The reptiles have dug up some old CVs of mine which appear to contain inaccuracies – in fact they do contain inaccuracies – but what of it? Who hasn’t told …

The post Ed’s Dodgy CV appeared first on Electronics Weekly .

Amazon OpenSearch Ingestion now allows you to leverage AWS Lambda for event processing and routing, enabling complex transformation and enrichment of your streaming data. Customers can now define custom Lambda functions in their OpenSearch Ingestion pipelines for use cases like generating vector embedding and lookups in external databases to power advanced search use cases.

OpenSearch Ingestion gives you the option of either using only Lambda functions or chaining Lambda functions with native Data Prepper processors when transforming data. You can also batch events into a single payload based on event count and size before invoking Lambda to optimize the number of Lambda invocations to reduce costs and improve throughput. Furthermore, you can use this feature with the inbuilt conditional expressions in Amazon OpenSearch Ingestion to enable use cases like sending out emails and notifications for real-time alerting.

This feature is available in all the 15 AWS commercial regions where Amazon OpenSearch Ingestion is currently available: US East (Ohio), US East (N. Virginia), US West (Oregon), US West (N. California), Europe (Ireland), Europe (London), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Sydney), Asia Pacific (Singapore), Asia Pacific (Mumbai), Asia Pacific (Seoul), Canada (Central), South America (Sao Paulo), and Europe (Stockholm).

To learn more, see the Amazon OpenSearch Ingestion webpage and the Amazon OpenSearch Service Developer Guide .

AWS Step Functions announces support for two new capabilities: Variables and JSONata data transformations. Variables allow developers to assign data in one state and reference it in a subsequent state, simplifying state payload management, reducing the need to pass data through multiple intermediate states. With support for JSONata, an open source query and transformation language, customers can now perform advanced data manipulation and transformation such as date and time formatting, and mathematical operations. Additionally, when using JSONata, we have simplified input and output processing by reducing the number of JSON transformation fields required to call services and pass data to the next state.

AWS Step Functions is a visual workflow service capable of orchestrating over 14,000 API actions from over 220 AWS services to build distributed applications and data processing workloads. With support for Variables and JSONata, developers can build distributed serverless applications faster and more efficiently with enhanced payload management capabilities. These features also reduce the need for custom code, lowering costs and reducing the number of state transitions needed to construct and pass data between states.

Variables and JSONata are available at no additional cost in: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland and Frankfurt), and Asia Pacific (Tokyo, Seoul, Singapore, and Sydney) with the remaining regions to follow in the coming days. We have also partnered with LocalStack and Datadog to ensure that their local emulation and observability experiences are updated to support Variables and JSONata. To learn more, please visit:

• Documentation
• Blog Post
• Introductory Video
• LocalStack Blog
• Datadog Blog

Amazon QuickSight now supports the ability to customize fonts across specific visuals. Authors can now completely customize fonts for Table and Pivot table, while for remaining visuals they can customize fonts for specific properties including title, subtitle, legends title and legends values.

Authors can set the font size(in pixels), font family, color, and styling options like bold, italics, and underline across analysis, including dashboard, reports and embedded scenarios. With this update, you can align the dashboard’s fonts with your organization’s branding guidelines, creating a cohesive and visually appealing experience. Additionally, the font customization options can help improve the readability and meet accessibility standards, especially when viewing visuals on a large screen.

Font customization for above listed visuals is now available in all supported Amazon QuickSight regions – US East (Ohio and N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Jakarta, Mumbai, Seoul, Singapore, Sydney and Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Milan, Paris, Stockholm, Zurich), South America (São Paulo) and AWS GovCloud (US-West).
 

AWS Application Discovery Service (ADS) now supports AWS PrivateLink , providing private connectivity between virtual private clouds (VPCs), on-premises networks and ADS without exposing traffic to the public internet. With this integration, administrators can use VPC endpoint policies to seamlessly route their discovery data from either the ADS Agentless Collector or ADS Discovery Agent directly into ADS for analysis and migration planning.

This new feature is available in all AWS Regions where AWS Application Discovery Service and AWS PrivateLink are available.

To get started, see the AWS PrivateLink section of AWS Application Discovery Service user guide .

We are excited to announce that AWS HealthOmics sequence stores now support cross account read access to simplify data sharing and tool integration. AWS HealthOmics is a fully managed service that empowers healthcare and life science organizations to store, query, analyze omics data to generate insights to improve health and drive scientific discoveries. With this release, customers can enable secure data sharing with partners, while maintaining auditability and compliance frameworks.

Cross account reading for S3 API enables customers to write resource policies to manage sharing and restrict data reading based on their needs. Through the use of tag propagation and tag-based access control , users can create policies that share read access beyond their account while having a scalable mechanism to granularly restrict files based on their compliance structures. In addition, S3 access logs can be used to audit and validate access ensuring the data customers manage remains properly controlled.

Cross account S3 API access is now supported in all regions where AWS HealthOmics is available: US East (N. Virginia), US West (Oregon), Europe (Frankfurt, Ireland, London), Asia Pacific (Singapore), and Israel (Tel Aviv).

To get started, see the AWS HealthOmics documentation.
 

AWS Glue announces generative AI upgrades for Apache Spark, a new generative AI capability that enables data practitioners to quickly upgrade and modernize their existing Spark jobs. Powered by Amazon Bedrock, this feature automates the analysis and updating of Spark scripts and configurations, reducing the time and effort required for Spark upgrades from weeks to minutes.

AWS Glue is a serverless, scalable data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning, and application development. With Spark Upgrades, you can initiate automated upgrades with a single click in the AWS Glue console to modernize your Spark jobs from an older version to AWS Glue version 4.0. This feature analyzes your Python-based Spark jobs and generates upgrade plans detailing code changes and configuration modifications. It leverages generative AI to iteratively improve and validate the upgraded code by executing test runs as Glue jobs. Once validation is successful, you receive a detailed summary of all changes for review, enabling confident deployment of your upgraded Spark jobs. This automated approach reduces the complexity of Spark upgrades while maintaining the reliability of your data pipelines.

The generative AI upgrades for Apache Spark preview is available for AWS Glue in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Asia Pacific (Sydney). To learn more, visit the AWS Glue website , read the Launch blog , or read the documentation .
 

AWS Glue announces generative AI troubleshooting for Apache Spark, a new capability that helps data engineers and scientists quickly identify and resolve issues in their Spark jobs. Spark Troubleshooting uses machine learning and generative AI technologies to provide automated root cause analysis for Spark job issues, along with actionable recommendations to fix identified issues.

AWS Glue is a serverless, scalable data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning, and application development. With Spark troubleshooting, you can initiate automated analysis of failed jobs with a single click in the AWS Glue console. This feature provides root cause analysis and remediation steps for hard-to-diagnose Spark issues like memory errors, data skew problems, and resource not found exceptions. This helps you reduce downtime in critical data pipelines. Powered by Amazon Bedrock, Spark troubleshooting reduces debugging time from days to minutes.

The generative AI troubleshooting for Apache Spark preview is available for jobs running on AWS Glue 4.0, and in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Ireland), US East (Ohio), and more. To learn more, visit the AWS Glue website , read the Launch blog , or read the documentation .
 

Today AWS announces an integration between AWS Application Discovery Service (ADS) and AWS Application Migration Service (MGN), which allows data collected about your on-premises workloads to directly feed into your migration execution plan. This new capability provides a one-click export of the on-premises server configuration, tags, application grouping, and Amazon EC2 recommendations gathered during planning in a format supported by MGN.

ADS provides a system of record for configuration, performance, tags, and application groupings of your existing on-premises workloads. Now when using the Amazon EC2 instance recommendations feature, you also are provided an MGN-ready inventory file. This file can then be directly imported into MGN, removing the need to rediscover your workloads.

This new no-cost capability is available in all AWS Regions where AWS Application Discovery Service is available.

To learn more, please see the user guides for AWS Application Discovery Service and AWS Application Migration Service .
 

AWS Lambda announces Provisioned Mode for event source mappings (ESMs) that subscribe to Apache Kafka event sources, a feature that allows you to optimize the throughput of your Kafka ESM by provisioning event polling resources that remain ready to handle sudden spikes in traffic. Provisioned Mode helps you build highly responsive and scalable event-driven Kafka applications with stringent performance requirements.

Customers building streaming data applications often use Kafka as an event source for Lambda functions, and use Lambda’s fully-managed MSK ESM or self-managed Kafka ESM , which automatically scale polling resources in response to events. However, for event-driven Kafka applications that need to handle unpredictable bursts of traffic, lack of control over the throughput of ESM can lead to delays in your users’ experience. Provisioned Mode for Kafka ESM allows you to fine-tune the throughput of the ESM by provisioning and auto-scaling between a minimum and maximum number of polling resources called event pollers, and is ideal for real-time applications with stringent performance requirements.

This feature is generally available in all AWS Commercial Regions where AWS Lambda is available, except Israel (Tel Aviv), Asia Pacific (Malaysia), and Canada West (Calgary).

You can activate Provisioned Mode for MSK ESM or self-managed Kafka ESM by configuring a minimum and maximum number of event pollers in the ESM API, AWS Console, AWS CLI, AWS SDK, AWS CloudFormation, and AWS SAM. You pay for the usage of event pollers, along a billing unit called Event Poller Unit (EPU). To learn more, read Lambda ESM documentation and AWS Lambda pricing .