Explore the Complete Startups’ Technical Guide on Google Cloud Tech Channel

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Bootstrap your Startup with our technical guided series
At Google Cloud, we want to provide you with the access to all the tools you need to grow your business. Through the Google Cloud Technical Guides for Startups, leverage industry leading solutions with how-to video guides and resource handbooks curated for startups.
This multi-series contains 3 chapters: Start, Build and Grow, which matches your startup’s stage of growth:
- The Start Series: Begin by building, deploying and managing new applications on Google Cloud from start to finish.
- The Build Series: Optimize and scale existing deployments to reach your target audiences.
- The Grow Series: Grow and attain scale with deployments on Google Cloud.
Kick off with The Start Series
The Start Series is designed to help your startup begin building, deploying and managing new applications on Google Cloud from start to finish. The series contains 12 videos and is dedicated to those who are starting out their cloud journey with Google Cloud. From setting up your project, to choosing the right compute option, to configuring your networking to managing your databases, and understanding support and billing – the Start Series guides you at every step of the journey.
Check out our website and our Google Cloud Technical Guides for Startups full playlist.
Coming up next – The Build Series
Launch into the next part of the journey continuing from the Start Series, with the upcoming Build Series, where we will be focusing on the optimization and scaling of existing deployments to help your startups reach your target audiences.
Join us by checking out the video series on the Google Cloud Tech channel, and subscribe to stay up to date.
See you in the cloud!

How The New York Times Increased Speed of Delivery by Using Kubernetes
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When New York Times decided a few years ago to move out of its data centers, its first deployments on the public cloud were smaller and less critical applications that were being managed on virtual machines.
“We started building more and more tools, and at some point, we realized that we were doing a disservice by treating Amazon as another data center,” says Deep Kapadia, Executive Director, Engineering at The New York Times.
Kapadia was tapped to lead a Delivery Engineering Team that would “design for the abstractions that cloud providers offer us.”
The team decided to use Google Cloud Platform and its Kubernetes-as-a-service offering, GKE (Google Kubernetes Engine). Owing to Google Cloud solution and GKE, The New York Times was able to increase the speed of delivery.
Some of the legacy VM-based deployments took 45 minutes; with Kubernetes, that time was “just a few seconds to a couple of minutes,” says Brian Balser, Engineering Manager at The New York Times.
“Teams that used to deploy on weekly schedules or had to coordinate schedules with the infrastructure team, now deploy their updates independently, and can do it daily when necessary,” says Tony Li, Site Reliability Engineer, The New York Times.
Adopting Cloud Native Computing Foundation technologies allowed The New York Times to have a more unified approach to deployment across the engineering staff, and portability for the company.

Modernize your Windows Server Workloads using Google Cloud Platform
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Application Modernization is an important enabler of Digital Transformations (DX), which fuel competitive advantage through increased productivity and business agility. Public cloud infrastructure proves to be a solid foundation for application modernization by providing Self-Service Provisioning capabilities, cloud-based & cloud-native technologies, and easier access to technology innovations such as AI/ML.
Windows Server-based enterprise applications rely on the underlying infrastructure for platform performance, security, and availability. A better performing cloud platform enables them to perform better and hence prove to be more resource-optimized and cost-effective.
Download this IDC report to understand why you should move your Windows Server workloads to Google Cloud and the benefits you can derive.

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Sky News live streamed the results from 150 of the 650 constituency counts in the U.K. while competitors, who did not have live video from as many counts, had to wait for slower independent data services to report the results. Sky News also delivered all the live streams over the Internet via YouTube, providing a service that none of its competitors offered.
Sky News faced a unique set of technical challenges in order to stream video from the constituency counting stations to YouTube and for TV broadcast. For streams to be used on air and be made simultaneously live via YouTube, each stream needed to be delivered to both Youtube and the Sky News studios. The streams from the field could not simply be sent to a receive server in the Sky News studios, as would be done for a regular news live.
So the company turned to Google Compute Engine, because it could quickly and affordably create virtual servers to process all incoming data streams. Sky News didn’t have to set up physical servers and connections.
How the City of Memphis Uses Technology to Identify 75 Percent More Potholes

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At 340 square miles, the City of Memphis is among the largest in the United States in terms of land area. Memphis has over 6,800 lane-miles of city streets, enough to drive back and forth to Los Angeles four times. Keeping these streets well maintained and safe for citizens and visitors is a major priority for the city.
Lots of traffic, lots of roads, and a four-season climate prone to wintertime freeze-thaw-refreeze cycles means the opportunity for potholes. Although the city aims to fill potholes within five business days of notification, it can take longer, especially during winter and early spring. Last year, the city’s Public Works crews repaired some 63,000 potholes, only 20% of which were reported by residents. Approximately 32,000-man-hours each year are spent repairing potholes, with seasonal fluctuations requiring ten to twelve Street Maintenance crews working steadily during the winter months. Still, many went unreported, leading the city to flag pothole request resolution under “needs improvement” on its open data portal website.
Like many large cities, Memphis also struggles with vacant and blighted properties. Nearly 15,000 properties in Memphis are likely vacant, and city officials contend that many are owned by out-of-town investors who live elsewhere and do not take necessary restoration or maintenance steps. These properties can decrease the value of surrounding real estate and discourage new businesses and other residents from moving to an area. Citizen frustration and concerns over the number of blighted properties has made blight eradication a major focus of the City of Memphis.
Historically, residents reported potholes and blighted properties by calling 311, or more recently by using the Memphis 311 app. However, these reports only covered about 20 percent of the problems — often the worst cases. And by the time residents took the initiative to submit a 311 report, they usually weren’t feeling good about the situation.
Recognizing that potholes and vacant properties are often the most visible indicators of whether a city government is doing its job efficiently, Memphis Mayor Jim Strickland and CIO Mike Rodriguez began looking for ways they could apply technology to fix the problems. Mike approached Google for ideas, and Google recommended conducting a machine learning proof-of-concept (POC) with SpringML, a Google Cloud Partner.
“Memphis is focused on easy living, and we want to do everything we can to keep our citizens happy,” says Mike Rodriguez. “Working with Google and SpringML to reduce potholes and urban blight using machine learning and artificial intelligence was an easy decision.”
Bringing machine learning to city operations and budgets
The city’s goal is to detect potholes and abandoned properties by analyzing video footage of roads and residential properties. It wanted to classify potholes by width and depth, and share the information with workers who can repair them. For abandoned properties, it wanted to enable more strategic deployment of resources for homeowners citywide and take action to hold neglectful property owners accountable.
The POC began by training TensorFlow models for ML object detection using preconfigured AI Platform Deep Learning VM Images on Compute Engine. SpringML helped set up cameras and developed a user interface to collect pothole data and automate the 311 ticketing process.
Together, the teams analyzed 30 days of video from a moving city bus and high-resolution video from 360-degree cameras mounted to a code enforcement vehicle, overlaid with data from 311 reports. As the models were refined, accuracy quickly climbed from 50 percent to over 90 percent as models were taught to differentiate a pothole from a manhole cover or other object.
The city also imported routes, potholes, and paving data along with geolocation data from ArcGIS and Google Maps into BigQuery to better understand street conditions and the proximity of potholes to one another. BigQuery also analyzes city property records, tax records, 311 reports, and third-party survey data on-demand to predict where homes are starting to become run down and where neighborhood decay is most likely to occur. The SpringML team created a pilot analysis to begin vacant property protections and developed a user interface tool to interact with the model’s results.
“Google Cloud Platform made it possible for us to experiment with machine learning and artificial intelligence to help solve our city’s problems while working within the budget constraints of a municipal IT organization,” says Mike. “Google turned a ‘nice to have’ into a ‘let’s do this!'”
Identifying 75 percent more potholes
Memphis expects to substantially reduce the number of potholes on its streets, creating a better driving experience for residents and visitors alike. Because drivers won’t be as likely to swerve to miss a pothole, streets will be safer and friendlier to bicycles and scooters. Fewer potholes will also save the city between $10,000 and $20,000 annually in city claims that it pays out in cases where vehicle damage results from a pothole that was not addressed in a timely manner.
“Historically, Public Works has relied primarily upon Street Maintenance crews to proactively locate and fill potholes. As Memphis has over 6,800 lane-miles of public streets, it is a daunting task to reliably survey the entire system in an efficient and systematic way,” says Robert Knecht, Public Works Director for the City of Memphis. “The outcome of the data collected will be invaluable to Public Works so that it can ensure it is managing the city’s street system in a more proactive manner.”
Memphis will be able to better prioritize road maintenance based on condition and impact, increasing the efficiency of its Public Works road crews. Analyzing video of streets also gave the city visibility into issues it wasn’t previously aware of, such as curbs, gutters, and manhole covers that had been mistakenly paved over and need to be excavated. The ML process is easily transferrable to other concerns as well, helping the city identify illegal signs or spools of cable hanging on light posts that could be potentially unsafe.
Helping communities recover and thrive
Memphis is also having success in analyzing predictive trends to combat high rates of abandoned and blighted properties, surpassing 97.5 percent accuracy. “In the past, Public Works experimented with comprehensive, city-wide blight identification by using approximately 200 volunteers to survey and photograph over 237,000 city parcels. This effort was costly, took a long time to complete, and resulted in inconsistent data collection,” says Robert. “Blighted property conditions can change quickly in a city the size of Memphis. Now, with this new technology, Memphis will be able to make a significant difference in the efforts to proactively and comprehensively identify and manage blighted and substandard properties.”
Code Enforcement with better data-driven detection mechanisms enables the city to also identify cases where homeowners are not physically or financially able to keep up with the challenges of homeownership and make them aware of resources that are available to assist them. Memphis Code Enforcement can do a better job of finding people living in derelict properties that pose hazards to inhabitants’ health and safety, and help them fix those problems or find a new place to live.
“Using SpringML and Google Cloud Platform to detect indicators of vacant or blighted properties will help Memphis create safer neighborhoods that will be more attractive to businesses and home buyers,” says Mike. “Property values and employment will go up, crime will go down, and social services can be more focused and effective.”
Revolutionizing service delivery for citizens
Memphis is proving the viability of a cost-effective, cloud-based machine learning model that other cities can follow. The city is already looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents.
As part of his commitment to a transparent government, Memphis Mayor Jim Strickland created an open data policy that commits to releasing raw data and sharing it with citizens in a variety of downloadable formats. Going forward, this transparency will help citizens understand how their needs are being served and uncover new, innovative use cases for AI and ML.
“Our goal is to become a smart city, and technologies such as Google Cloud Platform and SpringML put us ahead of the game,” says Mayor Strickland. “Google understands data, and there isn’t a better company to help us analyze our data resources for actionable insights.”
Highnote Build the First Flexible, End-to-end Embedded Finance Platform on Google Cloud

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The ability to quickly introduce and evolve payment options for products or services is essential for businesses, as nearly 50% of consumers who can’t use a preferred payment method abandon their purchase. At the same time, gift cards, branded credit cards and rewards programs are critical tools that companies rely on to build more loyal and lasting customer relationships. With Highnote, companies have an all-in-one embedded platform to quickly create payment cards and wallets, offer innovative rewards programs and credit, and provide sustainable wage access. It is the first platform that allows enterprises to make card issuance an embedded capability of their product without creating an entirely new (and costly) organization.
Creating an exciting fintech future with Google Cloud
When thinking about building the industry’s first end-to-end embedded finance platform, we quickly realized Highnote would only be successful if it enabled companies to truly innovate and quickly roll out new programs. To do so, the platform would have to be built on scalable infrastructure capable of securely delivering services with speed and reliability while offering easy access to actionable Big Data analytics.
Working closely with the team at the Google for Startups Cloud Program, we successfully implemented Google Cloud as a versatile, future-proof foundation of our platform—and built Highnote from the ground up in just one year. Highnote’s GraphQL-based API platform reinvents the card issuance process. Utilizing the developer-friendly Highnote platform, product and engineering teams at digital enterprises of all sizes can easily and efficiently embed virtual and physical payment cards (commercial and consumer prepaid, debit, credit, and charge), ledger, and wallet capabilities into their existing products. This creates compelling value while growing revenue and building a unique and differentiated brand.
We leverage Cloud Spanner, BigQuery, and Google Kubernetes Engine (GKE) to create a unified and highly secure PCI DSS-compliant platform with GraphQL APIs that provide rapid and flexible money transfers. This gives us a reliable platform to deliver and test customer experiences, respond to outcomes, and make better business decisions. Powered by Google Cloud, our data models and application domains are architected to support configurations and customizations that unlock a diverse set of new use cases across industries, including retail, travel, logistics, healthcare, and sustainable wage access programs.
We are especially proud to highlight our enablement of sustainable wage access, as this program helps the 50% of Americans living paycheck to paycheck. Embedding this program within payroll systems provides a viable alternative to payday lenders who often charge exorbitant fees and interest rates. In real world terms, this means Highnote helps people access earned wages before payday at no cost.
The other customer we just went live with was Tillful, and their Tillful card helps small businesses build their business credit. This program will help new and emerging businesses as well as underrepresented owners of small businesses by making the credit ecosystem accessible. Highnote’s platform is designed to support multiple use cases across many industries. For example, we also help the trucking and logistics companies to develop fleet and fuel cards, and spend management companies who are looking to uplevel offerings.
Delivering high-performance transactions with Cloud Spanner
Building one of the world’s most modern card platforms would not have been possible without Cloud Spanner. We needed a solution that would keep our massive petabyte databases from buckling and more securely deliver data anywhere in the U.S. Cloud Spanner does all this and more, as it routinely connects purchases from millions of customers to tens of thousands of vendors. We also wanted to reduce overhead by 80% by eliminating manual sharding, partitioning, and optimization of data. These processes are automatic with Cloud Spanner so we can operate at maximum efficiency.
We specifically selected Cloud Spanner as our distributed SQL database management and storage solution because of its outstanding availability, zero plan maintenance downtime, security certifications, and the highest consistency guarantees of any scale-out database. We continue to optimally scale without any downtime or compromises to the integrity or security of our data. This is key for us because we can address unexpected spikes, long-term growth, and new services without costly rearchitecting.
Highnote is designed to perform over billions of transactions on Cloud Spanner, and the average latency of less than 250 ms is a testament to the robustness of Google Cloud services.
Enabling actionable customer insights at scale
BigQuery is another key Google Cloud solution that we rely on to deliver deep insights and visibility for our customers on a highly secure and scalable platform. When building Highnote, we knew we needed a cost-effective solution that excelled at data analytics. This is particularly critical for accurately measuring the performance—whether profitability or efficacy—of any program or card.
Using BigQuery, we successfully run analytics at scale with as much as a 34% lower three-year TCO than cloud data warehouse alternatives. Over the past year, BigQuery has enabled our customers to unlock data-rich capabilities with a ledger that tracks money in real time and serves up complete debit and credit entries for every event across their accounts. Companies also access real time balances for revenue, fees, customer accounts, and available funds management without complicated spreadsheets.
To quickly and efficiently roll out Highnote to our customers, we needed a simple way to automatically deploy, scale, and manage Kubernetes. When selecting a Kubernetes management tool, our top priorities were rapidly spinning up and securely scaling across multiple sites. As part of Google Cloud’s expansive ecosystem, Google Kubernetes Engine (GKE) was the top choice due to seamless and automatic Kubernetes scaling and management.
We quickly got off the ground with single-click clusters and scaled up by using the high-availability control plane—including multi-zonal and regional clusters—to easily accommodate multiple active-active regions (which other solutions cannot do). As an embedded finance platform, stringent security protocols were obviously a key consideration for us. GKE is secure by default and runs routine vulnerability scans of container images and data encryption. Further security assistance was provided by Google Cloud partners 66degrees and DoiT International to help us rapidly validate VPC PCI compliance and ensure the uninterrupted performance of thousands of transactions per second.
Winning in fintech with Google for Startups
Building the industry’s first end-to-end embedded finance platform would have been extremely challenging without the extensive Google Cloud support. By working closely with our Startups team and Google partners, we had access to Google Cloud services to more easily validate VPC PCI compliance and address most issues before we exited stealth. Their responsiveness is incredible and stands out compared to support services we’ve seen from other technology providers.
Our participation in the Google for Startups Cloud program has been instrumental to our success. With Google Cloud, we are making embedded payments accessible to our customers without a big budget price tag. By doing so, we help unleash the creativity of emerging enterprises by enabling them to innovate with payment services and rewards programs to reach new markets and customers. If companies can dream, we can enable them to realize it on Highnote. Our platform really is that flexible. We’re excited where we can go and grow with Google Cloud.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
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