3 Ways To Reduce App Downtime With Google Cloud’s API Monitoring Tools
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How many times have you closed an application when you encounter the “spinning wheel of death” (a melodramatic way of saying an application that is taking too long to load)? In today’s digital economy where many organizations rely on applications as a primary source of revenue, that spinning wheel of death (or poor application performance) can mean lost users or revenue. And just about every modern application relies on APIs as the nervous system across distributed systems, third-party services and microservice architectures. While meeting the need for rapid release cycles and frequent API updates, it is also imperative for IT teams to ensure that your APIs are meeting SLOs, performance requirements, and proactively mitigating issues.
Why are synthetic monitoring tools not enough?
When thousands or even millions of users are making multiple requests to your APIs, just relying on synthetic monitoring tools (that rely on sampling or limited API availability information) is not enough for precision diagnoses or useful forensics. At the same time, monitoring every single aspect just increases your overhead and mean time to diagnosis. Apropos, API monitoring has become absolutely critical — and a fusion of art and science — for operations teams to make sure all APIs are running and performing as intended. If you are worried about monitoring blind spots or overheads, let us look at 3 key practices you can follow to stop dreading your sev1 alerts.
#1 Prioritize alerts for critical events requiring immediate investigation
- Ask any engineer on-call for a critical service and they will tell you about the overheads created by incorrectly prioritized alerts. For example let us imagine a distributed application with 20 APIs. Even if you set up basic alert monitors across latency, error, and traffic for these APIs, you end up monitoring and maintaining 60 alert definitions – which is a lot. To balance avoiding monitoring blindspots and alert fatigue at the same time, operations teams must develop a clear understanding of all events and prioritize configuring alerts for events supporting critical traffic.
- Consider quality over quantity while defining new alert conditions where each new condition is urgent, actionable, and actively or imminently user-visible. Every alert condition created should also contain intelligence that requires active engagement from a user as opposed to a mere robotic response. Apigee’s API monitoring allows creation of alert conditions based on metrics or logs while providing actionable information (Ex: status code, rate etc.,) and playbooks for diagnosi
- In today’s multilayered systems, one team’s symptom (“what’s broken?”) is another downstream system’s cause (“Why?”). Even if some events are not suitable for actionable alerts, a failure needs to create an informational broadcast to a downstream system to mitigate the impact of the upstream dependency. In such cases, investing in automating alerts, grouping multiple incidents in notification channels, and incident tracking. For example, Apigee lets you integrate and group your alert notifications in channels like Slack, Pagerduty, webhooks etc.,
- Modern production systems are ever-evolving where an alert that’s currently rare might become frequent and automatable. Analogous to ticket backlog grooming, alert policies need to be reviewed periodically to make sure new conditions are identified and existing alerts are refined with new thresholds, prioritization and correlation. Controls like Advanced API Ops leverage AI and ML to detect anomalous traffic differentiated from random fluctuations to help define accurate alert definitions
Check out some examples of alerts here
#2 Isolate problem areas quickly with dashboards
Google’s Site Reliability Engineering book presents the case for efficient diagnosis by building dashboards that answer basic questions about every service, normally including some form of the four golden signals — latency, traffic, errors and saturation. But at the same time, capturing just these golden metrics at different levels of granularity can quickly add up. Like all software systems, monitoring can become an endless pit of complexity, complicated to change and a burden to maintain. In the same book, the most effective direction to create a well-functioning standalone system is to collect and aggregate basic metrics, paired with alerting and dashboards.
If you are running a large scale API program with a dedicated team to monitor your APIs, you can leverage the out-of-the-box monitoring dashboards in your API Management solution (like Apigee’s API monitoring) to gather real-time insights into your API performance, availability, latency, and errors. In other cases, you can use solutions like Cloud Monitoring that can provide visibility across your full application stack where individual metrics, events, and metadata can be visualized in a rich query language for rapid analysis. Leveraging a single system for your application stack provides observability in context and can reduce your time spent navigating between systems (Apigee customers can use Cloud Monitoring by default or integrate with other systems using Cloud Monitoring API)
Even after you collect and aggregate the metrics, it is important to have impactful data visualizations to quickly understand the issue and identify correlations during diagnosis. In data visualizations as well, focusing on too many dashboards creates a steep learning curve and increases mean time for every diagnosis. For example, Apigee API Monitoring provides the following visualizations as a standard to balance simplicity and efficiency:
- Timeline view to diagnose traffic (in 1 min intervals), error rates (4xx and 5xx across traffic) and latencies (50th, 90th, 95th and 99th percentiles)
- Pivot tables of metrics and attributes for all API traffic, to help compare activity across different metrics
- Treemaps of recent API traffic by proxy to get a snapshot of incidents, error rates and latencies
#3 Incorporate distributed tracing into your end to end observability strategy
Modern application development accelerated the adoption of technologies and practices like cloud, containers, APIs, microservice architectures, DevOps, SRE etc. While this increases release velocity, it also introduces complexity and more points of failure in an application stack. For example, a slow response to a customer request spans across multiple micro services owned (and monitored) by various teams who might not observe any individual performance issues. Without an end-to-end contextual view of a request, it is nearly impossible to isolate the point of high latency.
In such cases, distributed tracing is the best way for DevOps, Operations and SREs to get answers to questions such as service health, root cause of defects, or performance bottlenecks in a distributed system. Organizations should invest in instrumenting their distributed applications using open source standards such as OpenCensus and Zipkin. Using tools like Cloud Trace with a broad platform, language and environment support can help easily ingest data from any source — open instrumentation or proprietary agents.
While distributed tracing helps in narrowing the issue to a given service, in some cases you might need further context to pinpoint the root cause. For example: Even if you have isolated the source of a performance issue to an API proxy, it is still a tedious process to identify the right bottleneck among multiple policies being executed. Tools like Apigee Debug enable you to zoom into an API proxy flow and probe the details of each step to see internal details like policy executions, performance issues, and routing etc.,
As soon as a request starts to span across a handful of microservices, tools like distributed tracing and Debug will become crucial elements of your monitoring strategy. When every service in a distributed system emits a trace, the amount of data can quickly become overwhelming leading to the classic “needle in the haystack” problem. In such scenarios, it becomes vital to ask the right questions and choose between head-based sampling (randomly selecting which traces will be sampled for analysis) and tail-based sampling (observe all trace information and sample the traces with unusual latency or errors) based on application complexity
Implement effective API monitoring in Apigee
Apigee’s API monitoring (based on metrics exposed by the internals of the system) capabilities work with your existing monitoring infrastructure to help reduce mean time to diagnosis and increase application resiliency. Specifically, operations teams can leverage
- Monitoring dashboards to gain in-depth insights into API availability and performance metrics.
- Debug to precisely diagnose with deeper insights into an API proxy flow without toggling multiple tools.
- Alerts and notifications to gather contextual Insights, facilitate customization, and grouping with first class integrations to various tools (like Slack, PagerDuty, email and support for webhooks).
- Best of Google technologies such as Data Flow, Pub/Sub, Stackdriver, Bigtable and BigQuery to handle massive volumes and complex metrics at scale
Using Apigee’s API monitoring will help you maintain high application resiliency with comprehensive controls to reduce mean time to diagnosis and resolution. Get started with Apigee today or explore Apigee’s API monitoring for free here. Check out our documentation for additional information on API monitoring.
Vodafone Leverages Google Cloud to Aid COVID-19 Frontline with Anonymized Insights on Population Mobility
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Editor’s note: When Europe’s largest mobile communications company, Vodafone, was asked by the European Commission to help understand population movement across the European Union and the UK to help fight COVID-19, it was able to provide anonymized mobile network-based insights to answer the call. Here’s how Vodafone, with the support of Google Cloud, rapidly mobilized the COVID-19 frontline, while respecting its customers’ privacy.
With the emergence of COVID-19 in early 2020, the European Commission—the executive branch of the European Union (EU)—knew that technology would be instrumental in its fight to control the pandemic. With various lockdowns imposed across its member states, the Commission was keen to predict and prevent the spread of COVID-19 and to manage the related social, political and financial impacts.
Mobile network data helps track COVID-19 across the EU
Mobile networks produce location data, which can be turned into useful anonymous insights to understand population movement within a geographic area. The European Commission, working with mobile industry association GSMA (Groupe Speciale Mobile Association), asked Europe’s major mobile phone operators for help in producing insights to support the fight against COVID-19. As the largest mobile network operator within the EU, Vodafone saw this as a critical opportunity to participate.
Vodafone had previous experience of using mobile network data to support pandemic research. For example, in 2019, Vodafone provided mobility pattern analysis to help track the spread of Malaria in Mozambique. And, during the early stages of the COVID-19 pandemic (prior to working with the European Commission), Vodafone assisted the Italian and Spanish governments in understanding their citizens’ mobility patterns. Vodafone had also previously offered anonymized and aggregated population mobility insights to support public transport and tourism authorities and retail organizations in a number of countries. Consequently, Vodafone was perfectly placed to play a greater role in supporting the European Commission’s response to the pandemic.
When asked to assist the European Commission, Vodafone first considered how it could safely share its data with the governing body without providing details on the individual movements of its customers. It realized it could achieve this through an elaborate set of anonymization and aggregation techniques. Insights are aggregated from a minimum of 50 users and Vodafone only shared these anonymous insights and never the actual raw data with the Commission. As specified by the EU, these insights are then presented onto a large geographical region, typically a city or a county with thousands of people living in that area.
These insights illustrate how people move, helping to determine how lockdowns and self-isolation measures were impacting behaviors.
Using Google Cloud to collate and store population mobility data
In April 2020, Vodafone began migrating its operations, including its mobile data, to Google Cloud on servers in Europe and the UK with elaborate security safeguards, including encryption, building on a previous partnership.
With the data residing in EU and UK data centers and not the United States, Vodafone could then retrieve anonymous insights from Google Cloud Storage instantaneously. Before supplying any information to the European Commission, however, Vodafone used Dataflow to validate the data and run a series of tests to ensure the database had accurate data, before ingesting and archiving the relevant metrics. For instant access, the data was then made available to the European Commission using a Redis database on Google Kubernetes Engine.
To ensure aggregate Vodafone customer data was always safe, secure, and anonymous, all entry points to the front-end were protected behind Google Cloud Armor, where only specific IP addresses were allowed. Using these tools, seamless data pipelines fed in predefined key performance indicators from each specified European market. While data quality measures ensured the definitions for metrics across markets were consistent and could be accurately compared.
The architecture (pictured below) shows how Vodafone integrated and anonymized its data on Google Cloud.
Live interactive dashboard shows population mobility in real-time
With its data integrated on Google Cloud, Vodafone created a live, interactive dashboard to track mobility patterns and share relevant information with the European Commission in real-time.
The European Commission Joint Research Center (JRC) was able to gather valuable information from these insights, which enabled them to see where population mobility was aiding the spread of the disease, when cross-referenced with health data. It could also assess the implications of lockdowns on different populations and forecast cross-country spreading.
Mobile data aids disease modeling for multiple stakeholders
The Vodafone data became instrumental in modeling the likely course of the disease too. For example, the University of Southampton in the UK used it to predict the outcome of different coordinated COVID-19 exit strategies across Europe. This research was published in Science Magazine in September 2020.
The Vodafone data dashboard continues to be used by individual governments, NGOs and organizations to further investigate the impacts of the pandemic and to measure the effectiveness of response strategies alongside the rollout of vaccination programs. The project also helped Vodafone win a DataIQ award for most effective stakeholder engagement.
Using the learnings from this project, Vodafone has been able to adapt its own B2B solution, called Vodafone Analytics, by adaptIng and migrating the code to work in Google Cloud Platform. This solution has been rolled out across Germany, Greece, Portugal and South Africa, and new countries are being onboarded every day. Vodafone Analytics already has more than 100 customers leveraging it for a variety of use cases—Italian fashion retailer OVS, uses it for its smart retail operation, while global real estate company, JLL, uses it to understand the footfall passing through its properties.
Working together, Vodafone and Google Cloud continue to help a range of organizations, governments, and NGOs navigate through the ongoing pandemic, optimize their operations, and help the greater good, without infringing individuals’ fundamental rights to privacy.
To learn more about Google Cloud and Vodafone, watch our full interview here.
Headless e-Commerce is the Next Big Thing in Retail
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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!
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Even before the current crisis, IT organizations saw pressure to be more agile and innovative. Customer demographics and expectations are changing. Competition is emerging faster and from unexpected sources. Business models are being reinvented. Digital technology was at the heart of many of these challenges, and its adoption was key to every company’s response.
As a result, CIOs face a series of urgent challenges:
- How can they raise system visibility and system control over operations that are more dispersed and changing than ever?
- How can they cut costs, yet create a more agile and responsive IT system?
- How can they do more with older data, even as they understand better the data from a market that is changing every week?
- How can they help people work faster, with a minimum of change management, or set the stage for growth, while preserving capital?
In many cases the answer is a step-by-step deployment of cloud computing technology, tailored to meet the most pressing needs first.
NCR’s Emerald Leverages Google Cloud to Help Grocers Boost Operational Agility
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In recent years, the grocery industry has had to shift to facilitate a wider variety of checkout journeys for customers. This has meant ensuring a richer transaction mix, including mobile shopping, online shopping, in-store checkout, cashierless checkout or any combination thereof like buy online, pickup in store (BOPIS).
What’s more, in the past year and a half alone grocers have had to enable consumers new ways to shop for essentials. This has included needing to rapidly integrate or build on-demand delivery apps, offer curbside pickup with near-instant fulfillment as well as support touchless and cashless checkout experiences. Searches on Google Maps for retailers in the US with curbside pickup options have increased by 9000% since March 2020, and we believe these trends from 2020 will continue to define the future of grocery shopping.
The future of grocery will require agility and openness
Firstly, the need to rapidly adapt to changing consumer habits will be the new normal. Grocers will increasingly look to digitally transform legacy retail systems and modernize point of sale (POS) platforms to deliver and scale omnichannel experiences as quickly as possible. This necessitates a more agile and open architectural approach to technology – one built on microservices and leverages APIs so that new applications and experiences can be built, integrated and delivered faster.
Automation and data-driven retailing will be table stakes
In order for retailers to blend what they’re offering in the store with digital experiences more efficiently, they will also need to automate more. For example, with automation and business intelligence, grocers can take labor that might have been tied up with tender operations and checkout and redistribute those resources to restocking shelves, curbside pick-up or improving customer experiences.
Automation and access to real-time in-store inventory & supply chain data can also help grocers avoid the supply chain challenges seen in the early days of COVID-19. Grocers will need to find ways to leverage automation to ingest, organize, and analyze data from physical store networks, digital channels, distribution centers to better forecast demand and manage future fluctuations.
How NCR and Google Cloud are helping grocers adapt to disruption with operational agility
Helping grocers improve operational agility to address changing consumer shopping habits and to thrive during times of disruption is something that NCR and Google Cloud have teamed up to do. NCR has over 135 years of experience in retail, having invented the cash register and are continuing to help grocers innovate. NCR Emerald builds upon the company’s leadership in POS software and has turned it into a unified platform that helps grocers operate the entire store from front to back. The solution supports cashier-led checkout, self-checkout, integrated payments, merchandising, and enables regional managers and corporate employees access to the analytics and tools needed to optimize loyalty programs and promotions.
NCR has invested in a comprehensive, agile, and API-led retail architecture that lets grocers continually innovate and design new experiences as customers and the industry evolve. By running Emerald on Google Cloud, NCR can offer the solution on a subscription basis, helping grocers lower upfront capital expenditures and ensuring scalability. What’s more, NCR can tap into Google Cloud’s strength in data, analytics, and openness to deliver three key imperatives. Let’s take a look at each of these below.
Run the way grocers need to while leveraging Google Cloud as a single source of logic
Traditionally the POS system lived in the store. If disaster strikes, people still need access to food and essentials so the grocery store still needs to operate. It hardly gets more mission-critical than that. NCR Emerald is built on microservices, leveraging Kubernetes for front-of-house compute, and VMs (See graphic 1 below). This makes it easy to support lightweight clients accessible by store employees via any range of mobile devices, computer terminals, self-service kiosks, peripheral devices like receipt printers as well as legacy applications.
What’s unique is that because Emerald runs on Google Cloud, it supports all those in-store and digital touchpoints mentioned above, but also allows grocers to run lean. Emerald leverages Google Cloud as a single source of truth and operates a lot of what it does out of logic. Every sales transaction coming from every channel, including e-commerce, can be logged via NCR’s Hosted Service and centralized in BigQuery and Bigtable as a transaction data master. This enables the grocer to manage any transactional use case very consistently, whether it e supporting customers who want to purchase in one store and return in another, offering digital receipts or the ability to exchange online purchases in store. Emerald on Google Cloud can help retailers extend capabilities through the power of the cloud but not need to live exclusively in the cloud. In other words, the solution allows grocers the ability to run the way they need to.
Enable data-driven and real-time decision making for grocers
Store managers, regional managers, category managers, and others all require different cuts of the data to do their jobs effectively. However, data silos persist and how data is formatted and arranged can still remain pretty static. Therefore allowing users with different roles the ability to view and analyze that data quickly and in different ways continues to be a challenge.
As mentioned above, Emerald leverages Google Cloud data management solutions as the central repository for transactional, behavioral, and merchandising data. Every transaction from every store and every channel can be stored via NCR Hosted Service on BigQuery and Bigtable. NCR Analytics then harnesses the advanced analytical and data visualization capabilities of Looker to help grocers get a consolidated view of their business across all channels and then allow employees to slice and dice the data they way they need to. NCR Analytics also leverages the power of Google Cloud AI and machine learning to add another level of intelligence to the retailer’s data. For example, store managers can visualize how well they’re using their real estate and see how productive lanes 1-3 are compared with 7-10 or compare self-service versus manned lanes. By mapping to the retailer’s own catalog, they can also break down category-level performance and trends.
NCR Analytics takes advantage of Google Cloud’s data pipeline to reduce processing time, with scaling and resource management provided out of the box. By letting the cloud store and process the data, NCR is providing the ability for retailers to analyze their data in near real-time across all platforms – a real game changer in the grocery business.
Open APIs let grocers continually enrich the retail experience
Finally, Emerald is built on an API-first architecture managed through Apigee. It uses the power of Apigee as an open API platform to expose how Emerald can work with other NCR applications like loyalty and promotions, and third party applications like mobile ordering and order delivery to enrich the grocery experience for employees and customers. Every API that Emerald uses is available on Apigee, allowing them to share code samples and giving developers the ability to run scripts. This approach can allow retailers the ability to innovate in a fraction of the time and cost, speeding up 3rd party integrations up front and as businesses grow.
Take, for example, Northgate Market, a chain of 40 stores in California, that were able to transform its digital operations and enable experiences that set it apart from competitors – quickly and simply with Emerald. It took less than 6 months to go from contract to live deployment in the first store. Since then, Northgate Market has been able to extend their intelligence by leveraging the power of Looker and NCR Analytics.
Learn more about how NCR has been able to leverage an open, cloud-enabled architecture to help customers innovate across the retail, hospitality, and banking industries on the webinar “Role of APIs in Digital Transformation”. You can also learn more about how Northgate uses e-commerce to transform customer experience and gain consumer insights.
SoFi Stadium Personalizes Fan Experiences with Game-changing App Built on Google Cloud and Deloitte
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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.
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Centralized Analytics: Apigee and Cloud Run API Management
Most enterprise companies enforce strict requirements for how APIs should be exposed to the public internet that require centralized handling of the authentication credentials, the collection of metrics metrics and other classic API management capabilities. Before the introduction of sidecars in Cloud Run, developers either had to implement these requirements
Lower risk, greater return: Public sector organizations are focusing on multicloud adoption
Government organizations are starting to realize that no single cloud can meet their wide range of needs. According to the Nutanix 2022 Enterprise Cloud Index1, 75% of public sector organizations agree that multicloud is ideal, and more public sector organizations than average have adopted multicloud as a primary IT operating
