As we enter the holiday season, retailers are working behind the scenes to ensure they can provide the best experiences for customers, in store and online. Challenges in retail do not begin or end during the holiday season as sudden shifts in customer preferences, supply chain nuances, and overall demand ebbs and flows take place year round and retailers must be prepared to adapt swiftly.

Google Cloud’s retail customers globally, in total, saw more online traffic in the first six months of 2022 than all of 2019. This year, retailers can expect an early launch to holiday shopping activities, as 50% of consumers plan to start purchasing goods before the traditional Black Friday kick-off.

The very same improvements made to automate and improve retail infrastructure can prepare it for holiday surges and support year-round innovation. Let’s take a look at how Ulta Beauty, the largest beauty retailer in the U.S., is partnering with Google Cloud, MongoDB Atlas, commercetools, and HCLTech to cover these two areas and more.

Architecting for innovation

Creating personalized shopping experiences in stores and online is key to Ulta Beauty’s success. This commitment is best demonstrated through Ulta Beauty’s Virtual Beauty Advisor. Built on Google Cloud, this tool enhances shoppers’ experiences with personalized recommendations in addition to the ability to try on makeup virtually with GLAMLab.

As innovators in support of the best possible guest experience, Ulta Beauty needed to re-architect its infrastructure for greater agility and stability.

To start, Ulta Beauty chose to use Google Kubernetes Engine (GKE) as the backbone and orchestrator to build and deploy cloud-native applications. The Google Cloud deployments coincided with an organizational move from end-to-end application development to one that focuses on individual features, specific modules, and micro-applications.

This strategic change allowed Ulta Beauty to fix bugs, experiment with new offerings, and drive customer experiences faster and more efficiently. Thanks to the transformation and GKE, Ulta Beauty’s developer team now accelerates time to market for new products and services, and delivers new ways to engage with customers more quickly. These efforts all ladder to create ‘WOW’ experiences for the retailers’ guests who have emotional and personal connections to beauty and wellness. They can now discover and experience products that are served to them based on individual preferences.

Adapting to the new environment comes with its own set of challenges. “Microservices are not a silver bullet,” says Sethu Madhav Vure, IT Architect, Ulta Beauty. “For Ulta Beauty, the biggest challenge was how to break up a monolithic environment into multiple applications. We had to evolve our core systems—without impacting today’s services—and address what was needed for the future.”

Google Cloud partner HCLTech provided expert guidance throughout the re-architecting process, defining the solution blueprint and cloud-native deployment architecture through cross-functional workshops. HCLTech then assisted with the actual migration and platform setup, paving the way for fully automated, continuous integration and continuous delivery (CI/CD) pipelines to support faster rollouts and deployment architecture to drive higher availability and scalability.

Ulta Beauty took a domain-driven design approach to identify operations that could be grouped together to reduce complexity and improve scalability. Now, the applications are based on multiple domains, such as Commerce, Promotions, Catalog, Order, Customer, and Inventory. The new architecture prompted a fresh look at storage requirements to scale dynamically alongside its modernized applications.

For Ulta Beauty, MongoDB Atlas proved to be the best database solution for dynamic scaling, ease-of-use, and integrations with Google Cloud. The company also leveraged an entry-level plan to prove the value of MongoDB Atlas before investing in the technology.

“MongoDB Atlas offers a free tier that gave us an opportunity to quickly demonstrate tangible benefits of a proof of concept,” says Vure. “Once we proved the value of MongoDB Atlas, we benefited from the straightforward resource allocation supported by Google Cloud and MongoDB.”

Integrations between MongoDB Atlas and Google Cloud allow Ulta Beauty to take an iterative approach to new projects. The company creates new clusters in an existing project, then piggybacks them onto an existing Private Service Connect setup between a MongoDB project and Google Cloud project.

By removing complexities within infrastructure management, Ulta Beauty can manage its incredible amount of data, such as member preferences and purchases, that fuels its event-driven architecture. The much more agile infrastructure enables Ulta Beauty to deploy and scale offerings faster than ever.

“We recently had an unplanned traffic surge that impacted our domain services. It took less than an hour for MongoDB Atlas to scale up to the next level of the cluster and manage that traffic,” says Vure. “The on-demand, dynamic scaling, plus GKE, has saved the day more than once.”

Preparing for a happy holiday season

This holiday season, Ulta Beauty has a stronger technical foundation to manage demand surges and provide customers seamless shopping experiences. Previously, the company used 50 pods in a cluster, each with 6 GB of RAM without domain stores, to handle about 100 transactions each second. With domain stores, the same 6 GB of RAM with just 20 GKE pods was able to scale up to 2,400 transactions per second.

With Google Cloud as its technology foundation, Ulta Beauty partnered with Google Cloud partner commercetools to evolve its application APIs as products and properly separate interfaces and capabilities.

Ulta Beauty uses event-based integrations within commercetools to identify how best to leverage Cloud Pub/Sub middleware on top of MongoDB Atlas integrations. Patterns established here were extended into MongoDB change streams and in turn improved business processes.

“Working with the right technology partners has helped us to avoid analysis paralysis that can happen when developer teams spend a lot of time trying to understand and manage every detail,” says Vure. “Instead, we convert a proof of concept into a working solution, and quickly bring it to market. It’s been a major shift in our IT culture as we try out new things weekly and see incredible support from leadership.”

The improvements enable Ulta Beauty to maintain a high level of innovation, performance, and customer service year-round. Now, when the holiday shopping season begins, Ulta Beauty is prepared to handle surges in traffic through auto-scaling with Google Cloud and MongoDB Atlas. Customers get what they want, when they want, free from the frustrations of outages.

“With these changes, we are ready for a holiday season that everyone–even those of us in IT—gets to enjoy. We’re positioned to continuously focus on new, better ways to serve our guests,” says Vure.

Check out MongoDB and commercetools on Google Cloud Marketplace to learn more about what these partners can do for your business.

The last few years have underscored the importance of speed in bringing new drugs and medical devices to market, while ensuring safety and efficacy. Over this time, healthcare and life sciences organizations have transformed the way they research, develop, and deliver patient care by embracing agility and innovation.

Now, the industry is set to reap the benefits of cloud technology and overcome the existing barriers to innovation.

What’s holding back innovation?

Costly clinical trials: The process of trialing and developing new drugs and devices is still long and costly, with more than 1 in 5 clinical trials failing due to a lack of funding.1 The high failure rate comes as no surprise when you consider the average clinical trial costs $19 million and takes 10-15 years (through all 3 phases) to be approved.2

Stringent security requirements: Pre-clinical R&D and clinical trials use large volumes of highly sensitive patient data – making the life sciences industry one of the top sectors targeted by hackers.3 On top of this, the FDA and other regulatory bodies have strict requirements for medical device cybersecurity.

Unpredictable supply chains: Global supply chains are becoming increasingly complex and unpredictable. This can be brought on by anything from supply shortages, to geo-political events, and even bad weather. Making things worse is the lack of visibility into medical shipment disruptions – so when disaster strikes you’re often caught off guard.

Google Cloud for life sciences

At Alphabet, we’ve made significant investments in healthcare and life sciences, helping to tackle the world’s biggest healthcare problems, from chronic disease management, to precision medicine, to protein folding.

Together with Google, you can transform your life sciences organization and deliver secure, data-driven innovation across the value chain.

• Accelerate clinical trials to deliver life-saving treatments faster and at less cost. Clinical trials require relevant and equitable patient cohorts that can produce clinically valid data. Solutions like DocAI can enable optimal patient matching for clinical trials, helping organizations optimize clinical trial selection and increase time to value. How that patient data is collected is also important. Collection in a physician’s office captures a snapshot of the participant’s data at one point in time and doesn’t necessarily account for daily lifestyle variables. Fitbit, used in more than 1,500 published studies–more than any other wearable device–can enrich clinical trial endpoints with new insights from longitudinal lifestyle data, which can help improve patient retention and compliance with study protocols. We have introduced Device Connect for Fitbit, which empowers healthcare and life sciences enterprises with accelerated analytics and insights to help people live healthier lives. We are able to empower organizations to improve clinical trials in key ways:

1. Enable clinical trial managers to quickly create and launch mobile and web RWE collection mechanism for patient reported outcomes
2. Enable privacy controls with Cloud Healthcare Consent API and, as needed, remove PHI using Cloud Healthcare De-identification API
3. Ingest RWE and data into BigQuery for analysis
4. Leverage Looker to enable quick visualization and powerful analysis of a study’s progress and results

• Ensure security and privacy for a safe, coordinated, and compliant approach to digital transformation. Google Cloud offers customers a comprehensive set of services including pioneering capabilities such as BeyondCorp Enterprise for Zero Trust and VirusTotal for malicious content and software vulnerabilities; Chronicle’s security analytics and automation coupled with services such as Security Command Center to help organizations detect and protect themselves from cyber threats; as well as expertise from Google Cloud’s Cybersecurity Action Team. Google Cloud also recently acquired Mandiant, a leader in dynamic cyber defense, threat intelligence and incident response services.

• Optimize supply chains and enhance your data to prepare for the unpredictable. With a digital supply chain platform, we can empower supply chain professionals to solve problems in real time including visibility and advanced analytics, alert-based event management, collaboration between teams and partners, and AI-driven optimization and simulation.

Ready to learn more? We’ll be taking a deep dive into each of the challenges outlined above in our life sciences video series. Stay tuned.

1. National Library of Medicine
2. How much does a clinical trial cost?
3. Life Sciences Industry Becomes Latest Arena in Hackers’ Digital Warfare

Compute Engine is a customizable compute service that lets you create and run virtual machines on Google’s infrastructure. You can create a Virtual Machine (VM) that fits your needs. Predefined machine types are pre-built and ready-to-go configurations of VMs with specific amounts of vCPU and memory to start running apps quickly. With Custom Machine Types, you can create virtual machines with the optimal amount of CPU and memory for your workloads. This allows you to tailor your infrastructure to your workload. If requirements change, using the stop/start feature you can move your workload to a smaller or larger Custom Machine Type instance, or to a predefined configuration.

Machine types

In Compute Engine, machine types are grouped and curated by families for different workloads. You can choose from general-purpose, memory-optimized, compute-optimized and accelerator-optimized families. 

• General-purpose machines are used for Day-to-day computing at a lower cost and for balanced price/performance across a wide range of VM shapes. The use cases that best fit here are web serving, app serving, back office applications, databases, cache, media-streaming, microservices, virtual desktops, development environments.
• Memory-Optimized machine are recommended for ultra high-memory workloads such as in-memory analytics and large in-memory databases such as SAP HANA 
• Compute-Optimized machines are recommended for ultra high performance workloads such as High Performance Computing (HPC), Electronic Design Automation (EDA), gaming, video transcoding, single-threaded applications.
• Accelerator-Optimized machines are optimized for high performance computing workloads such as Machine learning (ML), Massive parallelized computations and High Performance Computing (HPC)

How does it work?

You can create a VM instance using a boot disk image, a boot disk snapshot, or a container image. The image can be a public operating system (OS) image or a custom one. Depending on where your users are you can define the zone you want the virtual machine to be created in. By default all traffic from the internet is blocked by the firewall and you can enable the HTTP(s) traffic if needed. 

Use snapshot schedules (hourly, daily, or weekly) as a best practice to back up your Compute Engine workloads. Compute Engine offers live migration by default to keep your virtual machine instances running even when software or hardware update occurs. Your running instances are migrated to another host in the same zone instead of requiring your VMs to be rebooted. 

Availability

For High Availability (HA) Compute Engine offers automatic failover to other regions or zones in event of a failure. Managed instance groups (MIGs) help keep the instances running by automatically replicating instances from a predefined image. They also provide application based autohealing health checks. If an application is not responding on a VM, the auto healer automatically recreates that VM for you. Regional MIGs let you spread app load across multiple zones. This replication protects against zonal failures. MIGs work with load balancing services to distribute traffic across all of the instances in the group. 

Compute Engine offers autoscaling to automatically add or remove VM instances from a managed instance group based on increases or decreases in load. Autoscaling lets your apps gracefully handle increases in traffic, and it reduces cost when the need for resources is lower. You define the autoscaling policy for automatic scaling based on the measured load, CPU utilization, requests per second or other metrics.

Active Assist’s new feature, predictive autoscaling, helps improve response times for your applications–When you enable predictive autoscaling, Compute Engine forecasts future load based on your Managed Instance Group’s (MIG) history and scales it out in advance of predicted load, so that new instances are ready to serve when the load arrives. Without predictive autoscaling, an autoscaler can only scale a group reactively, based on observed changes in load in real time. With predictive autoscaling enabled, the autoscaler works with real-time data as well as with historical data to cover both the current and forecasted load. That makes predictive autoscaling ideal for those apps with long initialization times and whose workloads vary predictably with daily or weekly cycles. For more information, see How predictive autoscaling works or check if predictive autoscaling is suitable for your workload, and to learn more about other intelligent features, check out Active Assist.

Pricing

You pay for what you use. But you can save cost by taking advantage of some discounts! Sustained use saving are automatic discounts applied for running instances for a significant portion of the month. If you know your usage upfront, you can take advantage of committed use discounts which can lead up to significant savings without any upfront cost. And by using short lived preemptive instances you can save up to 80%, they are great for batch jobs and fault tolerant workloads. You can also optimize resource utilization with automatic recommendations. For example if you are using a bigger instance for a workload that can run on a smaller instance you can save costs applying these recommendations.

Security

Compute Engine provides you default hardware security. Using Identity and Access Management (IAM) you just have to ensure that proper permissions are given to control access to your VM resources. All the other basic security principles apply, if the resources are not related and don’t require network communication amongst themselves, consider hosting them on different VPC networks. By default, users in a project can create persistent disks or copy images using any of the public images or any images that project members can access through IAM roles. You may want to restrict your project members so that they can create boot disks only from images that contain approved software that meet your policy or security requirements. You can define an organization policy that only allows Compute Engine VMs to be created from approved images. This can be done by using the Trusted Images Policy to enforce images that can be used in your organization. 

By default all VM families are Shielded VMs. Shielded VMs are virtual machine instances that are hardened with a set of easily configurable security features to ensure that when your VM boots, it’s running a verified bootloader and kernel — is the default for everyone using Compute Engine, at no additional charge. For more details on Shielded VMs refer to the documentation here.

For additional security, you also have the option to use Confidential VM to encrypt your data in use, while it’s being processed in Compute Engine. For more details on Confidential VM refer to the documentation here.

Use cases

There are many use cases Compute Engine can serve in addition to running websites and databases. You can also migrate your existing systems onto Google Cloud, with Migrate for Compute Engine, enabling you to run stateful workloads in the cloud within minutes rather than days or weeks. Windows, Oracle or VMware applications have solution sets enabling a smooth transition to Google Cloud. To run windows applications either bring your own license leveraging Sole-tenant nodes or using the included licenced images. 

Conclusion

Whatever your application use case may be, from legacy enterprise applications to digital native applications, Compute Engine’s families will fit it. For a more in-depth look into Compute Engine check out the documentation. 

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