Editor’s note: Engineers from SoFi Stadium, Google Cloud, and Deloitte built a fan-ready Personal Concierge app that uses advanced analytics to tailor game-day experiences for every visitor. SoFi’s 10-year partnership with Google Cloud not only pumps up the fans. Advanced analytics also enable stadium employees to securely store, analyze, and action analytics data, and seamlessly collaborate through tools and services included in Google Cloud Workspace.

SoFi Stadium, an industry-leading, outdoor-indoor events facility and home of the Los Angeles Rams and Los Angeles Chargers, is the centerpiece of Hollywood Park, a near 300-acre sports and entertainment destination. With the capacity to host up to 100,000 spectators across a range of luxury and premium seating options, the 3.1 million square-foot SoFi Stadium is the largest in the NFL. Completed in September 2020, it has already secured a global reputation—scheduled to host Super Bowl LVI in 2022, the College Football National Championship Game in 2023, and the Opening and Closing Ceremonies of the Olympic Games in 2028. Under the same roof canopy as the stadium is the 2.5-acre American Airlines Plaza and the 6,000-seat performance venue, YouTube Theater. 

Through a long-term partnership with Google Cloud, SoFi Stadium is personalizing fan experiences, enabling a more fulfilling, collaborative, and productive work experience for employees, and delivering game-changing technology to the event space.

Personalizing the experience for every fan 

Sports fans are known for their fierce loyalty and camaraderie. While they come to game day ready to cheer with fellow fans, each spectator has a different vision for what will make for a memorable event. SoFi Stadium is helping customers choose their game day flow by extending a personalized, streamlined experience to every stadium goer through several new digital innovations powered by Google Cloud.

First there’s the Personal Concierge App. Powered by Google Cloud and built by Deloitte, this personalized navigation and security tool may be the game day MVP that sports fans have always dreamed of. Hours of precious viewing time can be lost navigating a stadium campus for seating, parking, food, access, and amenities. This application enables fans to quickly locate available parking locations, select the ideal transportation for game day through real-time Google Maps routing, and discover SoFi Stadium events all from their mobile device. Event-goers can also receive digital credentials through the app, enabling them to bypass lines and accelerate their access to luxury viewing suites. 

The advanced data analytics accrued from multiple sources and processed through Google Cloud products such as Cloud Scheduler, App Engine, Dataflow, BigQuery, Looker, Firebase, and more, enable SoFi Stadium to anticipate spectator tastes. These Google Cloud tools can remember preferences and provide attendees with real-time personal recommendations around the clock. 

Performing at the top of their game with Workspace

Advanced data analytics enhance employee experiences, too. Credentialed team members can securely store and analyze data from multiple sources, create operational dashboards with real-time data, track key performance indicators after events, and better forecast attendance, revenue by product, and other important business metrics.

In addition to deploying a number of Google Cloud solutions including Compute Engine, Kubernetes Engine, Cloud CDN, BigQuery, and others to power the Personal Concierge App, SoFi Stadium is empowering its employees by fully migrating to Google Cloud’s Workspace. Workspace streamlines the stadium’s business operations by fostering fast and seamless access to collaboration and performance tools. Built-in software—including Gmail for business, Docs, Drive, Calendar, and Meet—will help employees work smarter and meet securely from anywhere. 

“We couldn’t have picked a better partner to help manage our technology needs,” said Skarpi Hedinsson, Chief Technology Officer, SoFi Stadium and Hollywood Park. “Incorporating Google’s solutions into critical areas of our business ensures we can usher in a new era of innovation in sports and entertainment, raising the bar on what the ultimate fan experience looks like globally.”

Making the big-game even bigger

As Hollywood Park combines the expertise of its team and Google Cloud engineers to power the future of sports and live events, SoFi Stadium prepares to welcome a product of this collaboration with the Infinity Screen by Samsung, the largest video board in sports. With 80 million pixels, weighing 2.2 million pounds, and boasting 120-yards in length, the Infinity Screen by Samsung leverages Google Cloud-based media workflows and optimization technology to power its game-changing screen. 

Through its partnership with Google Cloud, SoFi Stadium is actively developing new media streaming and high-speed wireless solutions. In addition, SoFi Stadium has the world’s first 4K/HDR live production facility. These digital innovations are powering a new standard for sports fans and opening a path to redefine what can take game day from good to great.

Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.

Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.

Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.

With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE. 

“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.” 

Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem. 

Windows Server running on-prem with Anthos 

The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.

Get started today

When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.

Editor’s note: We’re hearing here how martech provider Wunderkind easily met the scaling demands of their growing customer base on multiple use cases with Cloud Bigtable and other Google Cloud data solutions.

Wunderkind is a performance marketing channel and we mostly have two kinds of customers: online retailers, and publishers like Gizmodo Media Group, Reader’s Digest, The New York Post and more. We help  retailers boost their e-commerce revenue through real-time messaging solutions designed for email, SMS, onsite, and advertising. Brands want to provide a one-to-one experience to more of their customers, and we use our extensive history with best practices in email marketing and technology to help brands reach more customers through targeted messaging and personalized shopping experiences. With  publishers, it’s a different value proposition, we use the same platform to provide a non disruptive and personalized ad experience for their website. For example, if you are on their site and then you left, we might show an ad tailored to you when you come back later – depending on the campaign. 

After running into limitations with our legacy database system, we turned to Cloud Bigtable and Google Cloud, which helped us be more flexible and easily scale for high traffic demand – which can be a stable 40,000 requests per second, and meet the needs of our growing number of data use cases. 

Three different databases power our core product

In our core offering, companies send us user events from their websites. We store these events and later decide (using our secret sauce) if and how to reach out to those users on behalf of our customers. Because many of our customers are retailers, Black Friday and Cyber Monday are big traffic days for us as. On such days, we can get 31 billion events, sometimes as many as  200K events per second. We show 1.6 billion impressions that have seen close to 1 billion pageviews. And at the end of all this, we securely send about 100 million emails. We noticed the same thing for election time; traffic reached the same high volume. We need scalable solutions to support this level of traffic as well as the elasticity to let us pay only for what we use, and that’s where Google Cloud comes in.

So how does this work? Our externally facing APIs, which are running on Google Kubernetes Engine, receive those user events—up to hundreds of thousand per second. All the components in our architecture need to be able to handle this demand. So from our APIs, those events go to Pub/Sub, Dataflow and from there they are written to Bigtable and BigQuery, Google Cloud’s serverless, and highly scalable data warehouse. This business user activity data underpins almost all our products. Events can be things like product views or additions to shopping carts. When we store this data in Bigtable, we use a combination of email address and the customer ID as the Bigtable key and we record the event details in that record. 

What do we do with this information next? It’s important to mention that we also mark the last time we received an event about a user in Memorystore for Redis, Google Cloud’s fully managed Redis service. This is important because we have another service that is periodically checking Memorystore for users that have not been active for a campaign-specific period of time (it can be 30 minutes, for example), then deciding whether to reach out to them.

How we decide when we reach out is an intelligent part of our product offering, based on the channel, message, product, etc. When we do reach out, we use Memorystore for Redis as a rate limiter or token bucket. In order not to overwhelm the email or texting providers we send API requests to, we throttle those requests using Memorystore. (We prefer to preemptively throttle the outgoing API requests as opposed to handling errors later.)

When we do reach out, often we will need details for a specific product—let’s say if the website belongs to a retailer. We usually get that information from the retailer through various channels and we store product information in Cloud SQL for MySQL. We pull that information when we need to send an email with product information, and we use Memorystore for Redis to cache that information, since many of the products are repeatedly called. Our Cloud SQL instance has 16 vCPUs, 60GBs of memory and 0.5TB of disk space and when we perform those product information updates, we have about a thousand write transactions per second. We are also in the process of migrating some tables from a self managed MySQL instance, and we keep those tables synchronized with Cloud SQL using Datastream. 

Our user history database was originally stored in AWS DynamoDB, but we were running into problems with how they structured the data, and we’d often get hot shards but with no way to determine how or why. That led to our decision to migrate to Bigtable. We set up the migration first by writing the data to two locations from Pub/Sub, performed some backfill of data until that was up and running, and then started working on the reading. We performed this over a few short months, then switched everything to Bigtable. 

So, as mentioned, we are using Bigtable for multiple databases. The instance that stores our user events has about 30 TB with about 50 nodes.

Profile management

A second use case for Bigtable is for user profile management, where we track, for example, user attributes based on subscription activity, whether they’ve opted in or out of various lists, and where we apply list-specific rules that determine which targeted emails we send out to users. 

Our very own URL shortener

Our third use case for Bigtable is our URL shortener. When our customers build out campaigns and choose a URL, we append tracking information to the query string of the URLs and they become long. Many times, we are sending them via SMS texts, so the URLs need to be short. We originally used an external solution, but made the determination that they couldn’t support our future demands. Our calls tend to be very bursty in nature, and we needed to plan for a future state of supporting higher throughput. We use a separate table in Bigtable for this shortened URL. We generate the short slug that is 62 bit-encoded and use it as the rowkey. We use the long slug as a Protobuf-encoded data structure in one of the row cells and we also have a cell for counting how many times it was used. We use Bigtable’s atomic increment to increase that counter to track how many times the short slug was used. 

When the user receives a text message on their phone, they click the short URL, which goes through to us, and we expand it to the long slug (from Bigtable) and redirect them to the appropriate site location. Obviously, for the URL shortener use case, we need to make the conversion very quickly. Bigtable’s low latency helps us meet that demand and we can scale it up to meet higher throughput demands.

Meeting the future with Google Cloud

Our business has grown considerably, and as we keep signing up new clients, we need to scale up accordingly, and Bigtable has met our scaling demands easily. With Bigtable and other Google Cloud products powering our data architecture, we’ve met the demand of incredibly high traffic days in the last year, including Black Friday and Cyber Monday. Traffic for these events went much higher than expected, and Bigtable was there, helping us easily scale on demand. 

We are working on leveraging a more cloud native approach and using Google Cloud managed services like GKE, Dataflow, pub/sub, Cloud SQL , Memorystore, BigQuery and more. Google has those 1st party products and we don’t see the value in rolling out or self managing such solutions ourselves..

Thanks to Google Cloud, we now have reliable and flexible data solutions that will help us meet the needs of our growing customer base, and delight their users with fast, responsive, personalized shopping messaging and experiences. 

Learn more about Wunderkind and Cloud Bigtable. Or check out our recent blog exploring the differences between Bigtable and BigQuery.

Paper currency — which started gaining prominence in the 1600s — changed the face of global economics and ushered in a new era of international monetary regulation. The primary reason currency created such disruption was its ability to standardize the “medium of exchange”. APIs created a similar effect in the world of technology and digitalization by creating a standardized, reusable, and secure way to exchange information.

Modern web APIs took shape in the early 2000s and played a key role in “.com”mercializing every business. APIs started as a connective tissue primarily relegated to a technical context and quickly evolved into a gateway to new business models, revenue streams, and ecosystems. In 2017, McKinsey estimated a total of $1 Trillion in profit could be up for grabs in the API economy. And in 2022, GGV Capital created an index of API-first startups — a generation of stylistically divergent SaaS companies with leaner operating cost structures and organic usage growth. Just as currency is going through an evolution from banknotes to digital wallets, the world of API management is also on the brink of change.

With more than 15 years of experience managing APIs at Google-scale, we’ve got a unique vantage point from which to observe that change. In today’s post, we will spotlight seven API management use cases that we see growing in prominence — and how you can take advantage of these trends to future-proof your architecture.

1 “Shift left” in API security

As a gateway to a wealth of information, APIs have also quickly become the primary attack vector in security incidents. When we surveyed 500 technology leaders, we learned that more than 50% of organizations experienced an API security incident in the last 12 months. Adding to the increasing magnitude of attacks, there are an increasing number of vectors for potential API security incidents like misconfigurations, outdated APIs/data/components, and bots/spam/abuse.

These security issues aren’t just in production APIs, but at every stage in the API lifecycle. Notably, we found that 67% of the issues are discovered during testing as part of the release management process. This trend ushers in the need for forward-thinking organizations to “shift left with security” — moving controls earlier into the production workflow — by bringing security teams and API teams closer. To stay ahead of security threats, many organizations are actively looking for solutions that allow them to be proactive while minimizing the burden on their security teams. According to our research, integrating capabilities that proactively identify security threats (60%) is top of mind for most IT leaders for the next year.

2 “Knowledge graph” for your enterprise APIs

It comes as no surprise that every organization is relying on APIs to expand and even ground their digital ecosystem — a network of partners, developers, and customers facilitated by modern, cloud-first technologies. There is a growing magnitude and variety of middleware assets, contributing to the growth of IT complexity.

As the number of APIs continues to increase, there is a need to simplify consumption for internal and external developers. Even the most objectively useful APIs remain unseen by most of the organization. In turn this results in redundant code, reduced developer productivity, or worse — turned into a potential security attack vector. This complexity is shifting focus towards consolidation of all middleware assets, growing adoption, and improving education (see below) to improve developer efficiency and de-mystify the IT complexity.

This sprawl is a growing problem in the world of APIs, but it has a lot in common with an age-old phenomenon in the world of web pages and content—search. Google was born out of this problem to help organize the world’s information. Similar to Google’s knowledge graph for web pages, there is a need to index, organize, and instantly present API information for developers that need it. Although it is an emerging practice, we see an increasing number of digital leaders and security teams in larger organizations with mature API programs invest in solutions that help consolidate all APIs, organize their information, and manage their lifecycle.

3 The imminent need for “omni” control planes

APIs have taken on such a vital role in the modern application stack that they have slowly become the neural links across the entire enterprise architecture — bridging legacy and modern applications, shifting architectures towards microservices, and enabling operations across heterogeneous environments. To support all these technological decisions without sacrificing speed, organizations adopted multiple API gateways and fragmented API management solutions. However, this led to a lack of universal visibility, consistent governance, comprehensive security, and meaningful analytics across ALL the enterprise APIs (not just the ones within the confines of a given API management solution). And it increases the maintenance costs — fundamentally undercutting the value of APIs. With this evolution there is a growing need for an omni control plane — analogous to the brain in a human body — across all enterprise APIs.

4 API governance rising through the priority ranks

Despite the clear need for governance, there is still no unified understanding on a good (or right) approach to API governance. With the rapid adoption of APIs without appropriate standardization or quality standards, API governance is top of mind for IT leaders, again.

According to our research, 45% of IT leaders identified API governance as a critical component of their API program. The top three components of API security, performance analytics, and governance demonstrate the critical need for visibility, quality, and security across all APIs.

As digital consumers, we have seen this phenomenon across many industries and digital products. For example AirBnB disrupted the short-term rental market by providing standardized listings, detailed information, and high-resolution photos. In fact, the same governance phenomenon is ubiquitous in the world of e-commerce where there is a clear correlation between a high-quality website or product listing and increasing sales.

The same analogy holds true in the world of APIs, as ~90% of developers use APIs in their work there is a direct correlation between the use of APIs and developer productivity. Digital officers and CIOs need to add appropriate governance controls to standardize API design and improve reuse without adding friction to development timelines.

5 Evolution of design patterns with multiple API gateways

Adoption of new API architectural styles and microservices increased the complexity of the modern application stack. Our research found that 54% of organizations use a service mesh and API management in conjunction today to support the API gateway design pattern. In parallel, there is broad adoption of new protocols like GraphQL or AsyncAPI, outpacing the innovation in API gateways. For example, in a recent survey from DZone found that GraphQL accounted for 22.7% of application integrations.

In response to this challenge IT teams are adopting multiple API gateways — by design — which is creating the need for complex communication patterns for future scalability. But the existing design patterns were mostly sufficient when client applications used homogeneous API protocols (Ex: REST). Although patterns like Backend For FrontEnd (BFF) intended to provide specific API interactions that are relevant on a per-client basis, they still did not account for complexities from multiple gateways and protocols. In response to the adoption of new protocols, there is a need to evolve the existing BFF pattern to account for multiple API gateways and protocols.

6 Driving green value chains with digital twins

A digital twin is an effectively indistinguishable virtual representation of a physical object, system, or a process. For example the digital twin of a wind turbine (the object being studied) might be used to capture data like performance, rpm (revolutions per minute), or output captured by various sensors outfitted on the turbine. Digital twin adoption is growing and McKinsey estimates investments in digital twins will reach $58 billion by 2026 with a 58% CAGR. Every digital twin uses APIs to monitor, engage, and possibly control the physical asset. For example, Google created the Digital Buildings project — an open source, Apache-licensed effort to manage applications and analyses between a large heterogeneous portfolio of buildings.

Sustainability is one of the driving forces behind the increased use of digital twins. As the need to reach net zero emissions accelerates, many organizations are tying performance (and in some cases even executive pay) to environmental, social, and governance goals. APIs help connect the dots between digital twins and sustainability. For example, an organization operating a manufacturing process could build a digital twin with APIs to collect behavioral data from sensors, monitoring systems, or other sources — which can eventually be integrated into the organization’s digital platform or applications. These digital twins could be used to analyze and optimize the use of materials and energy, to minimize waste and emissions. Additionally, digital twins could be used to monitor and analyze the performance of systems over time, to identify opportunities for continuous improvement.

Overall, APIs play a valuable role in supporting sustainability efforts by enabling digital twins, effectively driving more efficient operation of systems, and providing insights to improve environmental impact. For further examples, check out this video about driving a green value chain with APIs.

7 Commercializing access to data products

The growing use of data-rich services (like IoT, ML models, remote access services, and web scraping, etc.) coupled with massive ingestion of data everyday is creating massive growth in data delivery paradigms like data lakehouses, data marketplaces, and data streaming systems (global data marketplaces alone are poised to reach $3.5 billion by 2028). Unfortunately, most of these systems are fragmented with almost no relationship or interoperability.

APIs are filling this critical gap for organizations in two critical ways. First, APIs are providing standard and easy access to systems like data lakehouses or analytics hubs. Second, APIs are a key enabler of data products (digital products or services built using data as a core value proposition), a core component of any data sharing system. APIs provide a standardized way for different applications to interact with the data product. For example, an API could be used to allow a mobile app to access data from a weather forecast or a recommendation engine data product. Beyond data products, APIs also provide easy and standardized access various data management platform 

APIs continue to play a critical role in every application, experience, and ecosystem. Robust API strategies help organizations adapt to any architecture, business model, or environment in the face of changing technology landscape. Learn more about how Apigee is driving innovation and helping companies future proof their architectures to stay ahead of the top API trends.