I’ve had to ask myself: Where is all this Euro-dosh coming from? and How do I get my maulers on it? Ed confides to his diary, US ditching NATO? OK …

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On March 3, Longsys (SZ.301308), a branded semiconductor memory enterprise, made its first-ever appearance at MWC 2025 in Barcelona. Under the theme “New Mode of Storage Empowering Global Mobility”, it …

The post Sponsored Content: Longsys Debuts at MWC25, Unleashing the Power of Storage Innovation in Mobile Communications appeared first on Electronics Weekly .

AWS Storage Gateway expands availability to the AWS Asia Pacific (Thailand) Region enabling customers to deploy and manage hybrid cloud storage for their on-premises workloads.

AWS Storage Gateway is a hybrid cloud storage service that provides on-premises applications access to virtually unlimited storage in the cloud. You can use AWS Storage Gateway for backing up and archiving data to AWS, providing on-premises file shares backed by cloud storage, and providing on-premises applications low latency access to data in the cloud.

Visit the AWS Storage Gateway product page to learn more. Access the AWS Storage Gateway console to get started. To see all the Regions where AWS Storage Gateway is available, please visit the AWS Region table .
 

Amazon S3 Access Grants now authenticate based on the union of both Identity Provider (IdP) and AWS Identity and Access Management (IAM) permissions. This means customers can use AWS machine learning and analytics services such as Amazon SageMaker Unified Studio, Amazon Redshift, and AWS Glue to request access to their S3 data, and S3 Access Grants will grant access to their data after evaluating both their IdP and IAM permissions.

Now, S3 Access Grants evaluate both IAM and IdP permissions so you no longer have to choose between identity contexts when requesting access to S3. With just a few clicks in the AWS Management Console or a few lines of code using the AWS SDK, you can map S3 permissions to users and groups in an existing corporate directory, such as Entra ID and Okta, or to an IAM user or role. S3 Access Grants automatically update S3 permissions based on end user group membership as users are added and removed from groups in the IdP.

Amazon S3 Access Grants are available in all AWS Regions where AWS IAM Identity Center is available . For pricing details, visit Amazon S3 pricing . To learn more about S3 Access Grants, visit the S3 User Guide .
 

Today, we are excited to announce the general availability of Amazon Data Firehose (Firehose) integration with Amazon S3 Tables, a feature that enables customers to deliver real-time streaming data into Amazon S3 Tables without requiring any code development or multi-step processes.

Firehose can acquire streaming data from Amazon Kinesis Data Streams, Amazon MSK, Direct PUT API, and AWS Services such as AWS WAF web ACL logs, Amazon VPC Flow Logs. It can then deliver this data to destinations like Amazon S3, Amazon Redshift, OpenSearch, Splunk, Snowflake, and others for analytics. Now, with the Amazon S3 Table integration, customers can stream data from any of these sources directly into Amazon S3 Tables. As a serverless service, Firehose allows customers to simply setup a stream by configuring the source and destination properties, and pay based on bytes processed.

The new feature also enables customers to route records in a data stream to different Amazon S3 tables based on the content of the incoming record. Additionally, customers can automate processing for data correction and right-to-forget scenarios by applying row-level update or delete operations in the destination S3 tables.

To get started, visit Amazon Data Firehose documentation and console .
 

AWS CodePipeline now enables direct pipeline-to-pipeline invocation with a new native action. This feature simplifies triggering downstream pipeline executions and passing pipeline variables and source revisions between pipelines.

The new CodePipeline Invoke action eliminates the need for workarounds like configuring CodeBuild projects or using the Commands action with custom shell commands. You can now directly specify subsequent pipelines to be executed with pipeline variables and source revisions. For example, when using separate pipelines for Docker image building and deployment, you can pass image digests between pipelines seamlessly. The action also supports cross-account pipeline triggering.

To learn more about using the CodePipeline Invoke action in your pipeline, visit our documentation . For more information about AWS CodePipeline, visit our product page . This new action is available in all regions where AWS CodePipeline is supported, except the AWS GovCloud (US) Regions and the China Regions.
 

Amazon EMR Serverless is now a FedRAMP High authorized service in the AWS GovCloud (US-East) and AWS GovCloud (US-West) Regions. Federal agencies, public sector organizations and other enterprises with FedRAMP High compliance requirements can now leverage EMR Serverless to run Apache Spark and Hive workloads.

Amazon EMR Serverless is a serverless option that makes it simple for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. The Federal Risk and Authorization Management Program (FedRAMP) is a US government-wide program that delivers a standard approach to the security assessment, authorization, and continuous monitoring for cloud products and services.

To get started with Amazon EMR Serverless, visit the User Guide .
 

Amazon Kinesis Data Streams now allows customers to make API requests over Internet Protocol version 6 (IPv6). Customers now have the option of using either IPv6 or IPv4 when sending requests over dual-stack public endpoints.

Kinesis Data Streams allows users to capture, process, and store data streams in real time at any scale. IPv6 increases the number of available addresses by several orders of magnitude, so customers will no longer need to manage overlapping address spaces. Many devices and networks today already use IPv6, and now they can easily write to and read from data streams.

Support for IPv6 with Kinesis Data Streams is available in all Regions where Kinesis Data Streams is available, except for AWS GovCloud (US) and China Regions. See here for a full listing of our Regions. To learn more about Kinesis Data Streams, please refer to our Developer Guide .

Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple for data engineers and data scientists to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers. Today, we are excited to announce that Amazon EMR Serverless Streaming jobs, which enables you to continuously analyze and process streaming data, is now available in the AWS GovCloud (US) Regions.

Streaming has become vital for businesses to gain continuous insights from data sources like sensors, IoT devices, and web logs. However, processing streaming data can be challenging due to requirements such as high availability, resilience to failures, and integration with streaming services. Amazon EMR Serverless Streaming jobs has built-in features to addresses these challenges. It offers high availability through multi-AZ (Availability Zone) resiliency by automatically failing over to healthy AZs. It also offers increased resiliency through automatic job retries on failures and log management features like log rotation and compaction, preventing the accumulation of log files that might lead to job failures. In addition, Amazon EMR Serverless Streaming jobs support processing data from streaming services like self-managed Apache Kafka clusters, Amazon Managed Streaming for Apache Kafka, and now is integrated with Amazon Kinesis Data Streams using a new built-in Amazon Kinesis Data Streams Connector , making it easier to build end-to-end streaming pipelines.

To get started, visit the Amazon EMR Serverless Streaming jobs page in the Amazon EMR Serverless User Guide.

AWS CodePipeline V2 type pipeline introduces CodeBuild rule and Commands rule that customers can use in their stage level condition to gate a pipeline execution. You can use CodeBuild rule to start a CodeBuild build or Commands rule to run simple shell commands before exiting a stage, when all actions in the stage have completed successfully, or when any action in the stage has failed.

These new rules will provide more flexibility to your deployment process and enable more release safety controls. With these two rules, you can run integration tests as a stage level condition when your deployment completes and automatically roll back or fail your deployment when the integration tests fail. You can also run custom cleanup scripts using these new rules when the stage execution fails.

To learn more about using these rules in stage level conditions in your pipeline, visit our documentation . For more information about AWS CodePipeline, visit our product page . This feature is available in all regions where AWS CodePipeline is supported.