Claiming the highest accuracy yet available from a power analyser, for its new WT5000.

This story continues at Yokogawa boosts power analyser accuracy to ±0.03%

Or just read more coverage at Electronics Weekly

GE’s Current is now collecting data from street lights, but the project remains in a pilot stage.

COB LED arrays will enable horticultural SSL form factors similar to HPS fixtures used broadly in indoor cannabis farms, and the LEDs will enable spectral options.

Amazon ElastiCache for Redis adds support for adding and removing read replica nodes for Redis Cluster, the sharded Redis. Now you can easily scale your reads and improve availability for your Redis Cluster environments without requiring manual steps or needing to make application changes. Amazon ElastiCache already supports adding and removing read replicas for unsharded Redis (non-Redis Cluster mode).

Amazon SageMaker now supports tagging for hyperparameter tuning jobs. With this new capability, customers can now add one or more tags to a tuning job that is launched with Automatic Model Tuning.

You can now use TensorFlow 1.9.0, the popular machine and deep learning framework, and S3 Select with Apache Spark on Amazon EMR release 5.17.0. Tensorflow libraries can be combined with big data processing engines like Spark on EMR to speed up the model training process by parallelizing the tuning of training parameters. The trained model can then be broadcast to all the nodes of the cluster to perform distributed inference on a large amount of data that are too big to run on a single node. TensorFlow on EMR is packaged with TensorBoard, a visualization tool, that helps you visualize and debug the flow of tensor graph in real-time, understand the effects of your design choices, and further optimize your model. TensorFlow builds on EMR vary by the instance type you use for your cluster.

With EMR release 5.17.0, you can use S3 Select with Spark. This feature allows your Spark application to selectively query a subset of data from a large object in S3. This improves performance by reducing the amount of data that needs to be transferred to and processed by the EMR cluster. Additionally, with this release, you can configure JupyterHub on EMR to save and persist notebooks directly to S3. You can also use the upgraded versions of Apache Flink 1.5.2, Apache HBase 1.4.6 and Presto 0.206.

You can create an Amazon EMR cluster with the release 5.17.0 by choosing the release label “emr-5.17.0” from the AWS Management Console, AWS CLI, or SDK. You can select TensorFlow, Flink, HBase, and Presto to install these applications when you launch your EMR cluster. Please visit the Amazon EMR documentation for more information about EMR release 5.17.0 , TensorFlow 1.9.0 , S3 Select with Spark , Flink 1.5.2 , HBase 1.4.6 , and Presto 0.206 .

Amazon EMR release 5.17.0 is now available in all supported regions for Amazon EMR .

You can stay up to date on EMR releases by subscribing to the feed for EMR release notes. Use the RSS icon at the top of the EMR Release Guide to link the feed URL directly to your favorite feed reader.

 

AWS has updated Live Streaming on AWS, a solution that automatically provisions the services necessary to build a highly resilient and scalable architecture that delivers your live video content worldwide. The solution now leverages the broadcast-grade features of AWS Elemental MediaLive to ingest your input feeds and transcode your content into two adaptive bitrate (ABR) HTTP Live Streaming (HLS) streams. MediaPackage packages those streams into HLS, Dynamic Adaptive Streaming over HTTP (DASH), and Microsoft Smooth Streaming (MSS) formats that are distributed through Amazon CloudFront. The solution also includes a demo HTML preview player that you can use to test the solution.

Amazon Aurora Parallel Query is a feature of the Amazon Aurora database that provides faster analytical queries over your transactional data. It can speed up your queries by up to 2 orders of magnitude, while maintaining high throughput for your core transactional workload. Read about it on the AWS Blog .

AWS Database Migration Service (AWS DMS) and the AWS Schema Conversion Tool (AWS SCT) make it easier for you to migrate your Apache Cassandra NoSQL databases to Amazon DynamoDB . Using AWS DMS and the AWS SCT, you can now migrate your Cassandra databases to DynamoDB and then replicate ongoing changes to your DynamoDB tables. After you have migrated a Cassandra database, you can benefit from the consistent, single-digit millisecond latency at any scale of DynamoDB.

Migrating from Cassandra to DynamoDB enables developers to focus on building products rather than managing and maintaining database infrastructure. The DynamoDB serverless provisioning model eliminates the need to overprovision database infrastructure. In addition, DynamoDB does not require specialized resourcing or licensing. As a result, customers run their DynamoDB-backed applications with up to a 70% TCO savings compared with Cassandra. Finally, DynamoDB global tables, backup and restore, and encryption at rest provide developers similar functionality as Cassandra, but with the benefits of these capabilities being easier to implement and without overhead or downtime.

The new IQXO-597 range of clock oscillators recently launched by IQD has an ultra-high frequency range of between 1GHz and 2.2GHz. Housed in a 14.0 x 9.0 x 3.3mm, 6 pad, this surface mount package has an FR4 base with a non-hermetically sealed metal lid. The IQXO-597 is available in three different outputs; Sine, Differential …

This story continues at IQD oscillators range between 1GHz and 2.2GHz

Or just read more coverage at Electronics Weekly