A Record Breaking Calculation: 100 Trillion Digits of π on Google Cloud!
3574
Of your peers have already read this article.
4:00 Minutes
The most insightful time you'll spend today!
Records are made to be broken. In 2019, we calculated 31.4 trillion digits of π — a world record at the time. Then, in 2021, scientists at the University of Applied Sciences of the Grisons calculated another 31.4 trillion digits of the constant, bringing the total up to 62.8 trillion decimal places. Today we’re announcing yet another record: 100 trillion digits of π.
This is the second time we’ve used Google Cloud to calculate a record number1 of digits for the mathematical constant, tripling the number of digits in just three years.
This achievement is a testament to how much faster Google Cloud infrastructure gets, year in, year out. The underlying technology that made this possible is Compute Engine, Google Cloud’s secure and customizable compute service, and its several recent additions and improvements: the Compute Engine N2 machine family, 100 Gbps egress bandwidth, Google Virtual NIC, and balanced Persistent Disks. It’s a long list, but we’ll explain each feature one by one.
Before we dive into the tech, here’s an overview of the job we ran to calculate our 100 trillion digits of π.
- Program: y-cruncher v0.7.8, by Alexander J. Yee
- Algorithm: Chudnovsky algorithm
- Compute node: n2-highmem-128 with 128 vCPUs and 864 GB RAM
- Start time: Thu Oct 14 04:45:44 2021 UTC
- End time: Mon Mar 21 04:16:52 2022 UTC
- Total elapsed time: 157 days, 23 hours, 31 minutes and 7.651 seconds
- Total storage size: 663 TB available, 515 TB used
- Total I/O: 43.5 PB read, 38.5 PB written, 82 PB total
Architecture overview
Calculating π is compute-, storage-, and network-intensive. Here’s how we configured our Compute Engine environment for the challenge.
For storage, we estimated the size of the temporary storage required for the calculation to be around 554 TB. The maximum persistent disk capacity that you can attach to a single virtual machine is 257 TB, which is often enough for traditional single node applications, but not in this case. We designed a cluster of one computational node and 32 storage nodes, for a total of 64 iSCSI block storage targets.
The main compute node is a n2-highmem-128 machine running Debian Linux 11, with 128 vCPUs and 864 GB of memory, and 100 Gbps egress bandwidth support. The higher bandwidth support is a critical requirement for the system as we adopted a network-based shared storage architecture.
Each storage server is a n2-highcpu-16 machine configured with two 10,359 GB zonal balanced persistent disks. The N2 machine series provides balanced price/performance, and when configured with 16 vCPUs it provides a network bandwidth of 32 Gbps, with an option to use the latest Intel Ice Lake CPU platform, which makes it a good choice for high-performance storage servers.
Automating the solution
We used Terraform to set up and manage the cluster. We also wrote a couple of shell scripts to automate critical tasks such as deleting old snapshots, and restarting from snapshots (we didn’t need to use this though). The Terraform scripts created OS guest policies to help ensure that the required software packages were automatically installed. Part of the guest OS setup process was handled by startup scripts. In this way, we were able to recreate the entire cluster with just a few commands.
We knew the calculation would run for several months and even a small performance difference could change the runtime by days or possibly weeks. There are also a number of combinations of parameters in the operating system, infrastructure, and application itself. Terraform helped us test dozens of different infrastructure options in a short time. We also developed a small program that runs y-cruncher with different parameters and automated a significant portion of the measurement. Overall, the final design for this calculation was about twice as fast as our first design. In other words, the calculation could’ve taken 300 days instead of 157 days!
The scripts we used are available on GitHub if you want to look at the actual code that we used to calculate the 100 trillion digits.
Choosing the right machine type for the job
Compute Engine offers machine types that support compute- and I/O-intensive workloads. The amount of available memory and network bandwidth were the two most important factors, so we selected n2-highmem-128 (Intel Xeon, 128 vCPUs and 864 GB RAM). It satisfied our requirements: high-performance CPU, large memory, and 100 Gbps egress bandwidth. This VM shape is part of the most popular general purpose VM family in Google Cloud.
100 Gbps networking
The n2-highmem-128 machine type’s support for up to 100 Gbps of egress throughput was also critical. Back in 2019 when we did our 31.4-trillion digit calculation, egress throughput was only 16 Gbps, meaning that bandwidth has increased by 600% in just three years. This increase was a big factor that made this 100-trillion experiment possible, allowing us to move 82.0 PB of data for the calculation, up from 19.1 PB in 2019.
We also changed the network driver from virtio to the new Google Virtual NIC (gVNIC). gVNIC is a new device driver and tightly integrates with Google’s Andromeda virtual network stack to help achieve higher throughput and lower latency. It is also a requirement for 100 Gbps egress bandwidth.
Storage design
Our choice of storage was crucial to the success of this cluster – in terms of capacity, performance, reliability, cost and more. Because the dataset doesn’t fit into main memory, the speed of the storage system was the bottleneck of the calculation. We needed a robust, durable storage system that could handle petabytes of data without any loss or corruption, while fully utilizing the 100 Gbps bandwidth.
Persistent Disk (PD) is a durable high-performance storage option for Compute Engine virtual machines. For this job we decided to use balanced PD, a new type of persistent disk that offers up to 1,200 MB/s read and write throughput and 15-80k IOPS, for about 60% of the cost of SSD PDs. This storage profile is a sweet spot for y-cruncher, which needs high throughput and medium IOPS.
Using Terraform, we tested different combinations of storage node counts, iSCSI targets per node, machine types, and disk size. From those tests, we determined that 32 nodes and 64 disks would likely achieve the best performance for this particular workload.
We scheduled backups automatically every two days using a shell script that checks the time since the last snapshots, runs the fstrim command to discard all unused blocks, and runs the gcloud compute disks snapshot command to create PD snapshots. The gcloud command returns and y-cruncher resumes calculations after a few seconds while the Compute Engine infrastructure copies the data blocks asynchronously in the background, minimizing downtime for the backups.
To store the final results, we attached two 50 TB disks directly to the compute node. Those disks weren’t used until the very last moment, so we didn’t allocate the full capacity until y-cruncher reached the final steps of the calculation, saving four months worth of storage costs for 100 TB.
Results
All this fine tuning and benchmarking got us to the one-hundred trillionth digit of π — 0. We verified the final numbers with another algorithm (Bailey–Borwein–Plouffe formula) when the calculation was completed. This verification was the scariest moment of the entire process because there is no sure way of knowing whether or not the calculation was successful until it finished, five months after it began. Happily, the Bailey-Borwein-Plouffe formula found that our results were valid. Woo-hoo! Here are the last 100 digits of the result:
4658718895 1242883556 4671544483 9873493812 1206904813
2656719174 5255431487 2142102057 7077336434 3095295560
You can also access the entire sequence of numbers on our demo site.
So what?
You may not need to calculate trillions of decimals of π, but this massive calculation demonstrates how Google Cloud’s flexible infrastructure lets teams around the world push the boundaries of scientific experimentation. It’s also an example of the reliability of our products – the program ran for more than five months without node failures, and handled every bit in the 82 PB of disk I/O correctly. The improvements to our infrastructure and products over the last three years made this calculation possible.
Running this calculation was great fun, and we hope that this blog post has given you some ideas about how to use Google Cloud’s scalable compute, networking, and storage infrastructure for your own high performance computing workloads. To get started, we’ve created a codelab where you can create and calculate pi on a Compute Engine virtual machine with step-by-step instructions. And for more on the history of calculating pi, check out this post on The Keyword. Here’s to breaking the next record!
- We are actively working with Guinness World Records to secure their official validation of this feat as a “World Record”, but we couldn’t wait to share it with the world. This record has been reviewed and validated by Alexander J. Yee, the author of y-cruncher.
3986
Of your peers have already watched this video.
1:20 Minutes
The most insightful time you'll spend today!
Philips Looks to Google Cloud for its Connected Lighting Solution
Philips Lighting wanted to transform the way people use lighting in their homes. The company aimed to connect light bulbs to the Internet, tie them to usage data, and make them interactive in order to offer benefits beyond basic lighting—for creating amazing experiences, home security, or to support well-being, like providing the right light for daily activities.
To do that, Philips Lighting launched Philips Hue connected lighting, designed so people could control their lighting from smartphone apps. But Philips Lighting needed a cloud platform that would let the apps securely access, monitor, and interact with the new lighting system. The company decided to build the backend using Google Cloud Platform.
Google Cloud Platform has dramatically cut the costs and resources required to handle the Philips Hue backend and scales on demand. Philips Lighting runs the platform with 10 times the scale of other similar projects, but with only one-tenth of the workforce.
Watch the video to find out how.
You Can Now ‘Listen’ to Over 50 Tech Blogs on Google Cloud Reader
4874
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
🎧 Prefer to listen? Check out this episode on the Google Cloud Reader podcast
If you’re anything like me, you love reading, but also appreciate that sometimes your eyes need to be doing other things; whether it’s finding your exit off the highway, or keeping your puppy from destroying the couch.
And sometimes the thought of sitting down to read something just feels like it’s going to take valuable multi-tasking time away from my day. I know, I know, multitasking can be frowned upon, but it’s the way I live a good chunk of my life, and it’s working out so far. And while I’m not alone in my multitasking, I’m also not alone in my desire for a non-visual way to get this content, or any content.
*Google Cloud Reader enters the chat*
Google Cloud Reader is a podcast that lets you listen to the Google Cloud Blog posts that aren’t as dependent on visuals. This means they’re articles that are, or are adapted to be, less focused on graphs, or code samples, and instead describe the meaning behind those visual aids.
It’s an easy, audible way to absorb content around all things new in Cloud, while still being able to make sure Ruthie doesn’t eat my work from home equipment.
So by now you’re probably thinking “OK, so you started a podcast during the pandemic, even though you definitely seemed like the type to start making sourdough”—and you’re right. My 53 plants agree with you. But rest assured, one can listen to an episode of this podcast *while* creating a macramé plant hanger, or waiting for bread to rise—multitasking, am I right?
We’re a little over 50 episodes/macrame plant hangers in, so you should check it out (Ruth and I would appreciate it).
Some of my personal favorites
- Beginners Guide to Painless Machine Learning – Learn how to get started with Google Cloud AI tools
- Introducing GKE Autopilot: A Revolution in Managed Kubernetes – Learn more about GKE Autopilot, a revolutionary mode of operations for managed Kubernetes that lets you focus on your software, while GKE Autopilot manages the infrastructure.
- Cook up your own ML recipes with AI Platform – Learn about Mars Wrigley’s new ML-inspired recipe experiment on Google Cloud and how you can get started with your own.
- Recovering Global Wildlife Populations using ML – Review Google’s Wildlife Insight’s ML project and help users create an image classification model for motion-sensor cameras (called camera traps) used to help protect wildlife in an non-invasive way by collecting and tagging species via pictures.
Let me know your favorite episodes, and what other articles you’d like to hear on Twitter @jbrojbrojbro!
No matter why you prefer an audio format, we’ve got you covered; Google Cloud Reader, where we read the tech blog for you, and to you.
Get all the Google Cloud Reader on your favorite podcast platform, including Google Podcasts, Apple Podcasts, and Spotify.
Making Mothers’ Day Special: How Google Cloud Migration for 1-800-FLOWERS.COM, Inc Impacts CX
6830
Of your peers have already read this article.
2:30 Minutes
The most insightful time you'll spend today!
Editor’s note: In honor of Mother’s Day, we look at how 1-800-FLOWERS.COM, Inc. migrated to Google Cloud as part of its digital transformation to quickly deploy seamless and convenient customer experiences across multiple brands on Mother’s Day and every day.
As a leading provider of gifts designed to help customers express, connect, and celebrate, 1-800-FLOWERS.COM, Inc. has embraced cloud technologies to grow and transform its business through constant innovation. As part of our digital transformation, we recently completed the migration of our ecommerce platform and other services to Google Cloud. We’ve transitioned from a monolithic to a microservices platform, moved many workloads from our on-premises data centers to our Google Cloud environment, and scaled both horizontally and vertically.
Since our migration, we’ve developed efficient processes to launch new brands, improved the customer experience across all brands, and seen significantly increased site traffic.
Nurturing a more delightful customer journey
Customer delight is at the core of everything we do. Whether it be with a flower bouquet, a sweet treat, or a personalized keepsake, our mission is to deliver smiles. With the rise of the COVID-19 pandemic, we’ve all been challenged to find unique and safe ways to continue honoring the special connections in our lives and celebrating occasions with loved ones. Our customers have adapted by doing things such as sending gifts to isolated loved ones, sharing the same meal together virtually, or using video to engage with others through group activities like flower arranging and building charcuterie boards.
The customer experience is a top priority for us, and we constantly look for innovative ways to enhance the customer journey across our ecommerce platform of more than a dozen brands. As a result, we’ve continued to see a rise in demand as customers enjoy the ease and convenience of our site and discover our full family of brands.
Migrating to a cloud-first mindset
As we’ve continued to innovate and iterate on the customer experience, we knew we wanted to evolve our platform. We wanted to shift to a microservices platform, which would allow our team the opportunity to release updates to our site more often and set up the right continuous integration/continuous deployment (CI/CD) practices.
Working with Google Cloud, we were able to move our ecommerce platform to the cloud and standardize our site and brand deployment by building one release that could then be repeated across all of our brands. We built everything in a modular fashion, including microservices and code libraries, so that sites could be easily constructed and replicated for each brand. And because of this, we were able to launch Shari’s Berries extremely quickly after we acquired the brand in 2019.
Moving our platform to a completely homegrown solution of microservices was a daunting task. But our team handled it beautifully through load-testing, stress-testing, and building new monitoring tools. And with Google Cloud supporting us all along the way, managing the migration process was simple and easy from start to finish.
Arranging a better bouquet of services
Currently, we’ve migrated every customer-facing touchpoint for all of our brands to Google Cloud—whether it’s on the web or mobile, our AI bots, or our chat interfaces.
- We run on Google Kubernetes Engine and Istio.
- We have nearly 200 microservices built to help power our entire ecommerce stack across several cloud services running on Google Cloud.
- We’re utilizing BigQuery for our offline intelligence.
Results are coming up roses
Our new stack on Google Cloud has benefits for both us and our customers. We moved from a session-based to a token-based system, which provides enhanced security as well as a consistent, convenient experience across all our brands. Using service workers and a single-page app, we are able to download all the relevant site content to the browser in under two seconds to create an instant-click experience for each and every customer. We also use Google Analytics to measure our user interactions and provide personalized results to each customer. Our hope is that with this new system, we can learn from customer behavior to offer gift givers a more personalized shopping experience during each visit.
The benefits of our new tech stack have not only helped us enhance the solutions we offer to customers today, they’ve also enabled us to offer new ones at lightning speed. With our legacy system, we used to release new code once a week or once a month. Now, even during our peak periods, we’re able to release 10 to 15 times a day and can deploy and pivot quickly to create new microservices and microsites on the fly—often without having to touch any code.
Efficiencies abound
The benefits of moving our platform to Google Cloud have extended to our internal teams as well. Before the migration, we had only two environments for developing and testing, which made it time-consuming to test updates before they went into production. Now with Google Cloud, we have several different journey teams—which are made up of developers, product owners, and technical owners—all working in several different environments, solving problems, and creating new solutions together.
Everyone is now empowered to be self-sufficient, developing and releasing microservices on their own when they’re ready. This has given our developers more time to take part in continued development and learning opportunities. For example, we offer lunch-and-learn sessions as well as other resources for everyone to take advantage of so they can continue to learn and refine their skills.
Planting the seeds for future growth
As we look to the future and think about how we help our customers express, connect, and celebrate, we’ll continue to collaborate across teams to deliver solutions that spread smiles. Specifically, we’re exploring additional use of AI to help us better serve our customers across all our brands.
We’ve enjoyed the ongoing support we’ve received from the Google Cloud team as they help us build new solutions and design a road map for the future. Their support has helped the 1-800-FLOWERS.COM, Inc. team to realize the power of the cloud and bring the very best experience to our customers.
Learn more about 1-800-FLOWERS.COM, Inc., or check out our recent blog about cloud migration for the real world.
Bharat Light & Power’s CEO Says Enough! It’s Time to Leverage AI and IoT
3940
Of your peers have already read this article.
4:30 Minutes
The most insightful time you'll spend today!
As community and business concern over global warming, sustainability, and energy security continues to rise, companies such as Bharat Light & Power (BLP) are working on answers.
Founded in 2010 and located in Bangalore and Delhi, BLP is one of the leading clean energy generation companies in India. According to Tejpreet Singh Chopra, Chief Executive Officer of BLP, the business started with the aim of delivering clean energy from renewable sources such as wind, solar, biomass, and hydro to the 300 million residents of India that did not have reliable access to power at that time.
However, BLP soon began experiencing problems ensuring availability of the wind turbines that powered utility-scale wind power generation across the country.
“While we were investing large amounts building wind farms, the failure of the smallest component could shut down an entire wind turbine and compromise our efficient operation,” says Chopra. “A $5,000 battery could fail and shut down a $2.5 million piece of equipment!”
BLP transformed its legacy thinking about man, machine, technology, and process, and implemented an AI and Internet of Things (IoT) project. The business used hundreds of tags from its wind turbines to capture and send machine behavior and performance data to a control center. This data triggered an 18-month project to create AI and machine learning algorithms that would enable engineers to predict component failures in wind turbines, improve generation, and adjust maintenance and replacement schedules accordingly.
This activity provided the foundation of a technology business—one of the three companies that comprise BLP—that delivers enterprise AI for industrial uses over a platform, branded Orion.
“We provide an end-to-end solution that enables businesses in industries such as transportation, logistics, ports, infrastructure, oil and gas, supply chain manufacturing, steel, and automotive to improve productivity,” says Chopra.
As the business expanded, it began to extend beyond its core “AI for industry” mission. It recruited an IoT team to help factory owners and operators enable the programmable logic controllers (PLC) and supervisory control and data acquisition (SCADA) systems that run disparate equipment and machines—such as production line machines of different ages and countries of origin—to talk to each other and provide usable data and insights.
Agility and adaptability key
BLP’s management team realized early that the business needed to be agile and adaptable to keep pace with changes in technology.
“We knew we would have to effectively destroy and remake the business every 18–24 months to remain relevant, so we needed a service that could support our dynamic infrastructure, data, and AI needs,” says Chopra.
The business started operations on a cloud service but quickly ran into problems. “We found in the world of industry—the vast amounts of data, the variety of sources of data, and the complexity of insights required—created a very different set of challenges relative to the consumer technology environment,” says Chopra. “Our cloud provider could not provide an architecture that made sense for an industry-focused solution.”
Google Cloud to power AI and visual analytics
Within two years, BLP advanced its strategy and focused on using open source to reduce the cost of its architecture. As Orion matured and its take-up grew, the business began looking at multinational cloud services to run the forthcoming version 3.0 of the platform.
BLP found Google Cloud provided the best fit for its needs for a range of reasons, including the ease of use of Google Cloud services—likened by the BLP technology team as “the equivalent of a consumer app experience”—and its high quality database services, high availability, fast response times, and the power to run the platform’s AI and visual analytics.
BLP established as its key objective a 20% reduction in costs over its previous cloud service and availability levels exceeding 99.9%. The business completed the deployment in June 2019 following discussions and input from Google Cloud’s engineering and architecture experts prior to and during the first stage of the project.
Cloud IoT Core—a managed service that allows organizations to connect, manage, and ingest data from dispersed devices—is the cornerstone of the architecture supporting the latest latest version of the Orion platform, Orion 4.0. BLP has also created a data pipeline based on Cloud Pub/Sub event ingestion and delivery and Cloud Dataflow data processing.
Cloud Bigtable provides a high-performance NoSQL database service for the platform’s analytical workloads and BigQuery delivers a powerful analytics data warehouse. Cloud Functions enables the business’s developers to build event-driven serverless applications.
This architecture currently captures, processes, analyzes, and reports on 8 million data points in 578 turbines around the world per day and processes data from 200,000 data points in 4,000 sensors per day.
The Google Cloud architecture supports the data visualization and reporting and the AI and machine learning-powered products created by BLP. These reports and products enable users to monitor remote assets and use AI-based analytics to predict machine failures before they occur, sequence failure events, and maximize equipment uptime.
A range of benefits to customers
With Google Cloud, BLP is delivering projects with a range of benefits to customers, including increasing manufacturing productivity by at least 5% and reducing costs by about 10%.
“We’ve enabled one of the largest electrical companies in India to compare production line performance by capturing data from PLC and SCADA systems and extracting it to Google Cloud for processing, analysis, and reporting,” says Chopra. “For another customer, we’ve deployed an IoT system that has saved them about $200,000 in energy costs over six months.”
The business is also providing monitoring, reporting, and analysis to predict likely failures of gearboxes, bearings, generators, and blades in 2,000 wind turbines—that provide close over 2 GW of wind power—in countries such as France, Germany, Italy, India, Portugal, Spain, the United Kingdom, and the United States.
Furthermore, BLP is providing visual analytics to help one of the largest ports in the world detect when workers are not wearing helmets or safety equipment.
“We also do a lot of inventory track-and-trace work to help companies improve supply chains and help factories keep track of tooling through Bluetooth low-energy technologies,” says Chopra.
With Google Cloud, BLP is now ideally placed to execute its business strategy of helping customers improve productivity and increase growth, control and reduce costs, and enhance quality and safety.
“We are realizing this strategy by working to become the best company in the world at using AI to predict machine failure,” says Chopra. “Our underlying technology strategy entails enabling the most advanced IoT hardware used by industry to talk to software and the cloud, using the most powerful AI cloud around—which is why we chose to marry our AI algorithms with Google Cloud—and delivering insights through visualization.”
Testing Edge TPU
The business is now testing Edge TPU to run AI at the edge in high-performance, small-footprint, and low-power ASIC environments.
“We think that will be the next big revolution as TPU costs come down,” says Chopra. “It will be a considerable benefit performing AI at the edge rather than involving the full infrastructure of the cloud.”
BLP is keen to build on its existing relationship with and use of Google Cloud to further transform manufacturing worldwide.
“Manufacturers are pushing the boundaries of quality and cost through initiatives such as Lean Six Sigma, Zero Defects, poka-yoke, just in time, and other methodologies and approaches,” says Chopra.
“The next wave of manufacturing improvements are coming through industry 4.0, AI, IoT, and Google Cloud’s analytics, databases and other services. These are ideal to power this change in the industrial world.”
Consumption Packs Shorten’s Customers Transition to Google Cloud and Boosts Partners’ Financial Growth
3237
Of your peers have already read this article.
2:00 Minutes
The most insightful time you'll spend today!
When we launched Partner Advantage, we committed to making it predictable and easy for partners to drive business with us. Since launch, those commitments have been validated by channel experts like CRN, which gave Partner Advantage a 5-Star award for 2022, and by the fact that our partners have seen impressive growth* across virtually every facet of their business.
I am pleased to announce that our commitments endure today with the launch of Consumption Packs for Deal Acceleration Funds and Partner Services Funds (DAF and PSF for partners). Inspired by partner feedback, these new packages are designed to accelerate all stages of a customer’s journey to the cloud, and make it even easier and faster for partners to do business with us.
Consumption Packs are purpose-built so that partners can plan and initiate customer projects much more quickly, with predictable funding. They include assets and templates that allow partners to deliver Google Cloud designed and validated infrastructure, application migration and modernization plans to customers faster than ever–particularly for customers beginning their journey to the cloud. Based on learnings gathered from thousands of customer deployments, these turnkey packs have been designed by our partners and Google Cloud Partner Engineering and Professional Services’ teams.
Here’s a brief look at Consumption Packs in action:
- Consumption Packs offer pre-approved, curated templates and assets to simplify and shorten the process for most common projects.
- For Deal Acceleration Funds (DAF), packages include everything partners need to conduct assessments, workshops and proofs-of-concept so they can quickly meet customers where they are on their journey to the cloud.
- For Partner Services Funds (PSF), packages are structured so that partners can develop cloud ready foundation and migration plans that align with Google Cloud priority solution areas.
- Packages have pre-determined funding levels to enable faster deployments.
Partners still have the option to engage with Google Cloud Partner Advantage and their customers through customized requests, as they always have. This is ideally suited for projects that require a tailored approach to meet unique customer requirements.
We are launching nine consumption packs today focused on key enterprise workloads, with a vision toward introducing additional packages to cover more solutions. Partners can explore Consumption Packs now by visiting the Partner Advantage portal.
We welcome your continued feedback and suggestions, and look forward to helping our customers achieve new levels of growth and success, together.
See you in the cloud.
- The Google Cloud Business Opportunity For Partners, a commissioned Total Economic Impact™ study conducted by Forrester Consulting, October 2021
More Relevant Stories for Your Company
Greenovation: Adani and Google Cloud Join Forces for a Sustainable and Data-Driven India
As the biggest renewable energy developer, Adani aims to put India on the green map. Working with the world's cleanest cloud inspired the organisation to solve their own efficiency issues, and better manage their facilities across the country to lower their carbon footprint. For more on Adani, visit: https://www.adani.com/ For
S4 Agtech Transforms Agriculture with Google Cloud
Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help
Learn to Access Process Metrics for Full-visibility into Software and Infrastructure behind Your Apps
When you are experiencing an issue with your application or service, having deep visibility into both the infrastructure and the software powering your apps and services is critical. Most monitoring services provide insights at the Virtual Machine (VM) level, but few go further. To get a full picture of the
Accelerating Success: Tips and Techniques for Optimizing and Scaling Your Startup
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 resources curated for startups. This multi-series contains 3 chapters: Start, Build and
