as a secure, online service that automates and accelerates moving data between on premises and AWS Storage services. When bandwidth is limited, or a connection is intermittent, customers can use AWS Snowball Edge Storage Optimized 210TB for offline data migration. The 100TB pricing option is available in all AWS Regions where the AWS Snowball Edge Storage Optimized 210TB is available. Learn more, visit the AWS Snowball Pricing, Snow product page and Snow Family documentation.

Amazon EMR 7.2 now supports Apache Spark 3.5.1

Today, we are excited to announce that the Amazon EMR 7.2 release is now generally available and includes Apache Spark 3.5.1, Trino 436, and PrestoDB 0.285, Apache Iceberg 1.5.0 and Delta 3.1. Furthermore, with Amazon EMR 7.2, you can view additional Amazon CloudWatch metrics for enhanced monitoring in the Amazon EMR console, which provides comprehensive monitoring capabilities, allowing you to track the performance and health of your cluster more effectively.\n You can configure the Amazon CloudWatch Agent to publish metrics for Apache Hadoop, YARN, and Apache HBase applications running on your Amazon EMR on EC2 clusters, and track the metrics of each cluster within the EMR console. In addition, Amazon EMR 7.2 adds support for Apache Flink Operator 1.8 with Amazon EMR on EKS. Amazon EMR release 7.2 is now available in all regions where Amazon EMR is available. See Regional Availability of Amazon EMR, and our release notes for more detailed information. To learn how to enable Amazon CloudWatch Agent metrics, view the documentation.

Amazon RDS for PostgreSQL supports minor versions 16.4, 15.8, 14.13, 13.16, and 12.20

Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions 16.4, 15.8, 14.13, 13.16, and 12.20. This release of RDS for PostgreSQL also includes updates for PostgreSQL extensions such as pgvector 0.7.3, hypopg 1.4.1, and others.\n We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes added by the PostgreSQL community. You are able to leverage automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance window. Learn more about upgrading your database instances in the Amazon RDS User Guide. Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See Amazon RDS for PostgreSQL Pricing for pricing details and regional availability. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.

AWS Glue announces GA of new ML-powered Glue Data Quality capability

AWS Glue announces general availability of a new AWS Glue Data Quality(Glue DQ) capability that uses ML-powered anomaly detection algorithms to detect hard-to-find data quality issues and anomalies. This helps customers proactively identify and fix data quality issues.\n Data engineers and analysts use rules in Glue DQ to measure and monitor their data. While Glue DQ’s existing rule-based approach works well for known data patterns, it may miss unexpected anomalies . Now, data engineers and analysts can use Glue DQ’s Anomaly Detection capability to easily detect unanticipated data quality issues. To use this feature, customers can write rules or analyzers and then turn on Anomaly Detection in Glue ETL. Glue DQ collects statistics for columns specified in rules and analyzers, applies ML algorithms to detect anomalies, and generates easy-to-understand visual observations explaining the detected issues. Customers can use recommended rules to capture the anomalous patterns and provide feedback to tune the ML model for more accurate detection. To learn more, visit read the blog, watch the introductory video, or refer to the documentation. This capability is available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Stockholm), Europe (Frankfurt), Asia Pacific (Singapore), Asia Pacific (Sydney), and Asia Pacific (Tokyo).

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