The retail industry is rapidly evolving, with customers demanding exceptional digital experiences, and retailers adjusting their businesses to be more efficient. With many retailers relying on SAP for critical business functions like digital transactions, finance, supply chains and more, this shift has led them to look for ways to modernize these systems, deliver them on highly scalable infrastructure, and extract more value out of the data from within them.  

I’m proud that we are helping German retailers successfully migrate business-critical SAP systems onto Google Cloud, minimizing risk and downtime and ultimately helping them build a foundation for future growth. Our work with retailers like Otto Group, one of the world’s largest ecommerce businesses, MediaMarktSaturn, the large consumer electronics retailer, and METRO, the wholesale retailer with operations across Europe, demonstrates our deep partnership with SAP to support customers’ digital transformations.

Helping Otto Group IT run SAP on secure, sustainable infrastructure

Otto Group, a leading online-retailer and services group in Germany and one of the world’s largest ecommerce companies, migrated their SAP workloads to Google Cloud in order to modernize their SAP landscape and build a more agile environment that would enable them to quickly scale up or down according to business needs. 

The Otto Group, which consistently balances the sustainable use of resources and climate-neutral action with economic growth, is also able to leverage Google Cloud’s clean infrastructure to deliver the bulk of its internal SAP environment, ensuring the company’s business systems are running on secure, sustainable infrastructure. Since 2017, Google has matched 100% of its global electricity use with purchases of renewable energy every year, and is now building on that progress with a new goal of running entirely on carbon-free energy at all times by 2030. 

With Google Cloud, Otto Group has told us they have access to better means of automation and improved network capability compared to what they had with their previous provider. Through this cloud migration, Otto Group IT is providing modern and flexibly scalable SAP systems to their internal customers.

Powering MediaMarktSaturn’s online ecommerce experience 

For MediaMarktSaturn, SAP is critical to the smooth day-to-day running of its business, so the company was keen to ensure maximum availability and stability with a cloud environment. After a thorough examination of its business needs and hands-on support from Google Cloud’s teams, MediaMarktSaturn elected to migrate its SAP HANA database onto Google Cloud, enabling a performance increase of four times compared to its previous on-premises installations.

The SAP suite is critical for the ecommerce platform to run smoothly on a day-to-day basis. By migrating to Google Cloud, the retailer is providing even more support and maximum availability for its customers. With Google Cloud’s industry-specific expertise, MediaMarktSaturn’s customers have access to a reliable and stable ecommerce platform, so they can browse for products online from wherever they are. 

Transforming METRO’s business by running SAP workloads in the cloud 

With more than 97,000 employees in 34 countries, Germany’s METRO is one of the world’s largest B2B wholesalers. Previously, METRO relied on unique finance systems that were different in each country,  and updates or system testing required substantial coordination across numerous teams, which was both time-consuming and costly. 

To address these pain points, METRO is moving away from on premise deployments and is now migrating its SAP S/4HANA finance systems to Google Cloud to support everything from classical role-based accounting to the use of cognitive tools. Now, internal METRO teams can work seamlessly across operations, enhancing the services they provide to customers by addressing demands in real time. 

To read more about how SAP on Google Cloud drives agility, efficiency, and innovation for our customers, visit our solutions page here.

Editor’s note: In this blog post we learn how TapClicks migrated to Google Cloud to offer their marketing customers a unified platform for data management, operations, insights, and analysis.

TapClicks is a smart marketing cloud, powered by data, that unifies our customer’s marketing. By choosing to migrate our core applications last year to Google Cloud, we cut costs, solved data-sharing concerns for our customers, and opened our stack up to a new ecosystem of possibilities. 

The core problem that we’re solving for our customers is how to manage their marketing infrastructures data and operations. Life isn’t easy for marketers now. There are 7,000 different vendors servicing this space today – creating much complexity between digital agencies, media, and brands. Marketers face challenges in navigating all of these systems, logging in and out, understanding pacing goals, and managing the flow of marketing data so they can analyze and report internally as well as to their clients at scale.

We unify omnichannel campaign data (250 API connectors and 6000 Smart Connectors ™ ) from a plethora of marketing sources on an automated data warehousing solution, creating simplicity for organizations. Over 4,000 agencies, media companies, and brands use our Marketing Operations and Data Management Platform, which imports data at scale and creates an automatic data warehouse on Google Cloud. Teams can also leverage TapClicks, like our world class Facebook connector, to import data directly into Google Data Studios.  Beyond importing and storing, we also provide data exporting to other Google solutions like Google Data Studio and Google Sheets.  We also create interactive dashboards that let stakeholders and clients analyze their data, as well as automated, multi-channel reports that go out to clients at specified times. So channel comparisons, optimizations, attribution, and calculations are easily performed.  Some of our customers are able to generate hundreds of thousands of individual reports and dashboards for their clients.

Although we may be best known for our reporting and analytics, we also empower teams managing the marketing operations workflow from customers and internal stakeholders, especially at scale. Our user-friendly, configurable system helps manage their orders and campaigns. Through automation of this process, we deliver tremendous amounts of efficiency, time saving, cost savings, and reduction of errors. The combination of these solutions makes up our unified platform, with additional capabilities like marketing intelligence that offers competitive and brand-level analysis. This is a disruptive solution in use by all leading media companies, agencies and many brands.

Partnering for possibilities

We faced a few challenges with our original tech stack, which included a mix of the leader in web services revenue, leaders in high performance data warehousing, as well as vendors on bare metal servers. 

• One challenge was around costs, which were growing. 
• Second, many of our customers work with multiple brands, and are very hesitant to share their data with the leader in web services, who’s often viewed as their competitor. 
• Third, these vendors are more focused on their own revenue rather than a true long term partnership that would enable their customers to enjoy similar success as they have experienced.

When looking at other cloud providers, Google Cloud emerged for us as the front runner. They were competitive on costs, and their native Kubernetes support was superior— a big selling point for our DevOps team. There’s also a movement in the marketing and advertising industry away from AWS toward Google Cloud because of the data-sharing concern. Finally, most of our customers are already using Google Cloud tools, so there’s brand recognition and familiarity there, and easier integrations with their own systems.

Migrating to Google Cloud

Our migration, which took about five months, involved moving a significant chunk of our infrastructure, including our core applications, using Google Kubernetes Engine (GKE). In our legacy architecture, each of our clients was assigned to one of our virtual machines (VMs), and there was a lot of unused capacity because we had to provision for the max usage. We appreciated GKE’s cloud native capabilities, especially autoscaling, a huge benefit for our web application. We have varying usage patterns during the day, and though our application is mostly used during business hours, there are also days in the month of higher usage, and autoscaling saves us time and costs. GKE also makes deployments much easier, and we anticipate a lot of benefits there for our developer environments. We’ve moved some of our microservices into GKE and plan to move more in the future. All in all, we were able to migrate our core products and the bulk of our AWS spend successfully to Google Cloud. 

We also moved from our other vendors Relational Database Service (RDS) to running MySQL on our own VMs on Google Cloud, which gives us more flexibility in terms of settings and fine tuning. We’re still trying to find the best mix as we’re modernizing our infrastructure, and we took this opportunity to migrate from MySQL 5.7 to 8.0.  

Our next stage is exploring more of the capabilities and services of Google Cloud, including BigQuery, which we’re considering for our own data warehouse. The fact that we could also run Snowflake on Google Cloud, if needed, was another selling point for our migration. 

We’re especially interested in BigQuery ML’s machine learning and natural language processing capabilities, which enabled better predictive insights. Our customers want insights from their campaigns— which are working, which are paying off, where should they invest next? Using our platform, they’re looking not only to generate reporting, but also identify opportunities to improve campaign performance. We plan to use AI and ML to improve those capabilities, so that our customers can seamlessly unlock insight and intelligence from their marketing data and campaigns.

Double-clicking on Google Cloud

For us, being able to deeply leverage and partner with Google Cloud to deliver those solutions on a single stack is critical, and we think our customers will love it. We see TapClicks and Google Cloud partnering at a level beyond what you typically see in a cloud provider relationship. Already, fifty percent of our company is working with various Google Cloud solutions, and we envision TapClicks and Google Cloud as extensions of each other, providing a single, powerful platform solution. 

Google Cloud understands the partnership concept, and their team was able to shine a light on their services and what they could bring to the table. Compared to our previous experiences, dealing with the Google Cloud team has been a true pleasure. Now that we’ve migrated, we’re ready to take our next steps into the services available to us in the Google Cloud ecosystem, and the problems we’ll continue to solve for our customers. Learn more about TapClicks and BigQuery ML.

We recently launched Vertex AI to help you move machine learning (ML) from experimentation into production faster and manage your models with confidence—speeding up your ability to improve outcomes at your organization.

But we know many of you are just getting started with ML and there’s a lot to learn! In tandem with building the Vertex AI platform, our teams are dropping as much best practices content as we can to help you come up to speed. Plus, we have a dedicated event on June 10th, Applied ML Summit, with sessions on how to apply ML technology in your projects, as well as grow your skills in this field. 

In the meantime, we couldn’t resist a quick lesson on hyperparameter tuning, because (a) it’s incredibly cool (b) you will impress your coworkers (c) Google Cloud has some unique battle tested tech in this area and (d) you will save time by getting better ML models into production faster. Vertex Vizier, on average, finds optimal parameters for complex functions in over 80% fewer trials than traditional methods. 

So it’s incredibly cool, but what is it?

While machine learning models automatically learn from data, they still require user-defined knobs which guide the learning process. These knobs, commonly known as hyperparameters, control, for example, the tradeoff between training accuracy and generalizability.  Examples of hyperparameters are the optimizer being used, its learning rate, regularization parameters, the number of hidden layers in a DNN, and their sizes.

Setting hyperparameters to their optimal values for a given dataset can make a huge difference in model quality. Typically, optimal hyperparameter values are found via grid searching a small number of combinations, or tedious manual experimentation. Hyperparameter tuning automates this work for you by searching for the best configuration of hyperparameters for optimal model performance. 

Vertex Vizier enables automated hyperparameter tuning in several ways:

1. “Traditional” hyperparameter tuning: by this we mean finding the optimal value of hyperparameters by measuring a single objective metric which is the output of an ML model.  For example, Vizier selects the number of hidden layers and their sizes, an optimizer and its learning rate, with the goal of maximizing model accuracy.
2. When hyperparameters are evaluated, models are trained and evaluated on splits of the data set. If evaluation metrics are streamed to Vizier (e.g. as a function of epoch) as the model is trained, Vizier’s early stopping algorithms can predict the final objective value, and recommend which unpromising trials should be early stopped. This conserves compute resources and speeds up convergence.
3. Oftentimes, models are tuned sequentially on different data sets. Vizier’s built in transfer learning learns priors from previous hyperparameter tuning studies, and leverages them to converge faster on subsequent hyperparameter tuning studies.
4. AutoML is a variant of #1, where Vertex Vizier performs both model selection, and also tunes architectures/non-architecture modifying hyperparameters. AutoML usually requires more code on top of Vertex Vizier (to ingest data etc), but Vizier is in most cases the “engine” behind the process. AutoML is implemented by defining a tree like (DAG) search space, rather than a “flat” search space (like in #1). Note that you can use DAG search spaces for any other purpose where searching over a hierarchical space makes sense.
5. There are times when you may wish to optimize more than one metric. For example, we would like to optimize model accuracy, while minimizing model latency. Vizier can find the Pareto frontier, which presents tradeoffs for multiple metrics, allowing users to choose the appropriate tradeoff. Simple example: I want to make a more accurate model, but would like to minimize serving latency. I do not know ahead of time what’s the tradeoff between the two metrics. Vizier can be used to explore and plot a tradeoff curve, so users can select on the most appropriate one. For example, “a latency decrease of 200ms will only decrease accuracy by 0.5%”

Google Vizier is all yours with Vertex AI

Google published the Vizier research paper in 2017, sharing our work and use cases for black-box optimization—i.e. The process of finding the best settings for a bunch of parameters or knobs when you can’t peer inside a system to see how well the knobs are working. The paper discusses our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning that the service provides. Vizier has been essential to our progress with machine learning at Google, which is why we are so excited to make it available to you on Vertex AI.

Vizier has already tuned millions of ML models at Google, and its algorithms are continuously improved for faster convergence and handling of real-life edge cases. Vertex Vizier’s models are very well calibrated and are self-tuning (they adapt to user data), and offer unique power features, such as hierarchical search spaces and multi-objective optimization. We believe Vertex Vizier’s set of features is a unique capability to Google Cloud, and look forward to optimizing the quality of your models by automatically tuning hyperparameters for you.

To learn more about Vertex Vizier, check out these docs and if you are interested in what’s coming in machine learning over the next five years, tune in to our Applied ML Summit on June 10th, or watch the sessions on demand in your own time.

The capability gap between hyperscale cloud providers has begun to narrow; however, fierce competition for enterprise workloads extends to secondary markets worldwide. Infrastructure and operations leaders should evaluate cloud providers with a broad range of use cases and a wide market presence.

Market Definition/Description

Cloud computing is a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using internet technologies. Cloud infrastructure and platform services (CIPS) are defined as standardized, highly automated offerings, in which infrastructure resources (e.g., compute, networking and storage) are complemented by integrated platform services. These include managed application, database and functions as-a-service offerings. The resources are scalable and elastic in near-real time and are metered by use. Self-service interfaces are exposed directly to the customer, including a web-based user interface (UI) and an API. The resources may be single-tenant or multitenant, and can be hosted by a service provider or on-premises in the customer’s data center.The scope of this Magic Quadrant has changed, compared with its predecessor, the “Magic Quadrant for Cloud Infrastructure as a Service.” Gartner has developed this Magic Quadrant to reflect the changing dynamics of cloud services offered and the ways that enterprise customers adopt them. Ultimately, hyperscale cloud providers, and the broad array of services they offer beyond infrastructure as a service (IaaS), have found strategic importance in Gartner’s enterprise clients and the Magic Quadrant needed to evolve to reflect as much.The scope of the Magic Quadrant for CIPS includes IaaS and integrated platform as a service (PaaS) platforms. These include application PaaS (aPaaS), functions as a service (FaaS), database PaaS (dbPaaS), application developer PaaS (adPaaS) and industrialized private cloud offerings that are often deployed in enterprise data centers.

Understanding the Vendor Profiles, Strengths and Cautions

CIPS providers that target enterprise and midmarket customers generally offer high-quality service, with excellent availability, good performance, high security and good customer support. Exceptions will be noted in this Magic Quadrant’s evaluations of individual providers. When we say “all providers,” we specifically mean “all the evaluated providers included in this Magic Quadrant,” not all CIPS providers in general. Keep the following in mind when reading the vendor profiles:

• All the providers have public cloud IaaS and PaaS offerings. Most also offer, or are in the process of building, industrialized private cloud offerings, in which every customer is on standardized infrastructure and cloud management tools. In some cases, the provider’s industrialized, on-premises offering may share similarities to hyperconverged infrastructure (HCI), but tethered to the cloud. However, this may not resemble the provider’s public cloud service in architecture or quality. A single architecture and feature set and cross-cloud management, for both public and private CIPS, make it easier for customers to combine and migrate across service models as their needs dictate. They also enable the provider to use its engineering investments more effectively. Gartner is beginning to describe the notion of cloud-provider-managed infrastructure, wherever it may exist, as “ distributed cloud.”
• All the providers target midmarket businesses and enterprises, as well as other companies that use technology at scale. Some of the providers may also target small businesses and startups. Just because a provider targets a segment, however, does not necessarily mean that it is well-suited to that segment’s needs. Furthermore, not all providers have the capacity to serve very-large-scale customers, and some have capacity constraints in particular regions.
• All the providers offer basic cloud IaaS — compute, storage and networking resources as a service. They also offer additional value-added capabilities, notably cloud software infrastructure services — typically middleware and databases as a service — including PaaS capabilities. These services, along with IT operations management (ITOM) capabilities as a service (especially DevOps-related services), are a vital differentiator in the market, especially for Mode 2 agile IT buyers.
• All the providers claim to have high security standards. However, the extent of the security controls provided to customers varies significantly. All the providers evaluated can offer solutions that will meet common regulatory compliance needs, unless otherwise noted. All the providers have undergone SOC 1, SOC 2 and SOC 3 audits, as well as SSAE 16, ISO/IEC 27001, ISO/IEC 27017 and ISO/IEC 27018 audits. This provides a relatively high level of assurance that the providers are adhering to generally accepted practices for the security of their systems, but it does not address the extent of controls offered to customers.
• Security is a shared responsibility. Customers need to correctly configure controls, and they may need to supply additional controls beyond what their providers offer. Furthermore, providers vary in their degree of transparency as to how services are architected, although customers typically have access to third-party assessment reports under a nondisclosure agreement (NDA).

• Monthly compute availability service-level agreements (SLAs) of 99.95% and higher are generally the norm. They are typically higher than availability SLAs for managed hosting. Service credits for outages in a given month are typically capped at 100% of the monthly bill; however, some providers have caps as low as 25%. This availability percentage is typically non-negotiable, because it is based on an engineering estimate of the underlying infrastructure reliability.
• Single-instance compute SLAs have become common for providers in this Magic Quadrant. It might be more accurate to say that there are usually two SLAs — one for the compute service, and one for individual instances. Some providers have a compute availability SLA that requires customers to use compute capabilities in at least two fault domains (sometimes known as “availability zones” or the like).
• Many providers have additional SLAs. These cover network availability and performance, customer service responsiveness and other service aspects.
• Infrastructure resources are not normally automatically replicated into multiple data centers. Customers are responsible for their own business continuity. Some providers offer optional disaster recovery solutions.
• All providers offer per-second metering of virtual machines (VMs). Some can offer shorter metering increments, which can be more cost-effective for short-term batch jobs. Unless otherwise noted, providers charge on a per-VM basis.
• Providers are increasingly offering bare-metal physical servers on a dynamic basis. These are priced by the second. Providers with a bare-metal option are noted as such.
• All the providers partner with carrier-neutral colocation exchanges. This enables customers to obtain connectivity from a variety of carriers that are located in these facilities. In addition, many customers require a small amount of supplemental colocation in low-latency proximity with their cloud provider. For example, they may have a large-scale database, specialized network equipment or legacy equipment, such as a mainframe.
• Some providers offer software marketplaces. In these marketplaces, software vendors specially license and package their software to run on that provider’s cloud IaaS offering. Marketplace software can be automatically installed, and can be billed through the provider, although the software vendor often provides support.
• All providers offer enterprise-class support with 24/7 customer service. This is provided via phone, email and chat, along with an account manager. Some offer a lower level of support, but allow customers to pay extra for enterprise-class support.
• All the providers will sign contracts with customers, can invoice and can consolidate bills from multiple accounts. All providers offer online sign-up and credit card billing, because they recognize that enterprise buyers prefer contracts and invoices. Some will sign “zero dollar” contracts that do not commit a customer to a certain volume.
• Some providers will sign a U.S. Health Insurance Portability and Accountability Act Business Associate Agreement (HIPAA BAA).
• Unless otherwise noted, all providers will sign the following contract addendums:
  • An EU Data Protection Directive (95/46/EC) data-processing agreement (DPA), which includes the model clauses
  • An EU General Data Protection Regulation (GDPR) DPA
• Managed and professional services are an optional but important accelerator for customer success. Almost all providers rely heavily on managed service providers (MSPs) and system integration (SI) partners for these services. However, most providers offer their own first-party professional services and some also offer first-party managed services offerings.
• All of the evaluated providers offer a portal, documentation, technical support, customer support and contracts in English. Some can provide one or more of these in languages other than English. Most providers can conduct business in local languages.

The service provider descriptions are accurate as of the time of publication. Our technical evaluation of service features took place between January 2020 and March 2020.

Format of the Vendor Descriptions

When describing each provider, we first summarize the nature of the company, then provide information about its industrialized cloud IaaS offerings in the following format:

• Locations: Cloud data center locations by country, languages in which the company does business and languages in which technical support can be conducted.
• Recommended Uses: These are the circumstances under which we recommend the provider. They are not the only circumstances in which it may be a useful provider, but they are the scenarios for which, in Gartner’s opinion, the provider is well-suited.

For a detailed technical description of CIPS offerings, along with a use-case-focused technical evaluation, see “Critical Capabilities for Cloud Infrastructure and Platform Services, Worldwide.”We also provide a detailed list of evaluation criteria in “Solution Criteria for Cloud Integrated IaaS and PaaS.” A detailed assessment of each provider against these criteria can be found in the Solution Scorecards. The results are also available in Gartner’s Cloud Decisions portal (see “Cloud Decisions’ Cloud Compare: Perform Real-Time IaaS Pricing and Performance Analysis”).

Magic Quadrant

Figure 1. Magic Quadrant for Cloud Infrastructure and Platform Services

Vendor Strengths and Cautions

Google

Google is a Leader in this Magic Quadrant.

Locations: Google has multiple regions across Japan and the U.S., as well as a presence in Belgium, Singapore, Finland, Germany, the Netherlands, the U.K., India, Australia, Brazil, Canada and, Switzerland, as well as the Hong Kong and Taiwan markets.

Recommended Uses: Google has evolved by enhancing its strengths and attacking its limitations to providing a strong offering in every use case, other than the edge use case. Google has a future focus on building out hybrid capabilities and partnerships with telco providers.

Strengths

• Google’s open-source contributions, such as Kubernetes and TensorFlow, have been market-moving innovations that have changed the course of enterprise IT. Such innovations have served to enable other cloud service providers, but also brought developer “mind share” to Google Cloud Platform (GCP). Google’s long-term strategy is to bring additional open-source-focused partners into GCP as managed services.
• During the past year, GCP has experienced a noticeable increase in year-over-year market share in terms of IaaS and dbPaaS, albeit from a lower base, relative to other providers in this Magic Quadrant. Google has also made significant gains by closing a number of critical capability gaps between GCP and Microsoft Azure, its nearest competitor in terms of market share and capabilities.
• Gartner clients continue to associate GCP with its big data and data science capabilities, stemming from the use of services such BigQuery and Dataproc. However, the company is pressing into new territory with Anthos, GCP’s container and Kubernetes-based middleware layer, which is designed to support the development and deployment of cloud applications in a hybrid and multicloud model.

Cautions

• Some of Gartner’s clients remain cautious about Google’s commitment to serving the needs of enterprise clients when put in the context of SAP’s preference for Microsoft Azure, and GCP’s slowness in executing on some highly touted partnerships. GCP lacks enterprise-focused aPaaS capabilities and support for Oracle, and it continues to struggle with having an enterprise mindset in the field.
• From a financial perspective, GCP’s revenue is a small fraction of overall Google revenue and GCP’s criticality to the overall business is not as clear as its competitors. Furthermore, GCP’s success may erode the company’s overall healthy gross margins.
• Google’s much-vaunted network capabilities have been the source of a number of GCP outages during the last year, with devastating impact on customers. One outage was multiregional in scope, affecting GCP customers and Google consumer services, such as G Suite and YouTube. This resulted in complete GCP network unavailability for some customers.