Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations to detect security vulnerabilities, improve code quality and identify an application’s most expensive lines of code.
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Today, we are excited to announce the First Annual AWS BugBust re:Invent challenge. Java and Python developers of all skill levels, can compete to fix as many software bugs as possible to earn points and climb the global leaderboard. There will be an array of prizes, from hoodies and fly swatters to Amazon Echo Dots, available to participants who meet certain milestones in the challenge. There’s also the coveted title of “Ultimate AWS BugBuster” accompanied by a cash prize of $1500 for whomever earns the most points by squashing bugs during the event.
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Amazon Personalize now offers intelligent user segmentation which allows you to run more effective prospecting campaigns through your marketing channels. Traditionally, user segmentation has relied on demographic information and manually curated business rules to make assumptions about users’ intentions and assign them to pre-defined audience segments. Amazon Personalize uses machine learning techniques to learn about your items, users, and how your users interact with your items. Amazon Personalize segments users based on their preferences for different products, categories, brands, and more. This can help you drive higher engagement with marketing campaigns, increase retention through targeted messaging, and improve the return on investment for your marketing spend.
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Today, we are announcing the public preview of a new feature that allows you to use AWS Chatbot to manage AWS resources and remediate issues in AWS workloads by running AWS CLI commands from Slack channels. Previously, you could only monitor AWS resources and retrieve diagnostic information using AWS Chatbot.
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Today, Amazon Personalize is excited to announce recommenders which are optimized to deliver personalized experiences for common use cases in Media & Entertainment and Retail. It is now faster and easier to deliver high performing personalized user experiences in your applications without any ML expertise required. Recommenders reduce the time needed to build and deliver personalized experiences and fully manage the lifecycle of the experience to help ensure you recommend what is most relevant to your users.
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AWS IoT RoboRunner is a new robotics service that makes it easier for enterprises to build and deploy applications that help fleets of robots work together seamlessly. With AWS IoT RoboRunner, it is easier to build applications that make it possible to interoperate and orchestrate robots from a single view by reducing the complex development work required to connect robots to each other and the rest of your industrial software systems.
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AWS App2Container(A2C) now supports Jenkins for setting up a CI/CD pipeline to automate building and deploying application in containers on AWS. With this new integration, customers can configure their existing Jenkins pipeline in the current Jenkins environment for managing automated build and deployment of containerized applications.
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AWS Single Sign-On (AWS SSO) is now in scope for AWS SOC 1 , SOC 2, and SOC 3 reports. You can now use AWS SSO in applications requiring audited evidence of the controls in our System and Organization Controls (SOC) reporting. For example, if you use AWS to manage access to accounts and applications, you can use the SOC reports to help meet your compliance requirements for those use cases. AWS SOC reports are independent third-party examination reports that demonstrate how AWS achieves key compliance controls and objectives.
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AWS Lambda now provides content filtering options for SQS, DynamoDB and Kinesis as event sources. With event pattern content filtering, customers can write complex rules so that their Lambda function is only triggered by SQS, DynamoDB, or Kinesis under filtering criteria you specify. This helps reduce traffic to customers’ Lambda functions, simplifies code, and reduces overall cost.
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Effective December 1, 2021, AWS is making two pricing changes for data transfer out to the internet. Each month, the first terabyte of data transfer out of Amazon Cloudfront, the first 10 million HTTP/S requests, and the first 2 million CloudFront Functions invocations will be free. Free data transfer out of CloudFront is no longer limited to the first 12 months. In addition, the first 100 gigabytes per month of data transfer out from all AWS Regions (except China and GovCoud) will be free. Free data transfer out from AWS Regions is also no longer limited to the first 12 months. These changes will replace the existing data transfer and CloudFront AWS Free Tier offerings, and AWS customers will see these changes automatically reflected in their AWS bills going forward. All AWS customers will benefit from these pricing changes, and millions of customers will see no data transfer charges as a result.
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Amazon Redshift data sharing allows you to share live, transactionally consistent data across different Redshift clusters without the complexity and delays associated with data copies and data movement. Data sharing now adds several performance enhancements including result caching, and concurrency scaling allowing you to support broader set of analytics applications and meet critical performance SLAs when querying shared data.
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Amazon Managed Grafana announces new data source plugins for Amazon Athena and Amazon Redshift, enabling customers to query, visualize, and alert on their Athena and Redshift data from Amazon Managed Grafana workspaces. Amazon Managed Grafana now also supports CloudFlare, Zabbix, and Splunk Infrastructure Monitoring data sources as well as the Geomap panel visualization and open source Grafana version 8.2.
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Now on EC2 Image Builder, customers can share their Amazon Machine Images (AMIs) with AWS Organizations and Organizational Units (OUs) in the image distribution phase of their build process. As their organization structure changes, customers no longer have to manually update AMI permissions for individual AWS accounts in their organization. Customers can create OUs within AWS Organizations and manage AMI permissions for AWS accounts within those OUs.
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With Amazon EC2 Auto Scaling’s new predictive scaling policy, you can now use custom metrics to predict the EC2 instance capacity needed by an Auto Scaling group. Predictive scaling proactively increases the capacity of an Auto Scaling group to meet predicted demand. For workloads that experience recurring, steep demand changes, predictive scaling can help improve your application’s responsiveness without having to overprovision capacity, resulting in lower EC2 costs. Custom metrics are useful when the predefined metrics (CPU Utilization, Network I/O, and ALB Request Count) are not sufficient to capture the load on your application. Previously, you could only use custom metrics with step scaling and target tracking, but you can now use them with predictive scaling as well.
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The Amazon Chime SDK lets developers add real-time audio, video, screen-sharing, and messaging capabilities to their web or mobile applications. The Amazon Chime SDK now offers machine learning (ML) based echo reduction to help improve audio experiences. Acoustic echoes disrupt meetings or conference calls when the sound played by the loudspeaker is picked up by the microphone and it circulates back into the call. The new ML-based echo reduction capability is designed to reduce acoustic echoes and preserve voice quality during double-talk conditions, when two or more people speak at the same time.
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Elastic Beanstalk now supports AWS Graviton-based Amazon Elastic Compute Cloud (Amazon EC2) instance types. AWS Graviton is an arm64-based processor built by Amazon that provides up to 40% better price-performance over a comparable x86-based processor. AWS Graviton on Elastic Beanstalk enables customers to benefit from the superior price-performance of arm64-based processors along with the ease-of-use of Elastic Beanstalk.
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AWS Database Migration Service (AWS DMS) has expanded functionality by adding support for Google Cloud SQL for MySQL as a source. Using AWS DMS, you can now perform live migrations from Google Cloud SQL for MySQL to any AWS DMS supported targets.
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Elastic Fabric Adapter (EFA) now supports new instance sizes within the Amazon EC2 compute-optimized, GPU, and dense SSD storage instance types that support EFA. Until now, EFA could be enabled for select bare-metal instances or for the largest instance size that support EFA. Starting today, you can associate EFA with additional sizes within Amazon C5, G4, and I3 instance types. By enabling EFA for smaller instance sizes that match the performance requirements of your application, you can lower costs.
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Amazon QuickSight now supports dataset versioning, which allows dataset owners to understand historical changes within a dataset, preview a specific version, or revert back to a previous version if needed. Dataset versions can be viewed and tracked via the UI, allowing dataset owners to view versions and switch to a specific version via UI. Dataset Versions gives dataset authors the confidence to experiment with their content, knowing that their older versions are available and that they easily can revert back to it when required.
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Editor’s note: When Europe’s largest mobile communications company, Vodafone, was asked by the European Commission to help understand population movement across the European Union and the UK to help fight COVID-19, it was able to provide anonymized mobile network-based insights to answer the call. Here’s how Vodafone, with the support of Google Cloud, rapidly mobilized the COVID-19 frontline, while respecting its customers’ privacy.
With the emergence of COVID-19 in early 2020, the European Commission—the executive branch of the European Union (EU)—knew that technology would be instrumental in its fight to control the pandemic. With various lockdowns imposed across its member states, the Commission was keen to predict and prevent the spread of COVID-19 and to manage the related social, political and financial impacts.
Mobile networks produce location data, which can be turned into useful anonymous insights to understand population movement within a geographic area. The European Commission, working with mobile industry association GSMA (Groupe Speciale Mobile Association), asked Europe’s major mobile phone operators for help in producing insights to support the fight against COVID-19. As the largest mobile network operator within the EU, Vodafone saw this as a critical opportunity to participate.
Vodafone had previous experience of using mobile network data to support pandemic research. For example, in 2019, Vodafone provided mobility pattern analysis to help track the spread of Malaria in Mozambique. And, during the early stages of the COVID-19 pandemic (prior to working with the European Commission), Vodafone assisted the Italian and Spanish governments in understanding their citizens’ mobility patterns. Vodafone had also previously offered anonymized and aggregated population mobility insights to support public transport and tourism authorities and retail organizations in a number of countries. Consequently, Vodafone was perfectly placed to play a greater role in supporting the European Commission’s response to the pandemic.
When asked to assist the European Commission, Vodafone first considered how it could safely share its data with the governing body without providing details on the individual movements of its customers. It realized it could achieve this through an elaborate set of anonymization and aggregation techniques. Insights are aggregated from a minimum of 50 users and Vodafone only shared these anonymous insights and never the actual raw data with the Commission. As specified by the EU, these insights are then presented onto a large geographical region, typically a city or a county with thousands of people living in that area.
These insights illustrate how people move, helping to determine how lockdowns and self-isolation measures were impacting behaviors.
In April 2020, Vodafone began migrating its operations, including its mobile data, to Google Cloud on servers in Europe and the UK with elaborate security safeguards, including encryption, building on a previous partnership.
With the data residing in EU and UK data centers and not the United States, Vodafone could then retrieve anonymous insights from Google Cloud Storage instantaneously. Before supplying any information to the European Commission, however, Vodafone used Dataflow to validate the data and run a series of tests to ensure the database had accurate data, before ingesting and archiving the relevant metrics. For instant access, the data was then made available to the European Commission using a Redis database on Google Kubernetes Engine.
To ensure aggregate Vodafone customer data was always safe, secure, and anonymous, all entry points to the front-end were protected behind Google Cloud Armor, where only specific IP addresses were allowed. Using these tools, seamless data pipelines fed in predefined key performance indicators from each specified European market. While data quality measures ensured the definitions for metrics across markets were consistent and could be accurately compared.
The architecture (pictured below) shows how Vodafone integrated and anonymized its data on Google Cloud.
With its data integrated on Google Cloud, Vodafone created a live, interactive dashboard to track mobility patterns and share relevant information with the European Commission in real-time.
The European Commission Joint Research Center (JRC) was able to gather valuable information from these insights, which enabled them to see where population mobility was aiding the spread of the disease, when cross-referenced with health data It could also assess the implications of lockdowns on different populations and forecast cross-country spreading.
The Vodafone data became instrumental in modeling the likely course of the disease too. For example, the University of Southampton in the UK used it to predict the outcome of different coordinated COVID-19 exit strategies across Europe. This research was published in Science Magazine in September 2020.
The Vodafone data dashboard continues to be used by individual governments, NGOs and organizations to further investigate the impacts of the pandemic and to measure the effectiveness of response strategies alongside the rollout of vaccination programs. The project also helped Vodafone win a DataIQ award for most effective stakeholder engagement.
Using the learnings from this project, Vodafone has been able to adapt its own B2B solution, called Vodafone Analytics, by adaptIng and migrating the code to work in Google Cloud Platform. This solution has been rolled out across Germany, Greece, Portugal and South Africa, and new countries are being onboarded every day. Vodafone Analytics already has more than 100 customers leveraging it for a variety of use cases—Italian fashion retailer OVS, uses it for its smart retail operation, while global real estate company, JLL, uses it to understand the footfall passing through its properties.
Working together, Vodafone and Google Cloud continue to help a range of organizations, governments, and NGOs navigate through the ongoing pandemic, optimize their operations, and help the greater good, without infringing individuals’ fundamental rights to privacy.
To learn more about Google Cloud and Vodafone, watch our full interview here.
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