Melexis claims to have produced the world’s first surface-mount device (SMD) designed specifically for temperature monitoring of critical components in electric vehicle (EV) powertrain applications. The MLX90637 brings the benefits …

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Farnell  has launched The DevKit HQ, a new online resource that brings together evaluation boards, development kits, single board computers (SBCs), tools and technical documents in one place. A recent …

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Fabless companies are benefitting from CHIPs Act funding, with its impact on the talent pipeline, Jim Guzzo of Impinji told Electronics Weekly. Guzzo, the investment in fabs and the development …

The post The CHIPs Act is a positive for talent pipeline   appeared first on Electronics Weekly .

Prusa Research has revealed its eight-material 3D printer at Formnext in Frankfurt. Co-developed with Swedish 3D printer component maker Bondtech, it is a modification of Prusa’s ‘Core One’ 3D printer …

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AP7369Q is a series of 45V input low-drop-out linear regulators from Diodes that can provide up to 150mA to a load. Quiescent current can be as low as 2.1µA and, …

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Amazon Redshift announces support for the SUPER data type in databases with case insensitive collation, enabling analytics on semi-structured and nested data in these databases. Using the SUPER data type with PartiQL in Amazon Redshift, you can perform advanced analytics that combine structured SQL data (such as string, numeric, and timestamp) with the semi-structured SUPER data (such as JSON) with flexibility and ease-of-use.

This enhancement allows you to leverage the SUPER data type for your structured and semi-structured data processing needs in databases with case-insensitive collation. Using the COLLATE function, you can now explicitly specify case sensitivity preferences for SUPER columns, providing greater flexibility in handling data with varying case patterns. This is particularly valuable when working with JSON documents, APIs, or application data where case consistency isn’t guaranteed. Whether you’re processing user-defined identifiers or integrating data from multiple sources, you can now perform complex queries across both case-sensitive and case-insensitive data without additional normalization overhead.

Amazon Redshift support for the SUPER data type in databases with case insensitive collation is available in all AWS Regions, including the AWS GovCloud (US) Regions, where Amazon Redshift is available. See AWS Region Table for more details. To learn more about the SUPER data type in databases with case insensitive collation, please visit our documentation .

Amazon Redshift today announces the general availability of Just-In-Time (JIT) ANALYZE capability for Apache Iceberg tables, enabling users to run high performance read and write analytics queries on Apache Iceberg tables within the Redshift data lake. The Apache Iceberg open table format has been used by many customers to simplify data processing on rapidly expanding and evolving tables stored in data lakes.

Unlike traditional data warehouses, data lakes often lack comprehensive table-level and column-level statistics about the underlying data, making it challenging for query engines to choose the most optimal query execution plans without visibility into the table and column statistics. Sub-optimal query execution plans can lead to slower and less predictable performance.

‘JIT ANALYZE’ is a new Amazon Redshift feature that automatically collects and utilizes statistics for Iceberg tables during query execution, eliminating manual statistics collection while giving the query engine the information it needs to generate optimal query execution plans. The system uses intelligent heuristics to identify queries that will benefit from statistics, maintains lightweight sketch data structures, and builds high quality table-level and column-level statistics. JIT ANALYZE delivers out-of-the-box performance on par with queries that have pre-calculated statistics, while providing the foundation for many other performance optimizations.

The Amazon Redshift JIT ANALYZE feature for Apache Iceberg tables is now available in all AWS regions where Amazon Redshift is available. Users do not need to make any changes or enable any settings to take advantage of this new data lake query optimization. To get started, visit the documentation page for Amazon Redshift Management Guide .

AWS CloudFormation now offers capabilities that allow customers to catch deployment errors before resource provisioning begins and resolve them more efficiently. Change set creation now provides early feedback on common deployment errors. Stack events are now grouped by an operation ID with access through the new describe-operation API to accelerate analysis of deployment errors. This empowers developers to reduce deployment cycle times and cut troubleshooting time from minutes to seconds.

When you create a change set , CloudFormation now validates your template against three common failure causes: invalid property syntax, resource name conflict with existing resources in your account, and S3 bucket emptiness constraint on delete operations. If validation fails, the change set status shows ‘FAILED’ with a detailed status on the validation failure. You can then view details for each failure, including the property path associated with them, to pinpoint exactly where issues occur in your template. When you execute a validated change set, the deployment can still fail because of resource-specific runtime errors, such as resource limits or service-specific constraints. For troubleshooting runtime errors, every stack operation now receives a unique ID. You can zoom into the stack events for an operation and filter down to the events that caused the deployment to fail. This allows you to quickly identify root causes, reducing your troubleshooting time.

Get started by creating change sets through the CloudFormation Console, CLI, or SDK. View stack events by operation ID in the CloudFormation Console Events tab or via describe-events API. To learn more, visit the validate deployment and troubleshooting User Guide.

AWS Backup introduced a low-cost warm storage tier for Amazon S3 backup data that can reduce costs by up to 30%. After S3 backup data resides in a vault for 60 days (or longer based on your settings), you can move it to the new low-cost warm storage tier. The low-cost tier provides the same performance and features as the warm storage tier, including ransomware protection, recovery, and auditing.

Use the new low-cost warm storage tier to reduce storage costs for business, compliance or regulatory data you must retain long-term. With this launch, you can now configure automatic tiering for all S3 backups for all vaults in an account, a specific vault, or a bucket within a vault by setting an age threshold of 60 days or more. When you enable tiering, existing backup data beyond the threshold automatically moves to the low-cost warm tier, delivering immediate cost savings with no action required and no performance impact.

This low-cost storage tier is available in all AWS Regions where AWS Backup for Amazon S3 is available. There is a one-time transition fee when data moves to the low-cost warm tier. For additional pricing information, visit the AWS Backup pricing page .

To learn more about AWS Backup for Amazon S3, visit the product page and technical documentation . To get started, visit the AWS Backup console . 

Amazon Web Services (AWS) is launching flat-rate pricing plans with no overages for website delivery and security. The flat-rate plans, available with Amazon CloudFront, combine global content delivery with AWS WAF, DDoS protection, Amazon Route 53 DNS, Amazon CloudWatch Logs ingestion, and serverless edge compute into a simple monthly price with no overage charges. Each plan also includes monthly Amazon S3 storage credits to help offset your storage costs.

CloudFront flat-rate plans allow you to deliver your websites and applications without calculating costs across multiple AWS services. You won’t face the risk of overage charges, even if your website or application goes viral or faces a DDoS attack. Security features like WAF and DDoS protection are enabled by default, and additional configurations are simple to set up. When you serve your AWS applications through CloudFront instead of directly to the internet, your flat-rate plan covers the data transfer costs between your applications and your viewers for a simple monthly price without the worry of overages. This simplified pricing model is available alongside pay-as-you-go pricing for each CloudFront distribution, giving you the flexibility to choose the right pricing model and feature set for each application.

Plans are available in Free ($0/month), Pro ($15/month), Business ($200/month), and Premium ($1,000/month) tiers for new and existing CloudFront distributions. Select the plan tier with the features and usage allowances matching your application’s needs. To learn more, refer to the Launch Blog , Plans and Pricing , or CloudFront Developer Guide . To get started, visit the CloudFront console .