For the fourth consecutive year, Gartner has positioned Google as a Leader in the 2021 Gartner Magic Quadrant for Cloud Infrastructure and Platform Services (formerly titled as Magic Quadrant for Cloud Infrastructure as a Service upto 2014 Infrastructure as a Service, or (IaaS).

With our customers and communities adjusting to new ways of working and doing business, Google Cloud has remained focused on building services and platforms that help you be more resilient and derive even more value from your cloud infrastructure. We believe Gartner’s analysis and recognition gives our customers the confidence needed to choose Google as the platform for customer-centric innovation. Here are just a few recent examples.

Ready for the most demanding, mission-critical workloads

Our enterprise-ready cloud provides you the uptime, performance, and scale to run even your most demanding workloads. Examples of recent launches: 

• The largest single-node GPU-enabled VM in the industry with up to 16 NVIDIA A100 instances so that our customers can run their ML workloads 
• The only cloud to support scale-out out 96TB SAP HANA so that customers can confidently bring their most critical workloads to GCP
• Strategic partnerships with leading partners like SAP 
• Several regions and an expanded global network footprint including new subsea cables, Firmina, Dunant, Blue and Raman
• High bandwidth 50/75/100Gbps networking for VMs
• Persistent Disk Extreme (block storage) with 120K IOPS 
• Filestore High Scale scale-out NFS for HPC

Saves you money

Save money with a transparent and innovative approach to pricing and intelligent recommendations. In the past year, we’ve launched several innovations to help you save costs:

• Tau VMs, which offer the best price-performance among leading clouds for scale-out workloads 
• Machine-learning-driven predictive auto-scaling for VMs and GKE Autopilot, enabling infrastructure to scale up and down as needed with minimal waste 
• Standard network tier which routes traffic over the internet for cost optimization 

Open

We have a long history of leadership in open technologies—from projects like Kubernetes, the industry standard in container orchestration and interoperability, to TensorFlow, a platform to help anyone develop and train machine learning models. Here are a few recent improvements we’ve made to ensure your cloud is an open cloud: 

• Extended Anthos to bare metal and Microsoft Azure to support customers who want a multi-cloud and hybrid cloud posture. 
• Announced a new network dataplane for Google Kubernetes Engine (GKE) and Anthos that supports eBPF, an open-source Linux kernel technology optimized for Kubernetes. 
• Google Kubernetes Engine (based on the Kubernetes standard) received the top overall score based on 2021 Gartner Solution Scorecard for Google Kubernetes Engine.

Secure

Google Cloud’s trusted infrastructure uses layers of security to protect your data with advanced technologies and operations, keeping your organization secure and compliant. For example, we offer:

• Confidential VMs and Confidential GKE with in-memory encryption and encryption keys controlled by you, with a single checkbox
• Enhanced security for Cloud Run
• Strong support against DDoS attacks. In 2017, our infrastructure absorbed the largest-known DDoS attack at 2.5Tbps with no impact to customers. 

Sustainable

Google Cloud helps customers transform their business sustainably. We operate the cleanest cloud in the industry to make sure your digital footprint doesn’t leave a carbon one. Here are a few proof points:

• Google has been carbon neutral since 2007, and for the past four years has matched 100% of the electricity we consume globally with wind and solar purchases. Everything you run on Google Cloud is net carbon neutral. 
• We continue to innovate towards greater energy efficiency in our data centers, and compared with five years ago, now deliver around seven times as much computing power with the same amount of electrical power.
• Recently we announced new features to help customers reduce the carbon footprint of their applications and infrastructure, including a region picker to help with architecture decisions, and low carbon indicators in the Google Cloud Console.  

Supporting our customers

Most importantly, our field organizations and partner organizations work with a singular focus to ensure customer success. This has made Google Cloud the fastest growing hyperscaler, with a rapidly expanding customer base across all geos and industries.

Since launching Customer Care last year, we consolidated and simplified the post-sales engagement with customers, increased the support channels, created an API to allow programmatic case creation, and combined product specific support into a single package for all of Google Cloud. Enterprises with Customer Care continue to report high levels of satisfaction with their focused technical account managers (TAMs), helping them get the most business value out of Google Cloud.

We are committed to sustaining and accelerating the pace of customer-centric innovation. You can download a complimentary copy of the 2021 Magic Quadrant for Cloud Infrastructure and Platform Services on our website. 

Join us to learn much more about Google Cloud at the upcoming Google Cloud Next ‘21 digital conference.  

^{Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services,  Raj Bala | Bob Gill | Dennis Smith | Kevin Ji | David Wright, 27 July 2021
Gartner, Solution Scorecard for Google Kubernetes Engine,  Tony Iams | Traverse Clayton | Megan Bain, 12 April 2021
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

Customers need scale and flexibility from their cloud and this extends into supporting services such as monitoring and logging. Google Cloud’s Monitoring and Logging observability services are built on the same platforms used by all of Google that handle over 16 million metrics queries per second, 2.5 exabytes of logs per month, and over 14 quadrillion metric points on disk, as of 2020. However, you let us know through consistent feedback that the previous construct of Workspaces for Cloud Monitoring was not providing the flexibility needed for your larger scale projects.

Cloud Operation’s New Approach to Multi-Project Monitoring

We’re happy to announce a new model for multi-project monitoring, which replaces the concept of Workspaces. This overhaul is geared toward maximizing the flexibility you have to manage your monitoring environments by introducing Metrics Scopes. Starting today you can associate your Google Cloud projects with multiple Metrics Scopes! Like Workspaces, Metrics Scopes will still be used to store all of the configuration content for dashboards, alerting policies, uptime checks, notification channels, and group definitions. However there is no limit to the number of Metrics Scopes to which you can associate a project. Prior to this change, a project could only be scoped with a single Workspace. Now, there are virtually unlimited possibilities for how you can set up multi-project monitoring. This unlocks a large variety of options, from more granular permissions to mission-focused configurations. At its most simple implementation though: operators/SREs can now create org-wide Metrics Scopes with monitoring configurations focused on infrastructure health. And developers can leverage Metrics Scopes built on a subset of their organization’s projects that allow them to focus on their application’s performance.

How it works

• When you have a collection of projects, Metrics Scopes enable you to view each project’s metrics in isolation as well as in combination with metrics stored by other projects. 
• The Metrics Scope is hosted by a scoping project. This scoping project is the Cloud project that is selected in the Cloud Console project picker.

Example

• In this example, Project-SRE is the name of a scoping project to monitor your fleet. You added two developer teams’ projects: Project-Dev-1 and Project-Dev-2, to Project-SRE’s Metrics Scope. If you select Project-SRE with the Cloud Console project picker and then go to the Monitoring page, you view the metrics for all three projects: 

• If you select Project-Dev-1 with the Cloud Console project picker and then go to the Monitoring page, you view the Metrics Scope for Project-Dev-1 and you can only see the metrics for that project:

What else is new?

• Metrics Scopes can now monitor up to 375 projects (up from 100).
• New projects automatically start working in Cloud Monitoring without the previous 60-second Workspace creation process.
• If you want to monitor more than one project simply add it to your Metrics Scope:

Navigation

• Mentioned earlier, the Project Picker in the Cloud Console can be used to navigate between Metrics Scopes in Cloud Monitoring:

• This is now consistent with many other services across Google Cloud. Specifically, you can see how the project picker stays consistent when navigating from Cloud Monitoring to Cloud Logging:

• Additionally, to make your navigation between Metrics Scopes easy we’ve added the new Metrics Scope Tab and Panel in the UI:

Coming Soon

• The Metrics Scope API is coming within the next quarter! This API will enable you to programmatically manage your monitoring configurations and Metrics Scopes.

Current Workspaces users

If you are already using Workspaces in Cloud Monitoring you may have noticed that they converted to Metrics Scopes weeks ago. There is no additional action required and you can start taking advantage of the additional features of Metrics Scopes today.

Get Started

Companies that are digitally native or in the process of digital transformation have placed an increased operational role on developers and this often creates overlapping sets of responsibilities with Operations and SRE teams. Now multiple developer teams can focus on optimizing the performance of their applications while operators can take a fleet-wide view when maintaining and improving the performance of all of the infrastructure under their purview.For information on configuring a Metrics Scope to include metrics for multiple projects, see Viewing metrics for multiple projects.

Businesses that operate in complex cloud environments, large fleets, or sophisticated security operations all require visibility into their cloud assets in order to keep their teams nimble and their data secure. Cloud Asset Inventory (CAI) helps these teams understand their Google Cloud  and Anthos environments by providing complete visibility, real-time monitoring, and powerful asset analysis capabilities. Today, Cloud Asset Inventory gets four new capabilities that help you understand your environment more clearly and easily than ever before.

New user interface eases asset and insight discovery 

Cloud Asset Inventory console preview is now publicly available for GCP and Anthos customers. This preview provides insights into your cloud footprint, history and details of resource usage with powerful filtering and search capabilities. For example, you can view your global distribution of resources and policies, how your GCE VM footprint has been changing over time, as well as full metadata and change history for all your assets. The CAI console can be filtered at the organization, folder, or project-level, so each user can view the resources they have permissions for down to project level granularity.

Asset discovery and Datadog integration

A new asset list service in CAI provides quick and comprehensive asset discovery, including asset history, without needing to export the data to a storage destination. Datadog, a leading multi-cloud monitoring and security service provider, relies on deep integration with CAI for service and asset discovery. Datadog has been piloting and taking full advantage of the newly released asset list service. Datadog Product Manager, Steve Harrington, commented: 

“Google’s new Cloud Asset Inventory API provides us with an immensely valuable, single source of truth for determining the resources present in a given GCP environment. Along with its rich metadata, this enables us to enhance multiple aspects of our integration with GCP, including streamlined metric collection and ingestion of custom labels. We plan to continue building around Cloud Asset Inventory in the future to improve existing features, and are envisioning ways it could help us provide entirely new insights to our customers.”

Answer “who can access what resources?”

Determining authoritative answers to security-related questions like “Who can read data from my storage bucket that contains PII?” or “Does a terminated employee still have any remaining access to my system?” can be difficult and time consuming. This is why access management and identity certification is one of the top security priorities for enterprises running workloads in the cloud. To help alleviate this challenge, the new Policy Analyzer capability in CAI thoroughly analyzes the relationship between IAM policies and resources. The analysis includes powerful and efficient group expansion, service account impersonation, conditional access analysis, resource expansion, and more. You can even export the results to a BigQuery table or Cloud Storage bucket for further analysis and record keeping. CAI’s enhanced UI makes it even easier for you to build your own flexible queries and quickly get to a comprehensive answer.

Create asset posture visibility 

Cloud Asset Inventory now provides seven types of Asset Insights through the Active Assist platform. These new asset insights help proactively detect anomalies within your organization’s IAM policies, which may be opportunities to improve your security posture. The insights can be aggregated at the Organization, Folder or Project level. 

The seven new Asset Insights include: 

• External members in IAM policies. 
• External users that impersonate your service accounts.
• External members as policy editors.
• External users who can view cloud storage buckets.
• Terminated users/groups that are still in IAM policies
• IAM policies containing all users or all authenticated users.
• Projects with only terminated users as owners.

As a Google Cloud customer you can get started and use all the recently released capabilities and features immediately; check out our documentation to see how. We’d love to hear your feedback; email us with any questions or concerns!

Like virtually every business across the globe, The New York Times had to quickly adapt to the challenges of the coronavirus pandemic last year. Fortunately, our data system with Google Cloud positioned us to perform quickly and efficiently in the new normal. 

How we use data

We have an end-to-end type of data platform; on one side we work very closely with our product teams to collect the right level of data that they’re interested in, such as which articles people are reading, and how long they’re staying onsite. We frequently measure our audience to understand our user segments, and how they come onsite or use our apps. We then provide that data to analysts for end-to-end analytics. 

On the other side, the newsroom is also focused on audience, and we build tools to help them understand how Google Search or different social promotions play a role in a person’s decision to read The New York Times, and also to get a better sense of their behavior on our pages. With this data, the newsroom can make decisions about information that should be displayed on our homepage or in push notifications. 

Ultimately, we’re interested in behavioral analytics—how people engage with our site and our apps. We want to understand different behavioral patterns, and which factors or features will encourage users to register and subscribe with us. 

We also use data to create or curate preferences around personalization, to ensure we’re delivering to our users fresh content, or content that they may not have normally read. Likewise, our data also gets used in our targeting system, so that we can send out the right messaging about our various subscription packages to the right users.

Choosing to migrate to Google Cloud

When I came to The New York Times over five years ago, our data architecture was not working for us. Our infrastructure was gathering data that proved harder for analysts to crunch on a daily basis. We were also hitting hang ups with how that data was streaming into our system and environment. Back then we’d run a query and then go grab some coffee, hoping that the query would finish or give us the right data by the time we came back to our desks. Sometimes it would, sometimes it wouldn’t.

We realized that Hadoop was definitely not going to be the on-premises solution for us, and that’s when we started talking with the Google Cloud team. We began our digital transformation with a migration to BigQuery, their fully managed, serverless database warehouse. We were under a pretty aggressive migration timeline, focusing first on moving over analytics. We made sure our analysts got a top-of-the-line system that treated them the way that they themselves would want to treat the data. 

One significant prominent requirement in our data architecture choice was to enable analysts to be able to work as quickly as they needed to provide high-quality deliverables for their business partners. For our analysts, the transition to BigQuery was night and day. I still remember when my manager ran his very first query on BigQuery and was ready to go grab his coffee, but the query finished by the time he got up from his chair. Our analysts talk about that to this day.

While we were doing the BigQuery transition, we did have concerns about our other systems not scaling correctly. Two years ago, we weren’t sure we’d be able to scale up to the audience we expected on that election day. We were able to band-aid a solution back then, but we knew we only had two more years to figure out a real, dependable solution. 

During that time, we moved our streaming pipeline over to Google Cloud, primarily using App Engine, which has been a flexible environment that enabled quick scaling changes and requirements as needed. Dataflow and Pub/Sub also played significant roles in managing the data. In Q4 of 2020 we had our most significant traffic ever recorded, at 273 million global readers, and four straight days of the highest traffic we’ve had compared to other election weeks. We were proud to see that there was no data loss.

A couple of years ago, on our legacy system, I was up until three in the morning one night trying to keep data running for their needs. This year, for election night, I relaxed and ate a pint of ice cream because I was able to more easily manage our data environment, allowing us to set and meet higher expectations for data ingestion, analysis and insight among our partners in the newsroom.

How COVID-19 changed our 2020 roadmap 

The coronavirus pandemic definitely wasn’t on my team’s roadmap for 2020, and it’s important to mention here that The New York Times is not fundamentally a data company. Our job is to get the news out to our users every single day in paper, on apps, and onsite. Our newsroom didn’t expect the need to build out a giant coronavirus database that would enrich the news they share every day. 

Our newsroom moves quickly, and our engineers have built one of the most comprehensive datasets on COVID-19 in the U.S. With Google, The New York Times decided to make our data publicly available on BigQuery Google’s COVID-19 public dataset. Check out this webinar for more details on our evolution architecture:https://www.youtube.com/embed/mtNlrFpschU?enablejsapi=1&

Flexible approach

We have many different teams that work within Google Cloud, and they’ve been able to pick from the range of available services and tailor project requirements keeping those tools available in mind.

One challenge we think about with the data platform at The New York Times is determining the priorities of what we build. Our ability to engage with product teams at Google though the Data Analytics Customer Council allows us to see into the BigQuery roadmap, or the data analytics roadmap, and plays a significant role in determining where we focus our own development. For example, we’ve built tools like our Data Reporting API, which reads data directly from BigQuery, in order to take advantage of tools like BigQuery BI Engine. This approach encourages our analysts to be better managers of their domains around dimensions and metrics, but not have to focus on building caching mechanisms of their data. Getting that kind of clarity helps us plan how to build The New York Times in the new normal and beyond.

If you are interested to learn more about the data teams at the New York Times, take a look at our open tech roles here and you’ll find many interesting articles at NYT data blog.

Digital technology offers increasing flexibility and choice to consumers. As a result, the retail industry is dramatically shifting toward more tailored and personalized experiences for shoppers, and businesses are rethinking how they deliver value to customers.

This couldn’t be more true for the beauty retailing industry where leading companies are turning to digital technology to create customized shopping experiences.

At Google Cloud, we’re particularly excited about our work with Ulta Beauty, the largest beauty retailer in the United States with more than 1196 stores in all 50 states, and how the company is using Google Cloud technology solutions to power personalization and redefine beauty retailing.

Established in 1990, Ulta Beauty has had incredible success as a company, and as customers become more discerning and curious about their purchases, the company is finding new ways to meet their changing needs.

Recently, leaders at Ulta Beauty recognized a huge opportunity to complement and enhance the shopping experience by helping beauty enthusiasts navigate through more than 500 brands and 25,000 products carried in their stores and online channel.

They decided to leverage the data from Ulta Beauty’s successful Ultamate Rewards loyalty program to create and offer more unique and personalized user experiences.

With more than 30 million members generating data through sales, transactions, product reviews, and social media engagement, Ulta Beauty’s Loyalty Program creates a comprehensive data set, and the company sought the right technology partner to help organize, analyze and transform that data into valuable insights for its customers.

Ulta Beauty’s leaders knew they had an opportunity to leverage data analytics and machine learning to reach customers in new ways, enhance the guest experience, and continue to grow their active loyalty member base. After considering a number of cloud providers, they chose to expand their existing partnership with Google Cloud.

“Google Cloud listened to our needs and worked in tandem with our engineering team to address our challenges,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “The ease of working with the Google Cloud team and their breadth of experience made the decision a no-brainer, laying the foundation for a great partnership.”  

In 2019, Ulta Beauty announced it was working with Google Cloud Platform to unify and organize its data, using:

• BigQuery to perform data analysis and generate dynamic content, personalized product recommendations, and event-based messages for customers.
• Cloud Storage to provide highly available, secure, resilient and cost-effective access to data across the entire enterprise.
• Compute Engine for the high-performance scalability needed to grow with customer demand while painlessly migrating existing applications to the cloud.
• Anthos to build a hybrid cloud foundation that allows their applications to take advantage of all this data, combining the power and flexibility of GKE with the ability to leverage their existing investment in secure infrastructure on-premises.

Our partnership with Ulta Beauty has enabled increased engagement with customers in store and online, and the creation of new tools and capabilities, including a new Virtual Beauty Advisor tool to deliver tailored recommendations and help shoppers choose the right products, and a Customer Conversation Platform that’s enabling deeper connections with guests, ultimately driving customer loyalty.

“It’s been a really efficient process so far due in part to the ease of working with the Google team,” said Michelle Pacynski, vice president of digital innovation at Ulta Beauty. “They’re experienced, approachable, and their can-do style makes for a great partnership. They listened to our needs and worked in tandem with our engineering team, figuring things out, and getting it done.”