Faced with changing customer behaviors and demands, tightening margins, and increasing threat from digital competitors, financial services institutions (FSIs) will need to meet customers where they are, open up their services, and establish new ways to monetize their products. Doing so will also enable them to build a better profile of their customers, and deliver more personalized user experiences and fast, convenient banking and payment services. Cloud technology plays a big role in this shift toward digital FSIs. 

In Asia, bank branches now account for just 12% to 21% of monthly transactions in the region, with customers turning to digital channels for routine transactions such as peer-to-peer transfers and bill payments, according to McKinsey&Company. Overall customer engagement has climbed from an average 12.7 to 14.9 transactions a month in Asia’s developed markets, and from 6 to 8.1 in emerging markets.¹

Fueled by growing smartphone adoption, the evolving customer behavior and momentum toward digital platforms have enabled digital-first players to snag a growing piece of the banking pie. 

McKinsey estimates that digital banking penetration has grown an average of 97% in Asia’s developed markets, and 52% in emerging markets, with between 30% and 50% of those that have yet to use digital banking likely to do so.

Consumers now are more than ready to make the switch to neobanks, or digital banks. In Singapore, 63% are open to banking with digital-only players, according to a Visa study. On what will entice them to do so, 63% point to bill payments while 56% will use neobank services to make payments at retail outlets. Furthermore, 54% prefer digital banks for the convenience they offer while 52% like the faster service.

Among those who are open to digital banks, 60% will move some services from their current bank to these new players even if the latter have no prior banking experience. One in five of respondents say they are willing to switch all services to a neobank.

The same is true for small and midsize businesses (SMBs) in Singapore. According to a separate survey by Visa, 88% of these companies will consider moving some services to digital banks.

Driven to do so by their frustration over a lack of quality corporate products and control of their banking experience, 55% of SMBs believe neobanks will help bring down overall banking costs. Another 54% say digital banks offer greater convenience, while 53% point to greater ease in paying bills online.

These stats should worry even established FSIs, especially those that have not done quite enough to open up their service ecosystems and drive innovation through APIs.

An API toward new revenue

While most banks have active APIs, the services that some of them currently provide are just functional; they’re the means to an end for partners to obtain their targeted products and services. Without knowing, consumers use these types of APIs indirectly by using their favorite applications every day—a payment processing API will enable them to purchase their lunch, while a loan application API will get them that dream home.

But while banks do not always own the customer journey, they still can find opportunities to sell their products via partners. Many leading banks are leveraging key technologies, such as API management, artificial intelligence (AI), and data analytics to embed digital banking into consumers’ everyday lives, including groceries, travel, entertainment, healthcare, and food delivery. 

When traditional banks open up their APIs to third parties offering broader services that pull in unique services into their own apps, they then become plugged into the broader customer journey. This helps boost usage of their services and embeds them in the overall customer experience. It also provides aggregated data that will help banks build richer consumer profiles, and deliver more personalized products and services.

APIs also create equal opportunities for smaller participants to be involved in the financial services ecosystem, potentially creating micro-segments that previously may not have existed. With insufficient demand within a closed system, to justify the provision of such services, some customers in these micro-segments have previously been left unserved. The APIs, which facilitate collaboration between the different micro-segments so they can be commercially viable, help assuage this problem. 

Some banks are also opening up APIs to allow access to datasets that enable businesses to trigger automated workflows and enhance their operational efficiencies. Others, such as Bank Rakyat Indonesia (Bank BRI) have generated new revenue by leveraging Google Cloud’s Apigee to manage their API lifecycle and identify new revenue opportunities.

Apigee’s monetization feature has helped Bank BRI realize $50 million in revenue and enabled the bank to define its pricing based on API calls and automatically bill based on usage.

In addition, the Indonesian bank uses the data analysis alongside Google Maps Platform to score its customer base of 75.5 million, and identify those who can be recruited as BRILink agents for underbanked areas. These agents are customers who maintain a minimum balance of $800 USD and score high on reliability.

The appointment of branchless agents via the Agent BRILink app has pushed the loan volume from the bank’s branchless business to $26 billion in 2018, up from $15 billion the year before.

How banks can get started with APIs

Clearly, there are new revenue opportunities for banks to leverage the data they already have. Here are some tips to help FSIs kickstart their API journey:

• Align with internal leadership growth initiatives. Leverage executive key performance indicators around growth and cost savings to foster a culture that offers APIs to micro-segmented markets with an eye on cultivating a healthy financial services ecosystem.
• Productize APIs with a strong value proposition. Starting with an API-first approach, stock the shelves of your API shop with new services and a strong inventory of APIs that will entice third parties (i.e., retailers, telcos, etc.) to start using them. This customer-first, outside-in approach will serve as a strong base to build on and enable the addition of more APIs as adoption grows.
• Actively nurture a developer community. A properly trained API manager will ensure constant contact with the developer community, and that partners are provided with case studies to help them identify viable use cases for your APIs.
• Leverage security as a strategic enabler. Security is a key enabler of the API economy, and most API security postures are defensive. By leveraging deep security tooling together with strong identification of developers, banks can better track information and data usage offensively. 

FSIs also need to avoid some common pitfalls, such as overlooking the need to continuously improve their APIs. If no one is using it, the API clearly is failing to provide any real value to third-party developers.

In addition, efforts should be made to market the APIs and let developers know what is available. A common mistake FSIs make is assuming their work is done once their APIs are released and neglecting the need to carry out community outreach and marketing to generate awareness about the APIs.

If you are interested in learning more about this topic, don’t miss our session at the Google Cloud Financial Services Summit on Embedded Finance: The Future of Banking.

^{1. McKinsey & Company. “Asia’s digital banking race: Giving customers what they want.” Global Banking Practice. April 2018.}

The rapidly evolving landscape of distributed computing demands efficient and scalable frameworks. Ray.io is an open-source framework to easily scale up Python applications across multiple nodes in a cluster. Ray provides a simple API for building distributed, parallelized applications, especially for deep learning applications.

Google Kubernetes Engine (GKE) is a managed container orchestration service that makes it easy to deploy and manage containerized applications. GKE provides a scalable and flexible platform that abstracts away the underlying infrastructure.

KubeRay enables Ray to be deployed on Kubernetes. You get the wonderful Pythonic unified experience delivered by Ray, and the enterprise reliability and scale of GKE managed Kubernetes. Together, they offer scalability, fault tolerance, and ease of use for building, deploying, and managing distributed applications.

In this blog post, we share a solution template to get you started easily with Ray on GKE. We discuss the components of the solution and showcase an inference example using Ray Serve for Stable Diffusion.

Overview of the Solution

In this solution template we use KubeRay, an OSS solution for managing Ray clusters on Kubernetes, as the operator for provisioning our workloads. Follow the step-by-step instructions in the README file to get started. The solution contains two groups of resources: platform-level and user-level.

Platform-level resources are expected to be deployed once for each development environment by the system administrator. These include the common infrastructure and GCP service integrations that are shared by all users.

• GKE cluster and node pool. Configurations can be changed in the main.tf file. This module deploys a GKE cluster with a GPU node pool, including required Nvidia drivers for GPUs. You can replace these with other machine types.
• Kubernetes system namespace and service accounts, along with the necessary IAM policy bindings. This allows the platform administrator to provide fine-grained user access control and quota policies for Ray cluster resources.
• KubeRay operator. The operator is responsible for watching for changes in KubeRay resources and reconciling the state of the KubeRay clusters.  
• Logging. The `logging_config` section enables logs from system components and workloads to write logs to Cloud logging.
• Monitoring. The `monitoring_config` section enables Managed Prometheus integration. This allows the deployment to automatically scrape system-level metrics and writes them to the managed metrics service.
• Workload identity. This enables your workloads to authenticate with other GCP services using Google IAM service accounts.

User-level resources are expected to be deployed once by each user in the development environment.

• KubeRay cluster. This is the actual Ray cluster that we will be used for your workloads. It is configured to use a Workload Identity pool and a IAM-binded service account that provides fine-grained access to GCP services. You can customize the Ray cluster settings by editing the kuberay-values.yaml file.

• Logging. The solution adds a side car container deployed alongside each KubeRay worker node. This uses fluentbit to forward Ray logs from the head node to Cloud logging. You can edit the fluentbit-config file to change how the logging container filters and flushes logs.
• Monitoring. This module provides a PodMonitoring resource that scrapes metrics from the user’s Ray cluster and uploads data points to Google Managed Prometheus. An optional installation for Grafana dashboard is included and can be accessed through a web browser.
• JupyterHub server. This module installs a JupyterHub notebook server in the user namespace, enabling users to interact directly with their Ray clusters.

Run a Workload on Your Ray Cluster

Let’s try running the provided example with Ray Serve to deploy Stable Diffusion. This example was originally taken from the Ray Serve documentations here. To open the example in Jupyter notebook, go to the external IP for proxy-public in your browser (instructions to get the IP). And then click on File -> Open from URL, and input the raw URL of the notebook to open it.

Since the notebook runs in the same Kubernetes cluster as the Ray cluster, it is able to talk directly to the latter using its cluster-internal service endpoint – thus there is no need to expose the Ray cluster to public internet traffic. For production workloads, you should secure your endpoints with GCP account credentials. Google Cloud Identity Aware Proxy (IAP) can be used to enable fine-grained access control to user resources, such as our Ray cluster, to protect your GCP resources from unnecessary exposure. A full tutorial on how to enable IAP on your GKE cluster can be found here.

The notebook contains code for deploying a pre-trained model to a live endpoint. The last cell makes a call to the created service endpoint:prompt = “a cute cat is dancing on the grass.”input = “%20″.join(prompt.split(” “))resp = requests.get(f”http://example-cluster-kuberay-head-svc:8000/imagine?prompt={input}”)with open(“output.png”, ‘wb’) as f:   f.write(resp.content)

Executing the notebook will generate a file with a unique picture of a cute cat. Here is an example we got:

Congratulations! You have now deployed a large model for image generation on GKE.

Logging and Monitoring

As mentioned earlier, this solution enables logging and monitoring automatically. Let’s find those logs.

In your Cloud Console, open up Logging -> Log Explorer. In the query text box, enter the following:resource.type=”k8s_container”resource.labels.cluster_name=%CLUSTER_NAME%resource.labels.pod_name=%RAY_HEAD_POD_NAME%resource.labels.container_name=”fluentbit”

You should see the Ray logs from your cluster forwarded here.

To see your monitoring metrics, go to Metrics Explorer in the Cloud Console. Under the menu for “Target”, select “Prometheus Target” and then “Ray”. Select the metric that you want to see, for instance `prometheus/ray_component_cpu_percentage/gauge`:

The deployment also comes with a Grafana deployment. Follow this guide to open it up and view your Ray cluster’s metrics.

Conclusion

The combination of Ray and GKE offers a simple and powerful solution for building, deploying, and managing distributed applications. Ray’s simplicity makes it an attractive choice for data and model developers while GKE’s scalability and reliability is the defacto choice for enterprise platforms. The solution template presented in this blog post offers a convenient way to get started quickly with KubeRay, the recommended approach to deploy Ray on GKE.

If you have any questions for building Ray on Kubernetes and GKE, you can contact us directly at ray-on-gke@google.com or comment in GitHub. Learn more about building AI Platforms with GKE by visiting our User Guide.

Editor’s note: Today we’re hearing from Sagar Batchu, Director of Engineering at LiveRamp. He shares how Google Cloud helped LiveRamp modernize its data analytics infrastructure to simplify its operations, lower support and infrastructure costs and enable its customers to connect, control, and activate customer data safely and securely. 

LiveRamp is a data connectivity platform that provides best in class identity resolution, activation and measurement for customer data so businesses can create a true customer 360 degree view. We run data engineering workloads at scale, often processing petabytes of customer data every day via LiveRamp Connect platform APIs. 

As we integrated more internal and external APIs and the sophistication of our product offering grew, the complexity of our data pipelines increased. The status quo for building data pipelines very quickly became painful and cumbersome as these processes take time and knowledge of an increasingly complex data engineering stack. Pipelines became harder to maintain as the dependencies grew and the codebase became increasingly unruly.

Beginning last year, we set out to improve these processes and re-envision how we reduce time to value for data teams by thinking of our canonical ETL/LT analytics pipelines as a set of reusable components. We wanted teams to spend their time adding new features which encapsulate business value rather than spending time figuring out how to run workloads at scale on cloud infrastructure. This was even more pertinent with data science, data analyst and services teams whose daily wheelhouse was not the nitty gritty of deploying pipelines. 

With all this in mind, we decided to start a data operations initiative, a concept popularised in the last few years, which aims to accelerate the time to value for data-oriented teams by allowing different personas in the data engineering lifecycle to focus on the “what” rather than the “how.”  

We chose Google Cloud to execute on this initiative to speed up our transformation. Our architectural optimizations, coupled with Google Cloud’s platform capabilities simplified our operational model, reduced time to value, and greatly improved the portability of our data ecosystem for easy collaboration. Today, we have ten teams across LiveRamp running hundreds of workloads a day, and in the next quarter, we plan to scale to thousands. 

Why LiveRamp Chose Google Cloud 

Google Cloud provides all the necessary services in a serverless fashion to build complex data applications and run massive infrastructure. Google Cloud offers data analytics capabilities that help organizations like LiveRamp to easily capture, manage, process and visualize data at scale. Many of the Google Cloud data processing platforms also have open source roots making them extremely collaborative. One such platform is CDAP (Cask Data Application Platform), which Cloud Data Fusion is built on. We were drawn to this for the following reasons:

• CDAP is inherently multicloud. Pipeline building blocks known as Plugins define individual units of work. They can be run through different provisioners which implement managed cloud runtimes.
• The control plane is a set of microservices hosted on Kubernetes, whereas the data plane leverages the best of breed big data cloud products such as Dataproc.
• It is built as a framework and is inherently extensible, and decoupled from the underlying architecture. We can extend it both at the system and user-level through “extensions” and “plugins” respectively. For example, we were able to add a system extension for LiveRamp specific authorisation and build a plugin that encompasses common LiveRamp identity operations.
• It is open sourced, and there is a dedicated team at Google Cloud building and maintaining the core codebase as well as a growing suite of source, transform and sink connectors.
• It aligns with our remote execution and non-data movement strategy. CDAP executes pipelines remotely and manages through a stream of metadata via public cloud APIs. 
• CDAP supports an SRE mindset by providing out of the box monitoring and observability tooling.
• It has a rich set of APIs backed by scalable microservices to provide ETL as a Service to other teams.
• Cloud Data Fusion, Google Cloud’s fully managed, native data integration platform is based on CDAP. We benefit from the managed security features of Data Fusion like IAM integration, customer manager encryption keys, role based access controls and data residency to ensure stricter governance requirements around data isolation. 

How are teams using the Data Operations Platform? 

Through this initiative, we have encouraged data science and engineering teams to focus on business logic and leave data integrations and infrastructure as separate concerns. A centralised team runs CDAP as a service, and custom plugins are hosted in a democratized plugin marketplace where any team can contribute their canonical operations.

Adoption of the platform was driven by one of our most common patterns of data pipelining: The need to resolve customer data using our Identity APIs. LiveRamp Identity APIs connect fragmented and inaccurate customer identity by providing a way to resolve PII to pseudonymous identifiers. This enables client brands to connect, control, and activate customer data safely and securely. 

The reality of customer data is that it lives in a variety of formats, storage locations, and often needs bespoke cleanup. Before, technical services teams at LiveRamp had to develop expensive processes to manage these hygiene and validation processes even before the data was resolved to an identity. Over time, a combination of bash and python scripts and custom ETL pipelines became untenable. 

By implementing our most used Identity APIs, a series of CDAP plugins, our customers were able to operationalise their processes by logging into a Low Code user interface, select a source of data, run standard validation and hygiene steps, visually inspect using CDAP’s Wrangler interface for especially noisy cases, and channel data into our Identity API. As these workflows became validated, they have been established as standard CDAP pipelines that can now be parameterized and distributed on the internal marketplace. These technical services teams have not only reduced their time to value but have also enabled future teams to leverage their customer pipelines without worrying about the portability to other team’s infrastructures.

What’s Next ? 

With critical customer use cases now powered by CDAP, we plan on scaling out usage of the platform to the next batch of teams. We plan on taking on more complex pipelines, cross-team workloads, and adding support for the ever growing LiveRamp platform API suite.

In addition to the Google Cloud community and the external community, we have a growing base of LiveRamp developers building out plugins on CDAP to support routine transforms and APIs. These are used by other teams who push the limits and provide feedback — spinning a flywheel of collaboration between those who build and those who operate. Furthermore, teams internally can continue to use their other favorite data tools like BigQuery and Airflow as we continue to deeply integrate CDAP into our internal data engineering ecosystem. 

Our data operations platform powered by CDAP is quickly becoming a center point for data teams – a place to ingest, hygiene, transform, and sink their data consistently.

We are excited by Google Cloud’s roadmap for CDAP and Data Fusion. Support for new execution engines, data sources and sinks, and new features like Datastream and Replication will mean LiveRamp teams can continue to trust that their applications will be able to interoperate with the ever evolving cloud data engineering ecosystem.

Data-savvy businesses increasingly rely on images and videos for critical functions, and yet are challenged by the sheer mass of information—more than 3.2 billion images and 720,000 hours of video are created daily. This explosion in visual data has paved the way for the growth of computer vision, a form of artificial intelligence (AI) that enables computers to “see” the world similarly to the way people do, but with unblinking consistency, and greater accuracy. 

The transformational impact and value of computer vision solutions are significant and has been a guiding objective for companies and AI developers. And yet, even as the applications for computer vision increase dramatically, architecting and implementing vision AI solutions remain highly complex. Visual data, such as images and video, are made up of thousands of pixels of information that represent millions of different patterns and meanings, which can make interpreting even a single image overwhelming from a computational perspective.

Many organizations struggle with deployments and fail to operationalize vision AI solutions due to development delays, machine learning and data science hiring challenges, inaccurate output, a lack of integration with existing infrastructure, difficulty of use, and high cost. Plainsight, with the power of Google Cloud resources, is addressing all these challenges and helping businesses by enabling the deployment of vision AI within enterprise private networks that can be managed easily and scaled economically.

Plainsight has announced availability of its vision AI platform on Google Cloud Marketplace. Businesses can now easily deploy end-to-end vision AI to private clouds to realize the full value of their video and other visual data for accurate, actionable insights across diverse use cases.

Delivering on the Promise of AI: Seeing What’s Hiding In Plain Sight

For organizations to integrate AI and machine learning into their businesses successfully, the technology must be powerful enough to solve real challenges, yet fast, easy, and accessible enough to ensure the innovation potential is realized. Plainsight on Google Cloud delivers the power of enterprise vision AI that’s quick and easy to use with Google Cloud resources that enable global scale, increased security, bolstered privacy, unified billing, and cost savings. 

To streamline vision AI workflows, Plainsight facilitates the entire pipeline, from visual data ingestion and annotation, through continuous model training, deployment, and monitoring for easier innovation and faster time-to-production. Our platform accelerates vision AI development in a manner that is complete, accurate, and accessible to non-technical business leaders. We believe that AI should be available and accessible to anyone and everyone—so that teams across entire organizations can reap the benefits. 

By integrating Plainsight into their private networks, companies worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. These use cases include: social distancing monitoring, medical imaging, drug compound screening, defect detection in manufacturing processes, identifying gas leaks, or even livestock counting and crop health monitoring for agriculture, to name a few.https://www.youtube.com/embed/A7U_0UkjvEg?enablejsapi=1&

We enable customers so they can create successful solutions that enable them to clearly see their business from all angles and to take advantage of the knowledge visual data can reveal by simply and quickly operationalizing practical vision AI applications.

AI-Powered Dataset Creation, Automated Model Training & Easy Deployment Without A Single Line Of Code 

For vision AI applications, success is inextricably dependent on the quality and quantity of the datasets required to train the relevant models. To aid enterprises in this vital stage, the Plainsight platform provides built-in data annotation for the fast and easy creation of datasets. This includes AI-powered features that accelerate the speed and quality of labeling such as SmartPoly, for the automated polygon masking of objects, TrackForward, to predict and automatically label objects from frame to frame in video annotations, and AutoLabel for automated object recognition and labeling based on pre-trained machine learning models, to highlight a few.

In addition, to ensure the success of AI integration, we significantly reduce time-intensive processes with Plainsight vision AI’s automated machine learning with continuous model training and easy deployment capabilities. In just a few clicks, users can leverage optimizations for the most reliable model training without endless experimentation cycles. And, models are easily deployed at scale all within one, easy-to-manage model operationalization process for the business.

Growing With Google Cloud

Plainsight is a vision AI innovation leader, developing solutions that address unmet needs for challenger brands and Fortune 500s across vertical markets. As a team recognized for succeeding where others have failed, our expanding partnership with Google Cloud provides a powerful combination that helps customers see and activate the value of their visual data with a suite of services in a secure and private manner.

Our vision AI Platform simplifies building and operationalizing AI to solve business problems enterprises are facing every day—and the demand is increasing. To accelerate our journey to faster, more accessible AI for enterprises, we knew we needed strong support to grow Plainsight and scale our backend tools to match our vision. 

Google Cloud delivered everything, and more, in one program. The Startup Program by Google Cloud provided the technology and services for scale and the support we needed to maximize the value the Program provided us. The Startup Program has been a springboard for architecting Plainsight vision AI in the cloud, accelerating our goals and optimizing innovation, efficiency, and growth. The team also helped us optimize Google Ads campaigns, fueling adoption of Plainsight. 

After launching the SaaS version of Plainsight Data Annotation in November 2020, we grew our user base by nearly 110x in just three short months. Google Ads has also dramatically increased website traffic, growing new users by nearly 5.75X and page views by over 5X. The Google team helped us identify where Google Cloud offerings could be leveraged instead of developing in-house solutions and offered best practices that enabled us to deliver faster on our initiatives. 

Kubernetes was already the underlying component of our platform and leveraging Google Kubernetes Engine (GKE) as a managed service removed a layer of complexity. By combining GKE and Anthos, we were able to standardize our deployments, aligning to how our customers leverage Anthos for enterprise applications in their own organizations. In addition, as a fast-moving, customer-centric company we use Google Workspace to help us centralize and manage our day-to-day work internally. By leveraging multiple products across Google’s ecosystem, we take advantage of a holistic partnership that has helped our business tremendously as we scale. 

Leveraging Google’s Partners for Strategic Consultation

To facilitate this expansion of our partnership with Google and to maximize our use of Google Cloud services, we are working with DoiT International, a Google Managed Services Provider and 2020 Global Reseller Partner of the Year. DoiT provides us with ongoing technical consultation for cloud-native architecture, Google Cloud Marketplace integration, production-grade Kubernetes support, Google Cloud cost optimization, and technical support. The DoiT team has been invaluable in compiling best practices, tips, and strategies from their vast experience with various cloud customers to ease our Marketplace integration and is providing input for infrastructure strategy to support our continued rapid growth.

Plainsight Delivers Enterprise Vision AI Through The Google Cloud Platform Marketplace

Plainsight vision AI is now available to Google Cloud Customers on Google Cloud Marketplace enabling organizations across industries to deploy private Plainsight instances within their own environments. Marketplace customers will benefit from Google Cloud privacy, security, scalability and unified billing through their Google Cloud account. 

Combining the powerful benefits provided by Google Cloud resources with Plainsight’s vision AI Platform into private networks, enterprises worldwide can now leverage one intuitive platform for centralized control of streamlined vision AI model creation and training with optimized visual data handling for diverse enterprise solutions. 

Through our Google partnership, we’re able to leverage a powerful foundation that allows us to rapidly innovate, scale and accelerate delivery on our vision AI platform capabilities. By executing on our vision to make AI easier, faster and more accessible for all users across entire enterprises, we’re helping businesses see more and by seeing more, they’ll have the power to solve more. 

If you want to learn more about how Google Cloud can help your startup, visit our page here where you can apply for our Startup Program, and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.

Headless Ecommerce

In the last couple of years there has been a shift in the way retailers approach ecommerce: where in the past development efforts were prioritized around building a solid foundation for backend transactions and operations now it is clear that companies in this space are focusing on differentiating themselves by creating unique shopping experiences that increase engagement and reduce friction. 

But how can development teams spend the necessary time designing and writing code for this kind of interactions while also having to seamlessly maintain ecommerce vital components like online catalogs, shopping carts and checkout payment processes? Enter headless commerce.  

Headless commerce (HC) helps companies of all sizes to innovate, develop and launch in less time and using fewer resources by decoupling backend and frontend. Headless solution providers empower online retailers by offering a balance between flexibility and optimization through pre-built api-accessible modules and components that can be easily plugged into their frontend architecture. This translates into rapid development while keeping desired levels of security, compliance, integration and responsiveness. 

This composable approach enables dev teams not only to create new features but also connect other ecommerce components with less effort which is critical when responding to business trends. But above all, the main benefit retailers receive from HC, is owning and controlling the frontend for an engaging customer journey as well as quickly launching new experiences.  

Google Cloud + commercetools

commercetools, a leader in the headless commerce space, has partnered with Google Cloud to make their cloud-native SaaS platform available in the Google Cloud Marketplace. With a flexible API system (REST API and GraphQL), commercetools’ architecture has been designed to meet the needs of demanding omnichannel ecommerce projects while offering real flexibility to modify or extend its features. It supports a variety of storefront providers like Vue Storefront, offers a large set of integrations and supports microservice-based architectures. All this while providing access to multiple programming languages (PHP, JS, Java) via its SDK tools. 

commercetools and Google Cloud provide development teams with all the tools to build high-quality digital commerce systems. Google Cloud’s scalability, AI/ML components, API management capabilities and CI/CD tools are a perfect fit to build frontend shopping experiences that easily integrate with the commercetools stack. Developers can take advantage of this compatibility by:

• Integrating systems with Google’s Retail Search, Recommendations AI and Vision Product Search
• Injecting serverless functions into commercetools using Google Cloud Functions
• Extending and integrating commercetools via Events handled by Pub/Sub
• Managing 3rd party, legacy, microservices and commercetools APIs with Apigee
• Selecting the Google Cloud region commercetools uses for zero latency for custom apps
• Expanding their microservice ecosystem with components like Cloud Storage, Cloud SQL, Firestore and BigQuery

Additionally, commercetools allows ecommerce solutions to tap into the wider Google Ecosystem by providing authoritative data via Merchant Center, advertising via product listing ads and selling via Google Shopping. 

Architecture Overview

As mentioned previously, headless commerce is increasingly preferred by retailers who want to own and control the ‘front-end’ for providing and enabling an engaging and differentiated user and shopping experiences. 

The approach involves a loosely coupled architecture that separates ‘front end’ from the ‘back end’ of a digital commerce application. The front end is typically built and managed by the retailer. They want to leverage an independent software vendor (ISV) offered, ready-to-use ‘back-end’ commerce building blocks for capabilities, such as product catalog, pricing, promotions, cart, shipping, account and others.

Most retailers want to invest their time and resources in building a front end that requires an agile development model to introduce new and tweaking existing user experiences to acquire and retain customers. A few retailers that do not have an in-house web development team may choose an ISV that offers ready to use front end. The front end is a web app and designed as a progressive web application (PWA) on Google Cloud. The backend is a headless commerce offered by an ISV, such as commercetools. The backend commerce capabilities are built as a set of microservices, exposed as APIs, run cloud-native and implemented as headless. It is commonly referred to as the  “MACH” solution. The API-first approach of the architecture allows easily integrating ‘best of breed’ capabilities built internally and/or offered by 3rd party ISVs.

Leveraging Google Cloud Components

The architecture of the front end will be implemented on Google Cloud and will integrate with the ISV’s headless commerce back end that runs natively in Google Cloud. 

The front end will be designed using cloud-native services for 

• PWA web app development (Google Kubernetes Engine, CI/CD services), 
• Google Product Discovery solution that includes Retail Search and Vision API Product Search for serving product search (text and image) and Recommendations AI for serving recommendations.
• Storage (Cloud Storage), Database (Cloud SQL, Cloud Firestore), and edge caching for content delivery (Cloud CDN) 
• Networking (Cloud DNS,  Global Load Balancing), and 
• Security (Cloud Armor for DDoS, API Defense for API protection) 

Additionally, API management (Apigee on Google Cloud) can be used to orchestrate interactions of the front end with the APIs of the backend commerce services. The API management’s capability will be used for accessing the services of on-premises systems, such as ERP, order management system (OMS), warehouse management system (WMS) as needed to support the functioning of digital commerce application.  Alternatively, depending on the frontend capabilities, developers can use middleware to build custom services and route requests. 

What’s next?

A considerable number of retailers have adopted headless commerce and are now focusing on adopting best practices and leveraging the agility that comes with this approach. Just like commercetools offers robust components that meet the retailer’s backend operational needs (Product Catalog, Order Management, Carts, Payments, etc), Google Cloud’s Compute, Networking, Severless and AI/ML services  provide the agility and flexibility required by development teams to quickly and easily extend their frontend capabilities. 

commercetools and Google Cloud work seamlessly together because they both prioritize ease of integration, scalability, security and iterability while providing ready-to-use building blocks. It also helps that commercetools backend runs on Google Cloud. Once an initial foundation of Google Cloud and commercetools has been established, adding new commerce modules and extending functionally of the current ones becomes a straightforward process that allows to route efforts to innovation initiatives. In the end, the main beneficiaries of this technical synergy are the shoppers that enjoy experiences which increase engagement and minimize friction. 

Alternatively, retailers can also save time and resources by relying on frontend integrations. commercetools offers a variety of third-party solutions that can effortlessly be added to a headless commerce architecture. These integrations as well as other important headless commerce extensions will be explored in future blog entries.  In the meantime, all the necessary tools to leverage headless commerce can be found in just one place: 

Get started with commercetools on the Google Cloud Marketplace today!