Climate scientists face a deluge of environmental data to analyze and interpret from real-time sensors and satellites across the globe. The stakes are as high as our planet’s long-term future, but rapidly changing conditions are already impacting communities through extreme events like floods and wildfires as well as management of everyday resources. In this context, detailed environmental maps are key sources for urgent global issues like food security, water quality, and vegetation levels.

Scientists, researchers, and developers rely on state-of-the-art cloud computing tools like Google Earth Engine (EE) to detect changes, map trends, and quantify differences on the Earth’s surface. EE leverages the compute power of Google Cloud to combine a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.

To find out more about how climate scientists address these complex research challenges, I spoke to Dr. Richard Fernandes, Research Scientist at Natural Resources Canada (NRCan). Dr. Fernandes is also a 2022-23 member of the Google Research Innovators Program, which provides technical and professional support to a global cohort of leading researchers. I asked him to describe how cloud computing and machine learning (ML) tools can support climate resilience research and drive awareness about climate sustainability.

Dr. Fernandes, can you give me an overview of your research in climate sciences?

My research focuses on mapping the status and trends of vegetation over Canada. Every month we generate maps of vegetation parameters, such as canopy cover at 20m resolution, to support environmental monitoring and assessment. These maps contribute to global datasets that are used to reduce uncertainty in weather and climate forecasts.

Canada has approximately 10 million square kilometers of land and the annual data volume of these maps is equivalent to streaming HD movies for over 750 hours non-stop. And that’s only the tip of the iceberg: the volume of input data we need to generate those maps is typically 100 times more. Unlike movie streaming services, we have to independently process each input pixel to locate it accurately, screen for clouds (and even the shadows of clouds), and then transform it into a vegetation parameter value like canopy cover using ML algorithms.

The volume of high-resolution data and the amount of compute we need are challenging and ever-increasing. Rather than dedicating servers 24/7 for constant monitoring, we rely on cloud computing and ML. Cloud computing allows us to manage all this data in a useful and accessible way. We have also been able to successfully use the Google Artificial Intelligence (AI) Platform to calibrate new ML models for third-party datasets.

How did you start working with Google Cloud?

I started using EE together with open source APIs for integration with Google Drive about four years ago. With the pandemic my research group has transitioned to using EE, Google Cloud, and Google Drive for both our Canada-wide mapping as well as for R&D activities. In January we developed and released the LEAF toolbox, which builds on EE satellite data to create customizable maps of foliage density in near real-time. We do all of the pixel processing in EE and leverage the ability to integrate our own functions using their APIs. We combine EE with Google Cloud to handle and manage output datasets. Fortunately, EE has most of the input data already at hand so we don’t need to deal with that 100x larger volume.

What impact do you expect LEAF will have now–and down the road?

Both the Canadian federal government and provinces already use our data products as inputs to permafrost, crop status, and water resource models. Agriculture Canada had been using a conventional Geographic Information System (GIS) and approached us to ask how we manage our data. They want to use LEAF to assess how crops are progressing, which impacts local economies and the global food supply. We’re in a pilot with them to apply EE to their crop condition assessments.

Also exciting is that the Province of Alberta is integrating the LEAF toolbox within a system for monitoring the reclamation status of oil and gas wells and mines. They want to know how to rehabilitate sites that were developed for mineral and gas deposits. This is another great use case but many others are possible. The technology itself is cool, but that’s not even the point. ML algorithms are constantly evolving and improving. New ML algorithms use active learning that detects mistakes and makes updates on the fly. The technology will be different in five years — or five months! — so our priority is making these tools accessible and useful now.

Why is state-of-the-art technology for climate research so important?

Scientific research must be validated, transparent, and rigorous to drive the best solutions to our complex and changing ecosystem. Climate action needs greater public awareness, which requires more knowledge, which demands the best data. By democratizing information and decision-making, we can create an ecosystem of openness and public-private partnerships. We can lower the barrier to entry for advanced research and help scientists validate and reproduce their results. Also, scientists don’t want to have to become software engineers for these custom highly specific solutions. They want user-friendly tools that let them focus on their analyses and share their insights with the world. That’s one of the major appeals of the Google Research Innovators Program for me: I want to share LEAF with colleagues and collaborate with other researchers who are using new tools in new ways.

Do you have any parting words about your mission?

We’re so fortunate to have Canadian taxpayers and the Government of Canada funding our work. We have worked with many scientists over the past two decades to develop and validate the algorithms we use, especially Fred Baret and Marie Weiss at INRA France, who have championed the idea of free and open access to algorithms and knowledge, and data.

I really believe in democratic access to information. I like the fact that the terms of service of Google products allows us to offer not only maps but the actual processing system in a free and open manner to everyone. I also like that EE provides a simple-to-use user interface that works on mobile devices. We designed LEAF not just for experts, but for individuals. My mom was able to make maps of a nearby park in real time on her tablet. It is my hope that expanding access to critical environmental information will increase our collective awareness of how our actions impact both near and far places — and make us active in the cause of a sustainable future.

That’s an inspiring note to end on. Thank you for your time!

Thanks for having me!

Click here to learn more about Google’s commitment to renewable energy. Or try the LEAF toolbox for yourself!

Today we’re hosting our Retail & Consumer Goods Summit, a digital event dedicated to helping leading retailers and brands digitally transform their business. For me, this is a personally exciting moment, as I see tremendous opportunities for those companies that choose to focus on their customers and leverage technology to elevate experiences.

Our event includes breakout sessions to help retailers and brands become customer centric, embrace the digital moment and transform their operations. Some of my favorite sessions include: 

• Why Search Abandonment is the metric that matters, highlighting Retail Search, and featuring a conversation with Macy’s
• Driving Consumer Closeness in a Privacy-Centric World, discussing how retailers and brands can create a successful first party data strategy, featuring a conversation with P&G
• The Modern Store: 7 Innovation Hotspots, sharing how retailers and brands can approach store transformation to unlock the most value from technology, featuring a conversation with The Home Depot.   

I’ll be speaking in our Retail Spotlight session, discussing the current retail landscape and our industry approach, followed by conversations with Albert Bertilsson, Head of Engineering – Edge at IKEA Retail (Indga Group) and Neelima Sharma, Senior Vice President, Technology Ecommerce, Marketing and Merchandising at Lowe’s. 

Let me share a bit more about the topics we’ll discuss in that session.

In retail specifically, digital-first shopping journeys are blurring the lines between the physical and digital brand experience. Shoppers want to know what’s available before they visit your stores, and they expect fulfillment options like curbside pickup. We see this when tracking trends for interest in curbside pickup or in-stock items.

This has left many retailers asking how they can get smarter with their data, tackle the $300 billion dollar problem of “search abandonment,” move faster to create new customer experiences, and do a better job of connecting their employees and customers – with confidence.

Our team has been spending time thinking about how we can rise and succeed in this new era together. We continue to focus on areas where we can bring the best of our capabilities to our retail customers around the world. And we’re focused on ways we can bring the best of what Google has to offer through cloud integrations.

Our goal is to help retailers become customer-centric and data-driven, capture digital and omni-channel revenue growth, create the modern store and drive operational improvement.

Let’s dig into each of these strategic pillars in a bit more detail. 

Become customer centric and data driven

Customers today expect experiences that are timely, targeted, and tailored for them and their needs, and reject experiences that can’t deliver these features. Data modeling, legacy technology, and siloed systems often prevent retailers from providing that level of personalized experience. 

At Google Cloud, we work with global retailers and our ecosystem partners to activate and bring value from first-party data, particularly in the field of customer data platforms (CDPs). This includes integrations from Google Cloud, such as our business intelligence platform Looker and other popular platforms to power one source of customer data through the organization. We also help retailers modernize their data warehouse with Looker for gathering business intelligence across their organization. This is important not just for consumer data, but inventory, supply chain, and store operations as well. 

Capture Digital and Omnichannel Growth 

We power some of the largest e-commerce sites in the world, helping them scale for Black Friday, Cyber Monday, and other holiday events. While scale is critically important, it’s also important to consider the quality of the online experience. How do your customers find products? How can you help deliver seamless online and omnichannel experiences? 

To help, we’re building product discovery solutions that bring together the best of our technologies that help retailers drive engagement with their consumers. Retail Search, for example, gives retailers the ability to provide Google-quality search on their own digital properties – search that is customizable for their unique business needs and built upon Google’s advanced understanding of user intent & context. 

The imperative is clear. Recent research found that retailers lose more than $300 billion to search abandonment — when purchase intent is not converted into a sale due to bad search results — every year in the US alone. 

Today, we announced that Retail Search is available to a larger set of retailers. If you are interested in learning more about Retail Search you can contact your sales representative for additional details.

Create the modern store  

With the rise of buying trends like curbside pickup and proximity-based search, our Google Maps Platform team is working on new products and features to help raise inventory awareness for your shoppers. We want to help you make it easier for them to understand what’s available to purchase in their channel of choice.

With Product Locator, each product page connects customers with information they need for local pickup and delivery options. This ensures customers are aware of pickup and delivery options throughout the buying journey—not just checkout. 

Awareness of local inventory can boost a wide range of key metrics for your business. Shopify recently shared that shoppers who opt for local pickup over delivery had a +13% higher conversion rate and that 45% of local pickup customers make an additional purchase upon arrival.

This is just one quick example of how our Google Maps Platform team can improve experiences for your shoppers.

Operational improvement

It can be challenging to operate in a world and at a time when consumer behavior and supply chains are so disrupted and volatile, and where entire retail teams had to go remote during the pandemic and beyond. 

We’re working with retailers to leverage artificial intelligence (AI) to improve consumer experience through chat bots or conversational commerce that solves problems for customers from anywhere. You can learn more about these offerings in our Conversational Commerce with Google breakout session, featuring Albertsons.

As the need for digital transformation continues to accelerate, Google Cloud is helping retailers stay ahead of the curve with solutions for digital and omnichannel growth, data-driven and customer-focused experiences, and operational improvement. For every era of cloud technologies, from the past into the future, Google Cloud is committed to providing solutions to retailers.

Read more about our solutions for retail, and check out additional sessions, including the CPG Industry Spotlight Session How To Grow Brands in Times of Rapid Change – Featuring L’Oréal at our Retail & Consumer Goods Summit.

Hundreds of millions of residents of South East Asia have only limited access to banking and finance services. However, help is at hand. One of South East Asia’s largest fintech businesses, Ascend Money is using digital technologies to realize its mission of enabling as many people as possible to access innovative financial services and live better lives.

According to Ascend Money, 60 percent of the region’s 620 million residents do not have access to a full suite of financial services. Even a relatively small proportion of this market represents a big opportunity for the business.

Ascend Money’s offerings include the TrueMoney regional payment platform for underserved and digital consumers. The TrueMoney platform supports more than 40 million consumers across six countries: Cambodia, Indonesia, Myanmar, the Philippines, Thailand, and Vietnam.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business.”

– Abraham Jarrett, Head of Engineering, Ascend Money

TrueMoney also provides an e-wallet app that provides easy ways to top up mobile phones, undertake online shopping, and pay for products and services; a network of 65,000 branded shops; and international remittances, initially between Thailand and Myanmar. In addition, Ascend Money offers financial products to small to medium businesses.

Ascend Money is part of the South East Asian online business Ascend Group, which also operates e-commerce, e-procurement, data centers, cloud services, fulfilment, and digital marketing services.

Cost is a key criteria

Ascend Money initially started operations using physical infrastructure and on-premises workforce productivity applications. However, as the business grew its customer base and expanded into new markets, its technology leaders began to explore options to improve value for money; reduce the maintenance load on in-house team members; improve its data management and analysis; and collaborate more effectively.

“Operating our own on-premises infrastructure entails a higher cost of ownership and maintenance, as well as requiring us to scale up our hardware as needed,” says Abraham Jarrett, Head of Engineering, Ascend Money. “We conducted an evaluation and found that moving to the cloud would deliver a range of benefits.”

G Suite and Google Cloud Platform the best fit

The business evaluated solutions available in the market and opted to move to G Suite and Google Cloud Platform.

“Moving to Google Cloud Platform was an optimization exercise, with lowering our costs our primary goal, and achieving better performance an added benefit,” says Jarrett. Google Cloud Platform monitoring, diagnostic, and analytics tools have enabled Ascend Money to reduce its infrastructure spending, becoming more efficient and cost-effective in the process.

“Managing software is a big cost for us. Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.”

– Abraham Jarrett, Head of Engineering, Ascend Money

The Ascend Money team saw Google Kubernetes Engine as enabling a seamless way of transitioning from an on-premises data center to a cloud service. “We considered industry trends such as what people were adopting and who we could hire when making our decision,” says Jarrett. “Engineering teams are focusing on using Kubernetes open source container management at scale and as a way of doing business, which was attractive to us.

“Google Kubernetes Engine presented the easiest way to manage and orchestrate our containers, and drive us away from our existing infrastructure.”

BigQuery best for cost and ease of use

Ascend Money also reviewed data infrastructure solutions, with its business intelligence and data platform teams considering a range of options. The teams quickly ruled out an on-premises solution due to the capital investment required and opted for a cloud service. Following a rigorous evaluation of Google Cloud Platform against the services offered by another cloud provider, Ascend Money opted to run on Google Cloud.

“We found, based on value, ease of use, effectiveness, and the skills and adaptability of our own team members, BigQuery and other Google Cloud Platform services were the best fit for our business,” says Jarrett. The business is now running an architecture comprising a BigQuery analytics data warehouse; Cloud Storage to store raw and archive data; Cloud Dataflow to process stream and batch data, and Cloud Pub/Sub for event ingestion and delivery.

“We’re expanding our utilization of BigQuery and other services on a daily basis,” says Jarrett.

Ascend Money is also using a range of Google Cloud Platform services for the infrastructure outside its data platform, including Stackdriver logging and monitoring; Cloud KMS to manage cryptographic keys for its cloud services, Cloud Build to undertake continuous integration, delivery, and deployment; Cloud DNS to provide domain name system services; and Cloud Functions to enable its developers to run and scale code in the cloud.

With Google Cloud Platform, Ascend Money is now processing about 12GB of batch data per day and streaming data from about 200 data marts per day in Thailand alone.

Cost effective scalability and faster to market

Google Kubernetes Engine has enabled the business to scale and deliver to market faster. “We can build on the platform, take the application and container and deploy them to scale out,” says Jarrett. “The Google Kubernetes Engine orchestration mechanism that provides containerized, elastic scalability is extremely important in allowing us to manage costs and expend our effort efficiently. Through Kubernetes, we can write once and deploy everywhere.”

With Google Cloud Platform, the business has saved 3,000,000 THB (about US$90,000) in licensing costs and, through automation tools, reduced the time to complete infrastructure activities by 50 percent.

“We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion.”

– Abraham Jarrett, Head of Engineering, Ascend Money

Ascend Money’s entire workforce is now using G Suite to collaborate and operate productively. The business is achieving a range of benefits including being able to get new team members up and running more quickly and reduced cost. “Managing software is a big cost for us,” says Jarrett. “Through G Suite, we are significantly reducing our software deployment, licensing, and repair costs.” Meanwhile, the shorter time to onboard team members to G Suite is paying off with improved productivity and an accelerated ability to collaborate.

Ascend Money team members primarily use Sheets, Slides, and Docs to create internal materials, including product requirement documents in Docs and workforce and project planning spreadsheets in Sheets.

The organization also uses Hangouts Meet to collaborate when working from different locations. “We don’t have as many meetings since we deployed G Suite and we can work effectively in a distributed fashion,” says Jarrett. “People can work from home and we have five regions plus Thailand where they can collaborate live on a document, such as a slide deck for board meetings or meetings with our chief executive officer, head of engineering, or other senior managers.”

20 hours per week saved

Jarrett describes the time savings of using web browser-based productivity software and G Suite as saving them an average of 20 hours per week. This has improved productivity and the quality of life for Ascend Money’s hard-working team, increasing overall staff satisfaction.

“The ability to work remotely is a big win for us,” Jarrett says. “Our team members can keep one computer at work and one at home and access the same information, they can work in a plane while it sits on the tarmac, or update a Google Doc or a Google Sheet in real time on their phone. They can work anytime, from any location, as long as they have an internet connection. They have less dead time, meaning more uptime.”

With Google Cloud Platform and G Suite, Ascend Money is ideally positioned to support growing demand for its fintech services in South East Asia, particularly among people with limited access to banking. “We have the agility and dynamism to support rising demand and look forward to continuing to work with Google to realize our business ambitions,” says Jarrett.

I previously talked about Eventarc for choreographed (event-driven) Cloud Run services and introduced Workflows for orchestrated services.

Eventarc and Workflows are very useful in strictly choreographed or orchestrated architectures. However, you sometimes need a hybrid architecture that combines choreography and orchestration. 

For example, imagine a use case where a message to a Pub/Sub topic triggers an automated infrastructure workflow or where a file upload to a Cloud Storage bucket triggers an image processing workflow. In these use cases, the trigger is an event but the actual work is done as an orchestrated workflow.

How do you implement these hybrid architectures in Google Cloud? The answer lies in Eventarc and Workflows integration. 

Eventarc triggers

To recap, an Eventarc trigger enables you to read events from Google Cloud sources via Audit Logs and custom sources via Pub/Sub and direct them to Cloud Run services:

One limitation of Eventarc is that it currently only supports Cloud Run as targets. This will change in the future with more supported event targets. It’d be nice to have a future Eventarc trigger to route events from different sources to Workflows directly. 

In absence of such a Workflows enabled trigger today, you need to do a little bit of work to connect Eventarc to Workflows. Specifically, you need to use a Cloud Run service as a proxy in the middle to execute the workflow. 

Let’s take a look at a couple of concrete examples.

Eventarc Pub/Sub + Workflows integration

In the first example, imagine you want a Pub/Sub message to trigger a workflow. 

Define and deploy a workflow

First, define a workflow that you want to execute. Here’s a sample workflows.yaml that simply decodes and logs the Pub/Sub message body:

  main:
  params: [args]
  steps:
    - init:
        assign:
          - headers: ${args.headers}
          - body: ${args.body}
...
    - pubSubMessageStep:
        call: sys.log
        args:
            text: ${"Decoded Pub/Sub message data is " + text.decode(base64.decode(args.body.message.data))}
            severity: INFO
Deploy the workflow with a single command:
gcloud workflows deploy ${WORKFLOW_NAME} --source=workflow.yaml --location=${REGION}

Deploy a Cloud Run service to execute the workflow

Next, you need a Cloud Run service to execute this workflow. Workflows has an execution API and client libraries that you can use for your favorite language. Here’s an example of the execution code from a Node app.js file. It simply passes the received HTTP request headers and body to the workflow and executes it:

  const execResponse = await client.createExecution({
      parent: client.workflowPath(GOOGLE_CLOUD_PROJECT, WORKFLOW_REGION, WORKFLOW_NAME),
      execution: {
        argument: JSON.stringify({headers: req.headers, body: req.body})
      }
    });

Deploy the Cloud Run service with the Workflows name and region passed as environment variables:

  gcloud run deploy ${SERVICE_NAME} \
  --image gcr.io/${PROJECT_ID}/${SERVICE_NAME} \
  --region=${REGION} \
  --allow-unauthenticated \
  --update-env-vars GOOGLE_CLOUD_PROJECT=${PROJECT_ID},WORKFLOW_REGION=${REGION},WORKFLOW_NAME=${WORKFLOW_NAME}

Connect a Pub/Sub topic to the Cloud Run service

With Cloud Run and Workflows connected, the next step is to connect a Pub/Sub topic to the Cloud Run service by creating an Eventarc Pub/Sub trigger:

  gcloud eventarc triggers create ${SERVICE_NAME} \
  --destination-run-service=${SERVICE_NAME} \
  --destination-run-region=${REGION} \
  --location=${REGION} \
  --event-filters="type=google.cloud.pubsub.topic.v1.messagePublished"

This creates a Pub/Sub topic under the covers that you can access with:

  export TOPIC_ID=$(basename $(gcloud eventarc triggers describe ${SERVICE_NAME} --format='value(transport.pubsub.topic)'))

Trigger the workflow

Now that all the wiring is done, you can trigger the workflow by simply sending a Pub/Sub message to the topic created by Eventarc:

gcloud pubsub topics publish ${TOPIC_ID} --message="Hello there"

In a few seconds, you should see the message in Workflows logs, confirming that the Pub/Sub message triggered the execution of the workflow:

Eventarc Audit Log-Storage + Workflows integration

In the second example, imagine you want a file creation event in a Cloud Storage bucket to trigger a workflow. The steps are similar to the Pub/Sub example with a few differences.

Define and deploy a workflow

As an example, you can use this workflow.yaml that logs the bucket and file names:

  main:
  params: [args]
  steps:
...
    - log:
        call: sys.log
        args:
            text: ${"Workflows received event from bucket " + bucket + " for file " + file}
            severity: INFO

Deploy a Cloud Run service to execute the workflow

In the Cloud Run service, you read the CloudEvent from Eventarc and extract the bucket and file name in app.js using the CloudEvent SDK and the Google Event library:

  const cloudEvent = HTTP.toEvent({ headers: req.headers, body: req.body });
  //"protoPayload" : {"resourceName":"projects/_/buckets/events-atamel-images-input/objects/atamel.jpg}";
  const logEntryData = toLogEntryData(cloudEvent.data);
  const tokens = logEntryData.protoPayload.resourceName.split('/');
  const bucket = tokens[3]

Executing the workflow is similar to the Pub/Sub example, except you don’t pass in the whole HTTP request but rather just the bucket and file name to the workflow:

  const execResponse = await client.createExecution({
      parent: client.workflowPath(GOOGLE_CLOUD_PROJECT, WORKFLOW_REGION, WORKFLOW_NAME),
      execution: {
        argument: JSON.stringify({bucket: bucket, file: file})
      }
    });

Connect Cloud Storage events to the Cloud Run service

To connect Cloud Storage events to the Cloud Run service, create an Eventarc Audit Logs trigger with the service and method names for Cloud Storage:

  gcloud eventarc triggers create ${SERVICE_NAME} \
  --destination-run-service=${SERVICE_NAME} \
  --destination-run-region=${REGION} \
  --location=${REGION} \
  --event-filters="type=google.cloud.audit.log.v1.written" \
  --event-filters="serviceName=storage.googleapis.com" \
  --event-filters="methodName=storage.objects.create" \
  --service-account=${PROJECT_NUMBER}-compute@developer.gserviceaccount.com

Trigger the workflow

Finally, you can trigger the workflow by creating and uploading a file to the bucket:

  echo "Hello World" > random.txt
gsutil cp random.txt gs://${BUCKET}/random.txt

In a few seconds, you should see the workflow log the bucket and object name.

Conclusion

In this blog post, I showed you how to trigger a workflow with two different event types from Eventarc. It’s certainly possible to do the opposite, namely, trigger a Cloud Run service via Eventarc with a Pub/Sub message (see connector_publish_pubsub.workflows.yaml) from Workflows or a file upload to a bucket from Workflows. 
All the code mentioned in this blog post is in eventarc-workflows-integration. Feel free to reach out to me on Twitter @meteatamel for any questions or feedback.