Founded in 2006, PaGaLGuY started as a forum that enabled students, typically aged between 20 and 30 to discuss and seek advice on academic issues. By 2011, PaGaLGuY had increased its traffic to about 250,000 page views per month. The business is now one of India’s largest education networks and provides an app that users can download to their Android and iOS devices.

Over the past six years, PaGaLGuY has extended its service to include video advice from experts on education topics and grown the number of views of its pages to 1.5 million per day. As Head of Technology for the business, Sandeep Kalidindi has played a key role in ensuring PaGaLGuY is as engaging as possible to users. “Because the product is advertising based, the greater the user engagement, the greater the advertising revenue,” Kalidindi explains.

Google Cloud Platform Results

• Supported growth to 1.5 million page views per day and demand spikes that see requests increase from about 90 per second to about 1,200 per second
• Reduced API latency from about 1 second to about 40 milliseconds
• Reduced system administration time from three to four days per week to 30 minutes every two weeks

In 2015, PaGaLGuY’s senior management team decided to deliver an even more relevant experience for users of the education network. “The core thing we had to do was personalise the experience for each and every student that visited PaGaLGuY,” Kalidindi says. “So we had to capture each student’s data to customise what they see when they open the site.”

The business also found traffic to the network was straining its infrastructure. During demand peaks, created by exams involving as many as 5 million students, PaGaLGuY would be inaccessible for periods of 30 minutes to one hour. Furthermore, average API latency had climbed to an unacceptable 1 second, compromising performance.

PaGaLGuY needed to access extensive compute resources to undertake its planned change. Had the business relied on a physical technology architecture to undertake the transformation, it would have had to purchase capacity equivalent to 16 new servers. “There was no way with a small team we could grow to that extent in a short time,” Kalidindi says. “This was the right moment for us to explore cloud services.”

The business established two primary requirements the selected cloud service needed to meet. First, PaGaLGuY had to be able to scale the platform with costs rising only in proportion to the increase in resources consumed. Accordingly, the business would have to minimise the number of employees required to manage the cloud environment. Second, the platform had to give PaGaLGuY easy access to student data and the ability to undertake prompt, granular analysis.

PaGaLGuY reviewed available public cloud services and determined that Google Cloud Platform (GCP) was the best fit for its business. “Google Cloud Platform was considerably more mature than the alternatives, with a high degree of automation and a suite of managed services,” Kalidindi says. PaGaLGuY management then discussed with Google how to optimise cost, performance and availability of its personalised education network on GCP.

With assistance from Google and business transformation specialists Searce, PaGaLGuY was able to deliver the platform into production on GCP in 10 months. “Searce was very proactive in ensuring the environment met our needs and allowing us to gain priority access to Google services in development,” Kalidindi says. “Their team was integral to the success of the migration.”

PaGaLGuY has been running in production in GCP for two years. The education network’s GCP architecture comprises a scalable back-end built on Google App Engine; a managed environment for its containerised applications in Google Kubernetes Engine; messaging-oriented middleware through Google Cloud Pub/Sub; a relational database in Google Cloud SQL; a managed data analytics warehouse running in Google BigQuery; stream and batch data processing through Google Cloud Dataflow; and object storage in Google Cloud Storage.

PaGaLGuY has leveraged GCP services to break down its platform application from a monolithic build to a series of microservices running in Google App Engine that enable independent deployment cycles, minimise test and quality assurance overheads and provide clearer monitoring and logging.

Running on GCP has enabled PaGaLGuY to add new personalisation features and grow fourfold without having to add any new engineers or administrators to accommodate the increased traffic. The business has also used the platform to seamlessly collect and aggregate students’ data for analysis, reporting and delivering a more targeted user experience. Furthermore, PaGaLGuY has been able to provide its management team with direct access to Google BigQuery to scrutinise data rather than require them to wait at least a day to view reports created by the product or technology teams.

Support demand peaks of 1,200 requests per second

“Thanks to Google Cloud Platform, we can easily support demand peaks that see requests per second rise from an average 90 per second to about 1,200 per second for as long as 45 minutes,” Kalidindi says. Due to GCP’s scalability, PaGaLGuY can ensure its education network remains available and performance remains consistent during those periods.

Latency cut to 40 milliseconds

The business has also reduced average API latency from 1 second to about 40 milliseconds. Furthermore, using GCP has enabled PaGaLGuY to automate most of its processes and reduce system administration requirements from three to four days a week across its team members to about half an hour per week.

The performance of GCP has transformed PaGaLGuY’s culture and processes. “Once our team was exposed to Google Cloud Platform and understood the superiority of the platform, our mindset changed from ‘let us do everything on our own’ to ‘let us do what we do best’ and delegate the remainder,” Kalidindi says. The quality of the service provided by GCP means PaGaLGuY effectively considers the cloud provider as part of its team. “We are always eager to see what new services are being launched and are extremely excited about what Google Cloud Platform can provide as part of its roadmap.” he concludes.

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.

Google Cloud is built on world-class technical infrastructure that supports services that are loved and relied on by billions of people across the globe: Google Search, YouTube, Gmail, Google Maps and more. A core tenet at Google Cloud is to leverage Google’s experience building and operating highly available and highly reliable planetary-scale compute, storage and networking systems and data centers. 

Google takes a workload-optimized approach to building its infrastructure, employing a combination of dedicated hardware and software components to meet its workloads’ ever-growing demands. Underpinning this infrastructure is Titanium, a system of purpose-built, custom silicon and multiple tiers of scale-out offloads that together power improvements in the performance, reliability, and security of our customers’ workloads (for example, 25% faster block storage IOPS/instance compared to the other two leading hyperscalers). Unveiled today at Google Cloud Next, you’ll find Titanium technology in many of Google Cloud’s recent infrastructure offerings.

10x demands of tomorrow 

Meeting the growing performance, reliability, and security demands of both legacy and emerging workloads is a constant challenge for cloud infrastructure providers. And now, these demands are multiplying with the heightened adoption of generative AI across almost every industry. Meanwhile, the benefits of Moore’s law have been declining in recent years. We can’t rely on silicon advances alone to meet tomorrow’s needs.

As just one example, this chart shows the exponential computing demands of large language models.

It was clear to us a long time ago that we needed to rethink our infrastructure designs to meet these demands. This is why, for several years, we’ve adopted workload-optimization and intentional design as central principles for our infrastructure platform. We engineer golden paths from silicon to the customer workload, using a combination of purpose-built infrastructure, prescriptive architectures, and an open ecosystem to deliver workload-optimized infrastructure. 

Offloads play a pivotal role

Central to this strategy are offload technologies. Traditionally, the CPU wears many hats: It runs the hypervisor, the virtualization stack to enable your workloads, and manages storage and networking I/O; it’s responsible for security isolation for virtual interfaces and physical hardware, etc. In this model, customer workloads running on the CPU contend for resources with these platform tasks.

Offloads on dedicated hardware perform behind-the-scenes security, networking, and storage functions that were previously performed by the host CPU, allowing the CPU to focus on maximizing performance for customer workloads.

A recent example of an on-host offload or accelerator is the Infrastructure Processing Unit (IPU), a system-on-chip that we co-designed with Intel to enable better security isolation and performance on our 3rd gen compute instances. The IPU enables:

• Predictable and efficient compute
• Programmable packet processing for low latency, 200 Gbps networking with 3x the packets per second compared to our previous-generation compute instances
• In-transit encryption with the PSP protocol

Another important example of Google’s on-host hardware is Titan, a secure, low-power microcontroller that helps ensure that every machine in Google Cloud boots from a trusted state.

But we did not stop there. To meet tomorrow’s demands, we knew we needed to go past the performance that could be achieved using the host’s dedicated offload hardware.

A tiered system of offloads

A key component of Titanium is its modern offload architecture, which combines capabilities whose scale and performance are well-established within Google, as well as new capabilities tailored for cloud use cases. 

Just as modern workloads scale out horizontally in the cloud, with Titanium, we’ve extended the architecture to augment on-host offloads with an additional tier of scale-out offloads that run outside the host. This system of offloads is deployed fleet-wide and dynamically adjusts to changing workload needs to continually deliver the best performance.

Example 1: Block storage
Titanium scale-out offload enables Hyperdisk block storage to deliver stellar I/O performance. Hyperdisk’s Titanium offload on the host IPU works in tandem with the Titanium scale-out offload tier that distributes I/O across Google’s massive cluster-level filesystem, Colossus.

With traditional offload architectures, higher block storage IOPS requires purchasing larger compute instances. For example, you may need to deploy a data-intensive workload on a compute instance with many more vCPUs than the workload needs just to get sufficient storage performance. This tight coupling results in wasted resources and higher costs for customers. Further, even with large instances, storage performance in the cloud may be inadequate relative to what customers are used to with on-prem storage systems.     

With our new block storage, Hyperdisk powered by Titanium, we have decoupled compute-instance size from storage performance. Hyperdisk uses a tier of offloads in our cloud fabric to offload storage I/O from the customer hosts to achieve higher storage performance even with a general-purpose VM.   

In fact, today we are announcing that Titanium-powered C3 VMs with Hyperdisk Extreme now support 500K IOPS per compute instance in preview to meet the needs of the most demanding workloads. This is 25% faster IOPS/instance compared to the other two leading hyperscalers, courtesy of the Titanium system. 

Example 2: Network routing
Virtual network routing is another example of using a second tier of scale-out offloads (“hoverboards”). With Titanium, Google’s Andromeda virtual networking stack on the IPU offload device sends all packets for which it does not have a route to Hoverboard gateways, which have forwarding information for all virtual networks. Hoverboards are standalone software switches that act as default routers for some flows.

Unlike the traditional gateway model, the control plane dynamically detects flows that exceed a specified usage threshold and programs them to be direct host-to-host flows, bypassing the hoverboards allowing hoverboards to focus on the long tail of less frequent flows. Typically, only a small subset of possible VM pairs in a network communicate with one another, so the VMs only have to store and process a small fraction of the usual network configuration on an individual VM host, improving per-server memory utilization and control-plane CPU scalability.

Titanium already powers your workloads

The Titanium journey began years ago with the component technologies described above. Many of our products already benefit from this architecture, and the newest elements of this architecture are now available with our 3rd gen Compute Engine instances such as C3 and the new Hyperdisk block storage. 

Going forward, look for the Titanium architecture to underpin all future generations of our infrastructure offerings, in the process enabling new classes of infrastructure capabilities that move well beyond the confines of a single server.

While some traditional financial services companies have more slowly transitioned to the cloud, capital markets firms have embraced cloud computing across their entire value chains — front-, middle-, and back-office. We wanted to understand the dynamics behind this rapid adoption, the most common use cases, and the types of technology most in use, particularly as it relates to market data. Google Cloud commissioned Coalition Greenwich to survey 102 institutional capital markets professionals — at exchanges, trading systems, data aggregators, data producers, asset managers, hedge funds, and investment banks — in the United States, Canada, France, Germany, Italy, the Netherlands, Switzerland, and the United Kingdom. 

Our research found that while there are many drivers, demand for easier accessibility is fueling widespread adoption of cloud-based market data services, and associated trading infrastructures, across the buy side and sell side. In fact, 68% of sell-side and buy-side users find it critical for market data providers to offer public cloud-based data services. At the same time, exchanges, market data providers, aggregators, and trading systems are embracing the cloud as a delivery model by offering access to data directly via their own cloud services, APIs or partners.

Here were five noteworthy takeaways from the study: 

1. Cloud services are becoming ubiquitous for data delivery. Today, the cloud is pervasive, with 93% of exchanges, trading systems and data providers offering cloud-based data and services, according to surveyed executives. Moreover, 100% of those surveyed intend to offer new cloud-based services, such as derived data, in the next 12 months.

2. Commercial and investment banks are offering additional connectivity, real-time data feeds, and trading applications delivered via the cloud,demonstrating that it’s not only exchanges, trading systems, and data providers that are moving rapidly to the cloud. Internal use cases abound as well, with 67% of those surveyed consuming cloud-deployed market data, primarily for data analytics. 88% of surveyed sell-side firms intend to consume cloud-based market data services, with digital transformation, data science and quant research as the top use cases.

3. Buy side firms will consume even more cloud-deployed data. Today, 90% of surveyed buy-side firms are consuming cloud-deployed market data, mostly for portfolio management. 70% of buy-side firms intend to consume more public cloud-based market data services in the next 12 months, adding services such as compliance and regulatory reporting.

4. AI/ML, powered by cloud, is moving out of the pilot phase and into mainstream use. Today, 50% of exchanges, trading systems, and data providers are offering data products or services powered by AI/ML, and of those, 42% intend to offer AI-powered trade execution and trading analytics services in the next 12 months. Within commercial and investment banks, 55% said they are currently using AI/ML in the cloud, and while that was true for only 14% of overall buy-side respondents, 44% of large buy-side respondents are using it.

5. Exchanges, trading systems, and data providers are prioritizing public cloud for internal insights. 71% of these firms are using the public cloud, mostly for data transmission, processing, analysis, and long-term data storage. Over the next 12 months, 33% of new public cloud workloads will focus on data mining, data insights and advanced analytics, while 28% of new AI/ML tooling and infrastructure investments will focus on faster analytics and risk reviews, and 27% on data quality maintenance.

“We see new, dramatic shifts on the adoption of cloud across market data,” said David Easthope, Senior Analyst for Coalition Greenwich. “And we expect further proliferation of cloud-based services and greater consumption across the trading and investing lifecycle.”

Conclusions and future predictions

Based on the survey results, Coalition Greenwich predicts five following trends over the next 12 months:

1. Exchanges and trading systems will continue to launch a wide array of new cloud-based and possibly cloud exclusive data services across derived data, end of day data, reference data and pricing data.
2. Data providers will launch new data products such as pre-trade analytics powered by AI/ML in the cloud.
3. Commercial and investment banks will offer additional connectivity, real-time data feeds, and trading applications delivered via the cloud.
4. Buy-side firms will consume even more cloud-deployed data, including real-time market data, portfolio management data, and risk analytics.
5. Exchanges, trading systems and data providers will explore proof-of-concepts around core systems on the cloud. Improvements to AI/ML tooling or infrastructure will ramp up as firms seek more rapid responses to risk initiatives.

To learn more about these findings, download our two full reports, The Future of market data: Distribution and consumption through cloud and AI and Exchanges and data providers: Prioritizing the cloud and AI for internal insights or our short infographic.

Research methodology

The survey was conducted online by Coalition Greenwich on behalf of Google Cloud from March 2021 to April 2021 among 102 executives in North America (n=82), EMEA (n=17) and other (n=3) who are employed full-time and who are participants or influencers in decisions around cloud and/or senior management with a role at a company which is an institutional asset manager, hedge fund, alternative investment manager, exchange and/or trading system, information provider, information aggregator, or other asset manager/asset owner. The survey included wide perspectives from a range of firm size and asset class focus, including equity, fixed income, FX, commodities, multi-asset, and other asset classes.

Foot Notes

1.  We defined market data as direct feeds, consolidated feeds, terminal and desktop products, security and reference data, pricing data, historical data, alternative data, and index data.