Update to the Amazon Acceptable Use Policy

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Introducing the newest Heroes of the year – March 2024

AWS Heroes are inspirational thought leaders who go above and beyond to knowledge share in a variety of ways. You can find them speaking at local meetups, AWS Community Days, or even at re:Invent. And these technical experts are never done learning—they’re passionate about solving problems and creating content to enable the community to build faster on AWS. We’re excited to announce the first cohort of Heroes in 2024…

Let’s give a round of applause to our new Heroes!

Awedis Keofteian – Beirut, Lebanon

Community Hero Awedis Keofteian is a DevOps Engineer at Anghami. He has a strong background in DevOps practices, and he leverages modern technologies to enhance scalability, reliability, and efficiency in Anghami’s cloud-based architecture. His journey began as an AWS Community Builder, and over time, he took the helm as the leader of the AWS User Group in Beirut. Awedis is passionate about nurturing and supporting the growth of AWS communities, and shares his knowledge across DevOps, automation, serverless, and cloud technologies.

Daniel Aniszkiewicz – Wrocław, Poland

Security Hero Daniel Aniszkiewicz is a Senior Software Engineer at Algoteque International Hub. He co-organizes the Wrocław AWS User Group, and is passionate about contributing to the growth and engagement of the local AWS community. Daniel is also a seasoned speaker and loves to share his knowledge with others, such as presenting at re:Invent, AWS meetups, and AWS Community Days. He is particularly focused on promoting Amazon Verified Permissions and Cedar through workshops, blog posts, IaC templates, and open source projects.

Hazel Sáenz – Guatemala

Serverless Hero Hazel Sáenz is a Software Architect at Cognits. Her primary focus is modernizing on-premises applications to cloud environments using AWS, and predominantly designs high workload architectures in serverless frameworks. Hazel enjoys sharing her knowledge with the community through technical talks at local and international events, participating in AWS Summits, AWS Community Days, and meetups, as well as writing technical articles in both English and Spanish. Additionally, she is the leader of the AWS User Group Guatemala, where she excels at organizing inclusive events and sharing her knowledge with the community.

Kenta Goto – Tokyo, Japan

DevTools Hero Kenta Goto is a Backend Tech Lead and an enthusiastic contributor to AWS CDK. He has been selected as a top contributor and a trusted reviewer in AWS CDK, and serves as a maintainer for the community-driven CDK Construct Library. Kenta is also a conference speaker, having presented at the AWS Dev Day in Japan in 2022 and 2023. Furthermore, he actively contributes to the open source community by developing and publishing his self-made AWS tools and AWS CDK Construct libraries, which are used worldwide.

Martin Damovsky – Prague, Czech Republic

Community Hero Martin Damovsky is a Cloud Governance Lead at Ataccama.com, an AWS Partner providing Unified Data Management Solutions. He has been particularly interested in AWS Control Tower Account Factory for Terraform, Cloud Intelligence Dashboard, and security and govern tools, such as AWS Security Hub, Amazon GuardDuty, and AWS Config. Martin is a leader for AWS User Group Prague, and he enjoys sharing his knowledge with the greater AWS community through his blog and speaking at meetups, podcasts, and conferences.

Rafał Mituła – Warsaw, Poland

Community Hero Rafał Mituła is a Cloud Data Engineer and Architect within the Data & AI division at Chaos Gears. He is actively involved in the AWS community, co-organizing the AWS User Group Warsaw meetups and the AWS Community Day Poland conference. In addition to his technical and organizational roles, Rafał shares his expertise by speaking at conferences and leading workshops aimed at introducing new builders to AWS and data analytics, such as the AWS Data Engineering Immersion Days.

Sena Yakut – Izmir, Turkey

Security Hero Sena Yakut is a Senior Cloud Security Engineer at Lyrebird Studio. She has a master’s degree in cloud security, and builds security requirements for architectural designs, providing threat management and security concepts and services using AWS. Sena shares her knowledge through blog posts across various platforms, and engaging in discussions about cloud security at events, such as AWS Community Day Türkiye, and DevOpsDays Istanbul. As an active blogger and speaker, she enjoys learning new security features on AWS and informing others about them.

Tiago Rodrigues – Lisbon, Portugal

Community Hero Tiago Rodrigues is a Senior Cloud Consultant at tecRacer.com, an AWS Premier Partner and AWS Advanced Training Partner. He specializes in migrations from on-premises environments to the cloud, as well as modernizing architectures and implementing serverless solutions. Beyond his role, Tiago is deeply committed to knowledge sharing and actively contributes to the AWS community through engagements, such as the AWS User Group Lisbon, educational workshops, and guest lectures at universities. He is passionate about education and innovation, and developed an open source mobile app, AWSary, which is an AWS dictionary designed to provide solution architect diagram drawings and quick insights into AWS services.

Learn More

Please visit the AWS Heroes website if you’d like to learn more about the AWS Heroes program or to connect with a Hero near you.

— Taylor

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Free data transfer out to internet when moving out of AWS

You told us one of the primary reasons to adopt Amazon Web Services (AWS) is the broad choice of services we offer, enabling you to innovate, build, deploy, and monitor your workloads. AWS has continuously expanded its services to support virtually any cloud workload. It now offers over 200 fully featured services for compute, storage, databases, networking, analytics, machine learning (ML) and artificial intelligence (AI), and many more. For example, Amazon Elastic Compute Cloud (Amazon EC2) offers over 750 generally available instances—more than any other major cloud provider—and you can choose from numerous relational, analytics, key-value, document, or graph databases.

We believe this choice must include the one to migrate your data to another cloud provider or on-premises. That’s why, starting today, we’re waiving data transfer out to the internet (DTO) charges when you want to move outside of AWS.

Over 90 percent of our customers already incur no data transfer expenses out of AWS because we provide 100 gigabytes per month free from AWS Regions to the internet. This includes traffic from Amazon EC2, Amazon Simple Storage Service (Amazon S3), Application Load Balancer, among others. In addition, we offer one terabyte of free data transfer out of Amazon CloudFront every month.

If you need more than 100 gigabytes of data transfer out per month while transitioning, you can contact AWS Support to ask for free DTO rates for the additional data. It’s necessary to go through support because you make hundreds of millions of data transfers each day, and we generally do not know if the data transferred out to the internet is a normal part of your business or a one-time transfer as part of a switch to another cloud provider or on premises.

We will review requests at the AWS account level. Once approved, we will provide credits for the data being migrated. We don’t require you to close your account or change your relationship with AWS in any way. You’re welcome to come back at any time. We will, of course, apply additional scrutiny if the same AWS account applies multiple times for free DTO.

We believe in customer choice, including the choice to move your data out of AWS. The waiver on data transfer out to the internet charges also follows the direction set by the European Data Act and is available to all AWS customers around the world and from any AWS Region.

Freedom of choice is not limited to data transfer rates. AWS also supports Fair Software Licensing Principles, which make it easy to use software with other IT providers of your choice. You can read this blog post for more details.

You can check the FAQ for more information, or you can contact AWS Customer Support to request credits for DTO while switching.

But I sincerely hope you will not.

-- seb

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Verified Amazon Connector for Google Sheets

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Verified Amazon Connector for Google Sheets

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Verified Amazon Connector for Google Sheets

Connect Amazon and Google Sheets to keep data synced with scale, efficiency, and simplicity.

Do you want to use Google Sheets for Amazon Sponsored Brands, Products, Display, or TV analysis? Would you like to use Google Sheets to automate Amazon DSP, Amazon Marketing Stream, Vendor Central, and Seller Central data analysis?

Rather than manually downloading Excel or CSV reports and importing them into Google Sheets, you can automate Amazon data flow to Google BigQuery via verified Amazon APIs.

Google BigQuery is an official Google Sheets Add-on that allows you to connect and import data directly from your Amazon Seller Central, Vendor Central, and Amazon Ads accounts.

Why Automate Using An Amazon Connector for Google Sheets?

Direct access to Amazon API reporting data means no messy, time-consuming manual report downloads. Just simple, straightforward data access in Google Sheets powered by BigQuery.

BigQuery allows you to scale beyond Google Sheets limits. Here are just a few ways it helps you scale:

1. Handling Larger Datasets: BigQuery can process and analyze datasets much larger than the maximum allowed in Google Sheets (which limits the number of cells), enabling users to work with vast amounts of data without performance hits.
2. Increased Query Performance: For datasets that exceed Google Sheets’ capacity, BigQuery’s infrastructure allows for faster query execution times, even on very large datasets. Supports more dynamic and complex data exploration.
3. Designed To Scale With Your Business. BigQuery’s architecture is designed to handle petabytes of data, offering users virtually no data limits. This ensures that as a user’s data needs to grow, BigQuery can easily handle the workloads of a growing agency, seller, or vendor across 100s of clients, accounts, or advertisers.

Supercharged Amazon Connector for Google Sheets Powered By BigQuery

With the flow of data automated from Amazon to BigQuery, you access, analyze, visualize, and share billions of rows of data in Google Sheets using official Google-approved connectors.

The Sheets + BigQuery connector offers fast, automated, and simple data automation so you can focus on using data in Google Sheets to get the insight jobs done.

• Collaborate with partners, analysts, or other stakeholders in a familiar Google Sheets interface.
• Eliminate many data limits for Google Sheets by using BigQuery to store all your data, including years of historical data no longer available via the Amazon APIs.
• Cost-effectively scale the volume of data you can use in Sheets with the power of Google BigQuery.
• Ditch the messy, manual report downloads. Ensure a single source of truth for data analysis without constant downloads and file management.
• Streamline your reporting and dashboard workflows with fast, efficient data analysis on Google Sheets.
• The flexibility to use other tools like Looker, Power BI, or Tableau using the same data stored in BigQuery.

Connected Google Sheets runs queries on BigQuery for you. The results of those queries are saved in your spreadsheet for analysis and sharing. You get the power of a scalable cloud data warehouse like BigQuery with the familiarity of a tool like Google Sheets.

Google BigQuery to Google Sheets Examples

You can create a Google Sheets Amazon price tracker or have Google Sheets live update Amazon price updates to BigQuery. Not only can you have Amazon Seller Central to Google Sheets, but you can also have Amazon Vendor Central and Amazon Ads.

You can use Google Sheets to analyze hundreds of other automated data sources to do more than an Amazon Seller Google Sheets report.

• Create an Amazon FBA spreadsheet that streamlines the tracking of fulfillment metrics, enhancing logistics and inventory management efficiency.
• Utilizing data automation for an Amazon seller spreadsheet significantly improves sales and expense tracking accuracy and speed, aiding in better financial decision-making.
• Implementing data automation in an Amazon FBA inventory spreadsheet optimizes inventory levels, reducing the risk of stockouts or overstocking, thereby maximizing profitability.
• Rather than manually track Amazon sales data in Excel, Google Sheets + BigQuery Sellers and Vendors can quickly analyze sales trends and performance, enabling more informed strategic planning and marketing decisions.
• Data automation for an Amazon inventory spreadsheet ensures real-time inventory tracking, facilitating more effective stock management and replenishment strategies.
• Employing data automation in an Amazon profit Excel spreadsheet provides a clear, instantaneous view of profit margins, helping to identify areas for cost reduction and revenue enhancement.
• While you can manually create an Amazon product listing template in Excel, Google Sheets automation streamlines the listing process, improving accuracy and saving time, which can be redirected toward other growth activities.

To see how easy it is to connect sheets to BigQuery, check out this video guide:

Amazon API + Google Sheets

This is not Amazon price scraping to Google Sheets; this is using official Amazon APIs to load data into Google BigQuery!

Amazon offers Seller, Vendor, and Ads data feeds for a broad cross-section of data to be used in Google Sheets via BigQuery.

Openbridge fully automates the data flow from Amazon to a private, trusted BigQuery destination you own. Here are just some of the Amazon data you can access;

• Amazon Vendor Central Brand Analytics
• Amazon Vendor Central Retail Analytics
• Sponsored Product
• Sponsored Brand
• Sponsored Display
• Sponsored Video
• Amazon Attribution
• Amazon DSP
• Amazon Marketing Stream
• Brand Analytics
• FBA Fees
• Order Reports
• Fulfillment
• Inventory
• Sales & Traffic
• Settlement
• Returns

Get Started Automating Amazon Data To Google Sheets— For Free.

Amazon Seller, Vendor, and Amazon Ads to Google Sheets powered by BigQuery can be set up in minutes with Amazon automation.

Openbridge delivers code-free data automation to BigQuery across Amazon Advertising Sponsored Ads, Amazon DSP, Amazon Marketing Stream, and Amazon Attribution. We also fully support Amazon Seller and Vendor Central data automation.

If you are a Brand, Digital Agency, or something in between, we offer a free 30-day trial so that you can connect Google Sheets and Amazon data together via BigQuery.

>> Get a 30-day free trial to try the Amazon Connector for Google Sheets for yourself.

References

• Use BigQuery data in Google Sheets
• Automated Amazon Vendor Central Sales Reports
• Own Your Amazon Price Tracker Data
• Private Amazon Catalog Keyword Tracker

---

Verified Amazon Connector for Google Sheets was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Mistral AI models now available on Amazon Bedrock

Last week, we announced that Mistral AI models are coming to Amazon Bedrock. In that post, we elaborated on a few reasons why Mistral AI models may be a good fit for you. Mistral AI offers a balance of cost and performance, fast inference speed, transparency and trust, and is accessible to a wide range of users.

Today, we’re excited to announce the availability of two high-performing Mistral AI models, Mistral 7B and Mixtral 8x7B, on Amazon Bedrock. Mistral AI is the 7th foundation model provider offering cutting-edge models in Amazon Bedrock, joining other leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon. This integration provides you the flexibility to choose optimal high-performing foundation models in Amazon Bedrock.

Mistral 7B is the first foundation model from Mistral AI, supporting English text generation tasks with natural coding capabilities. It is optimized for low latency with a low memory requirement and high throughput for its size. Mixtral 8x7B is a popular, high-quality, sparse Mixture-of-Experts (MoE) model, that is ideal for text summarization, question and answering, text classification, text completion, and code generation.

Here’s a quick look at Mistral AI models on Amazon Bedrock:

Getting Started with Mistral AI Models
To get started with Mistral AI models in Amazon Bedrock, first you need to get access to the models. On the Amazon Bedrock console, select Model access, and then select Manage model access. Next, select Mistral AI models, and then select Request model access.

Once you have the access to selected Mistral AI models, you can test the models with your prompts using Chat or Text in the Playgrounds section.

Programmatically Interact with Mistral AI Models
You can also use AWS Command Line Interface (CLI) and AWS Software Development Kit (SDK) to make various calls using Amazon Bedrock APIs. Following, is a sample code in Python that interacts with Amazon Bedrock Runtime APIs with AWS SDK:

import boto3
import json

bedrock = boto3.client(service_name="bedrock-runtime")

prompt = "<s>[INST] INSERT YOUR PROMPT HERE [/INST]"

body = json.dumps({
    "prompt": prompt,
    "max_tokens": 512,
    "top_p": 0.8,
    "temperature": 0.5,
})

modelId = "mistral.mistral-7b-instruct-v0:2"

accept = "application/json"
contentType = "application/json"

response = bedrock.invoke_model(
    body=body,
    modelId=modelId,
    accept=accept,
    contentType=contentType
)

print(json.loads(response.get('body').read()))

Mistral AI models in action
By integrating your application with AWS SDK to invoke Mistral AI models using Amazon Bedrock, you can unlock possibilities to implement various use cases. Here are a few of my personal favorite use cases using Mistral AI models with sample prompts. You can see more examples on Prompting Capabilities from the Mistral AI documentation page.

Text summarization — Mistral AI models extract the essence from lengthy articles so you quickly grasp key ideas and core messaging.

You are a summarization system. In clear and concise language, provide three short summaries in bullet points of the following essay.

# Essay:
{insert essay text here}

Personalization — The core AI capabilities of understanding language, reasoning, and learning, allow Mistral AI models to personalize answers with more human-quality text. The accuracy, explanation capabilities, and versatility of Mistral AI models make them useful at personalization tasks, because they can deliver content that aligns closely with individual users.

You are a mortgage lender customer service bot, and your task is to create personalized email responses to address customer questions. Answer the customer's inquiry using the provided facts below. Ensure that your response is clear, concise, and directly addresses the customer's question. Address the customer in a friendly and professional manner. Sign the email with "Lender Customer Support."

# Facts
<INSERT FACTS AND INFORMATION HERE>

# Email
{insert customer email here}

Code completion — Mistral AI models have an exceptional understanding of natural language and code-related tasks, which is essential for projects that need to juggle computer code and regular language. Mistral AI models can help generate code snippets, suggest bug fixes, and optimize existing code, accelerating your development process.

[INST] You are a code assistant. Your task is to generate a 5 valid JSON object based on the following properties:
name: 
lastname: 
address: 
Just generate the JSON object without explanations:
[/INST]

Things You Have to Know
Here are few additional information for you:

• Availability — Mistral AI’s Mixtral 8x7B and Mistral 7B models in Amazon Bedrock are available in the US West (Oregon) Region.
• Deep dive into Mistral 7B and Mixtral 8x7B — If you want to learn more about Mistral AI models on Amazon Bedrock, you might also enjoy this article titled “Mistral AI – Winds of Change” prepared by my colleague, Mike.

Now Available
Mistral AI models are available today in Amazon Bedrock, and we can’t wait to see what you’re going to build. Get yourself started by visiting Mistral AI on Amazon Bedrock.

Happy building,
— Donnie

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Deprecation of LISTINGS_ITEM_ISSUES_CHANGE notification payload version 1.0

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Optimizing Amazon Brand Store Performance

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Optimizing Amazon Brand Store Performance

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Optimizing Amazon Brand Store Performance

Using insights to fuel Amazon Brand Store best practices, growth, and scale

Amazon now offers extensive performance insights for brand stores that provide metrics on your customer experience and products and recommendations on improving visibility.

These brand store insights are now available via Amazon APIs, which allows fast, automated access to data that allows your team new pathways to analyze, visualize, and optimize your customer experience investments.

Why Amazon Brand Stores? Enhancing Brand Visibility On Amazon

Amazon Stores allow you to showcase your brand and products in a multipage, immersive shopping experience. Amazon Brand Store enhances brand visibility and differentiation in a crowded marketplace, allowing sellers and vendors to showcase their products, tell their brand story, and engage customers with rich media content such as images and videos. This helps build brand loyalty and awareness and drives sales by providing a centralized location for customers to browse and purchase products directly from the brand.

Building Brand Affinity: Amazon Brand Stores Deliver Results

No matter the size of your brand, Amazon Stores gives you an immersive place to introduce audiences to your story, mission, and products.

Per Amazon, Brand stores have a direct impact on performance:

+83% higher dwell time. Stores with 3+ pages have 83% higher shopper dwell time and 32% higher attributed sales per visitor.

+35% higher attributed sales per visitor. On average, Stores updated within the past 90 days have 21% more repeat visitors and 35% higher attributed sales per visitor.

Brand Stores offer valuable insights into customer behavior and preferences through analytics, enabling brands to optimize their marketing strategies and product offerings.

Amazon Brand Store Examples: Analytics Insights

So, what type of data is available via API? There are three primary groups of store performance metrics;

• Brand Performance
• Product-level ASIN Performance
• Quality and Recommendations

Brand Performance Metrics

All the available insight metrics for evaluating Brand Store performance on Amazon:

• VIEWS: Number of page views.
• ORDERS: Estimated total orders placed by Store visitors within 14 days of their visit. Orders contain one or more units sold.
• UNITS: Estimated units purchased by Store visitors within 14 days of their last visit.
• SALES: Estimated total sales generated by Store visitors within 14 days of their last visit.
• VISITS: Total visits to a page within a single day. Each visitor can visit more than one page and your Store from multiple traffic sources.
• VISITORS: Total visitors to your Store within the selected date range, calculated based on daily unique users or devices.
• SCORE_LEVEL: Store Quality rating calculated on various factors defining the quality of a store. It can be HIGH, MEDIUM, or LOW.
• RECOMMENDATIONS: An array of objects containing two fields: recommended action (e.g., “Add a video”) and observed average well time increase (the improvement it would bring in the overall store quality).
• CONTRIBUTORS: An array of recommendations applied by the Store Owner improves overall store quality.
• DWELL: Average time a customer spends in the store, specifically for store quality measurement.
• PEER_DWELL: Average time a customer spends on other similar (peer) stores.
• DWELL_TIME: Average time a customer spends in the store, providing insights into user engagement by calculating the average duration of visits.
• BOUNCE_RATE: Ratio of total bounce visits (customers who landed at the store and left quickly without engaging) to total landing visits, providing insights into visitor engagement.
• NEW_TO_STORE: Total count of unique visitors new to the store, providing valuable insights into the number of first-time shoppers.

Product-level ASIN Performance Metrics

Beyond store-wide metrics, Amazon also offers ASIN-specific data, allowing brands to drill down into the performance of individual products within their store. These metrics include views, orders, units, add-to-carts, and others, providing a granular view of how products perform and interact with potential customers.

• VIEWS: Number of times a customer viewed an ASIN. It can happen once per page visit.
• ORDERS: Estimated total orders placed by Store visitors on the day of the ASIN view. Orders can have one or more total units.
• UNITS: Estimated units purchased by Store visitors during attributed orders for the ASIN.
• ADDTOCARTS: Total number of times an ASIN was added to a cart by a customer on a store page.
• IN_STOCK_VIEWS: Total views of an ASIN on a store page while the ASIN was in stock. For ASINs with variations, the customer must have selected a variation in stock to be counted.
• AVERAGE_IN_STOCK_PRICE: Average price in local currency the ASIN was viewed at by customers while it was in stock.
• IN_STOCK_RATE: Rate at which customers viewed an ASIN while it was in stock.
• AVERAGE_SALE_PRICE: Average price in local currency for which the ASIN is sold during the order.
• CONVERSION_RATE: Rate at which customers ordered a unit of the item over how many times customers clicked the item.
• CLICKS: Count how often a customer clicks an ASIN-related widget on the store page.
• CLICK_RATE: Rate at which the ASIN was clicked per view. This ratio can be above one if the widget interacts with a widget with engaging features.
• RENDERS: Number of times the ASIN is rendered on a store page. Note — this does not guarantee that the customer saw the ASIN.
• TOTAL_VIEWS: Total number of times customers viewed ASINs on the store’s pages. A view can happen once per store page visit.
• TOTAL_CLICKS: Total count of times a customer clicked an ASIN-related widget on the store’s pages.

Quality and Recommendations Metrics

Amazon’s quality and recommendations metrics are particularly noteworthy. They focus on your Brand Store’s average dwell time, compare your performance to peer groups, and rate your store’s quality.

High ratings indicate effective engagement strategies, and Amazon uses these ratings to suggest specific actions further to improve your store’s performance and customer dwell time.

For example, Amazon will provide a score and ranking for your store

• SCORE_LEVEL: High
• DWELL: 77.93

The SCORE_LEVEL is a qualitative metric that assesses your store’s quality based on various factors, categorized as HIGH, MEDIUM, or LOW. It directly reflects the overall appeal and effectiveness of your store’s design and content. DWELL measures customers' average time in your store, offering insights into engagement and interest.

Amazon will also provide a collection of other recommendations to improve the score;

• Add best selling products or recommended products tile to a subpage can improve the score by 0.4473
• Add a background video to reinforce your brand message or showcase a product can improve the score by 0.4011

Get Started Automating Amazon Brand Store Performance Data — For Free.

The Amazon Brand Store Insights data offers a goldmine of opportunities for data analysis, which can be leveraged by Amazon Sellers and Vendors to refine their strategies, enhance product visibility, and ultimately drive sales.

Sign up for a 30-day free trial and request access to our Amazon Brand Store Performance Data beta.

---

Optimizing Amazon Brand Store Performance was originally published in Openbridge on Medium, where people are continuing the conversation by highlighting and responding to this story.

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AWS Weekly Roundup — .Net Runtime for AWS Lambda, PartyRock Hackathon, and more — February 26, 2024

The Community AWS re:invent 2023 re:caps continue! Recently, I was invited to participate in one of these events hosted by the AWS User Group Kenya, and was able to learn and spend time with this amazing community.

AWS User Group Kenya

Last week’s launches
Here are some launches that got my attention during the previous week.

.NET 8 runtime for AWS Lambda – AWS Lambda now supports .NET 8 as both a managed runtime and container base image. This support provides you with .NET 8 features that include API enhancements, improved Native Ahead of Time (Native AOT) support, and improved performance. .NET 8 supports C# 12, F# 8, and PowerShell 7.4. You can develop Lambda functions in .NET 8 using the AWS Toolkit for Visual Studio, the AWS Extensions for .NET CLI, AWS Serverless Application Model (AWS SAM), AWS CDK, and other infrastructure as code tools.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional projects, programs, and news items that you might find interesting:

Earlier this month, I used this image to call attention to the PartyRock Hackathon that’s currently in progress. The deadline to join the hackathon is fast approaching so be sure to signup before time runs out.

Amazon API Gateway – Amazon API Gateway processed over 100 trillion API requests in 2023, and we continue to see growing demand for API-driven applications. API Gateway is a fully-managed service that enables you to create, publish, maintain, monitor, and secure APIs at any scale. Customers that onboarded large workloads on API Gateway in 2023 told us they chose the service for its availability, security, and serverless architecture. Those in regulated industries value API Gateway’s private endpoints, which are isolated from the public internet and only accessible from your Amazon Virtual Private Cloud (VPC).

AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

Upcoming AWS events
Season 3 of the Build on Generative AI Twitch show has kicked off. Join every Monday on Twitch at 9AM PST/Noon EST/18h CET to learn among others, how you can build generative AI-enabled applications.

If you’re in the EMEA timezone, there is still time to register and watch the AWS Innovate Online Generative AI & Data Edition taking place on February 29. Innovate Online events are free, online, and designed to inspire and educate you about building on AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn here about future AWS Innovate Online events happening in your timezone.

AWS Community re:Invent re:Caps – Join a Community re:Cap event organized by volunteers from AWS User Groups and AWS Cloud Clubs around the world to learn about the latest announcements from AWS re:Invent.

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS.

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New AWS Region in Mexico is in the works

Today, I am happy to announce that we are working on an AWS Region in Mexico. This AWS Mexico (Central) Region will be the second Region in Latin America joining the AWS South America (São Paulo) Region and will give AWS customers the ability to run workloads and store data that must remain in-country.

Mexico in the works

The Region will include three Availability Zones, each one physically independent of the others in the Region yet far enough apart to minimize the risk that an event in one Availability Zone will have impact on business continuity. The Availability Zones will be connected to each other by high-bandwidth, low-latency network connections over dedicated, fully redundant fiber.

With this announcement, AWS now has five new Regions in the works (Germany, Malaysia, Mexico, New Zealand, and Thailand) and 15 upcoming new Availability Zones.

AWS investment in Mexico

The upcoming AWS Mexico Region is the latest in ongoing investments by AWS in Mexico to provide customers with advanced and secure cloud technologies. Since 2020, AWS has launched seven Amazon CloudFront edge locations in Mexico. Amazon CloudFront is a highly secure and programmable content delivery network (CDN) that accelerates the delivery of data, videos, applications, and APIs to users worldwide with low latency and high transfer speeds.

In 2020, AWS launched AWS Outposts in Mexico. AWS Outposts is a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. AWS expanded its infrastructure footprint in Mexico again in 2023 with the launch of AWS Local Zones in Queretaro. AWS Local Zones are a type of AWS infrastructure deployment that places compute, storage, database, and other select services closer to large population, industry, and IT centers, enabling customers to deliver applications that require single-digit millisecond latency to end users. In 2023, AWS established an AWS Direct Connect location in Queretaro, allowing customers to establish private connectivity between AWS and their data center, office, or colocation environment.

Here is a glimpse into our customers in Mexico and the exciting, innovative work they’re undertaking:

Banco Santander Mexico is one of the leading financial groups in the country, focused on commercial banking and securities financing, serving more than 20.5 million customers. “AWS has been a strategic partner for our digital transformation,” said Juan Pablo Chiappari, head of IT Infrastructure for North America. “Thanks to their wide range of services, we have been able to innovate faster, improve our customer experience and reduce our operating costs.”

SkyAlert is an innovative technology company that quickly alerts millions of people living in earthquake-prone areas, promoting a culture of prevention against natural disasters. In order to provide customers—both businesses and individuals—with the right tools to protect themselves during earthquakes, SkyAlert migrated its infrastructure to AWS. After implementing its Internet of Things (IoT) solution to run on AWS and its efficient alert service, SkyAlert scales quickly and can send millions of messages in a few seconds, helping to save lives in the event of earthquakes.

Kueski is an online lender for the middle class of Mexico and Latin America. The company uses big data and advanced analytics to approve and deliver loans in a matter of minutes. The company has become the fastest-growing platform of its kind in the region and has already granted thousands of loans. They were born with AWS.

Bolsa Institucional de Valores (BIVA) is a stock exchange based in Mexico, backed by Nasdaq. BIVA provides local and global investors with cutting-edge technology for trading and market solutions and companies with listing and maintenance services. As part of its vision of innovation, BIVA started its journey to the cloud in 2023 by migrating its disaster recovery site, including its trading and market surveillance systems, to AWS, using edge compute capabilities available in both the AWS Local Zones in Queretaro, Mexico, to achieve their low latency needs.

Stay Tuned
The AWS Region in Mexico will open in early 2025. As usual, subscribe to this blog so that you will be among the first to know when the new Region is open!

To learn more about AWS Global Cloud Infrastructure, see the Global Infrastructure page.

— Irshad

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