Computing remained the leading IDM application field in 1Q24, accounting for 35% of the total share, up from 29% in the same period last year, says IDC. It was followed …

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Imec and ASML have patterned structures using High NA with the 0.55NA EUV scanner down to 9,5nm (19 nm pitch), random vias with 30nm center-to-center distance, 2D features at 22nm …

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Black Sesame International of Wuhan bombed on the Hong Kong stock exchange on Wednesday when its IPO – priced at HK$28 – saw its share price close at HK$20. Last …

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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.

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 .
 

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.

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).

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.

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 .
 

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.

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 .

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.

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

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.

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 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).

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 .