SAP forms the critical backbone of thousands of enterprises, supporting critical business functions such as finance, supply chain, warehouse management, and more. Google Cloud provides a highly scalable and resilient infrastructure to run such workloads and offers tools, such as Smart Analytics and Machine Learning that can accelerate your organization’s digital transformation. 

In fact, a recent study by Forrester found that running SAP on Google Cloud can generate a 160% return on investment and a payback period of six months or less, thanks to legacy infrastructure cost savings, downtime avoidance, and productivity improvements.

How you deploy your SAP systems across your network has a tremendous impact on its availability, resilience, and performance. In addition to separate production and high-availability (HA) environments, SAP deployments typically include sandbox, development, quality assurance (QA), and disaster recovery environments as well. 

Because most of the Google network is virtual, SAP administrators can easily design complex landscapes that suit your organization’s SAP deployment and organizational structure while also meeting security and operational requirements. 

As you get started with SAP on Google Cloud, you’ll need to decide how to configure your networking to ensure the availability and performance of various SAP systems. Here’s a look at your options.

VPC and shared VPC

A virtual private cloud (VPC) is a secure, isolated private network hosted within Google Cloud. VPCs are global in Google Cloud, so a single VPC can span multiple regions without communicating across the public internet. Similarly, subnets can span across zones within a region. A zone represents a single failure domain, so typical SAP deployments place production and HA systems in different zones to ensure resiliency. Google Cloud simplifies this type of deployment, because subnets containing both production and HA systems can span multiple zones.  

This capability also simplifies SAP clustering, since the cluster’s virtual IP (VIP) address can be in the same range as those of the production and HA machines. This configuration shields the floating IP using Google internal load balancers and is applicable to HA clustering of the application layer (ASCS and ERS) and the HANA database layer (HANA Primary and Secondary).

Shared VPCs are a feature unique to Google Cloud that allows an organization to connect resources from multiple projects to a common VPC network. This lets them communicate with each other securely and efficiently using internal IPs. You can also centrally control the network for all SAP projects (service) from the Host project while using firewalls to inspect communication between compute engines in the same subnet, and between those in different subnets. (Best practice is to limit the communication between these systems to only the required ports — typically via SAP remote function call (RFC) communication at Layer 4.)

When designing your network, start with a host project containing one or more Shared VPC networks. You can attach additional service projects to a host project, which allows them to participate in the Shared VPC. It’s common practice to have multiple service projects operated and administered by various departments or teams in your organization.

Depending on your needs, you can deploy SAP on a single Shared VPC or multiple ones. The two scenarios differ in terms of network control, SAP environment isolation, and network inspection. Let’s look more closely at these differences.

Scenario 1: Deploying SAP on a single Shared VPC

If you require only a single network inspection, deploying SAP on a single Shared VPC has the advantage of simplicity and reduces administrative overhead.

• Network control: The Shared VPC serves as the network hub, allowing central network management based on identity access management (IAM) roles for the network team(s) in both production and non-production environments.
• SAP environment isolation: You can create projects and subnets for each SAP environment. Projects help group resources together for finer IAM control and billing visibility, while subnets provide network isolation for individual SAP environments. In service projects, compute engines can communicate by default; however, you can adopt simple firewall rules to block communication between compute engines within a subnet or in separate subnets.
• Network inspection: Use Google Cloud firewalls to allow only the required ports for communication between SAP systems. Leverage network tags and service accounts to define granular control for both north-south and east-west traffic.

Scenario 2: Multiple Shared VPCs for SAP deployment

In scenarios requiring additional network inspections, you can create multiple Shared VPCs, typically one per environment. Use peering between these Shared VPCs to enable RFC communication among the SAP development, QA, and production systems.

• Network isolation: Multiple Shared VPCs are completely isolated from each other except via specific ports opened in Google Cloud firewalls. This allows additional East-West traffic inspection by a Network Virtual Appliance (NVA) within a Google Cloud network. 
• Network control:As the number of Shared VPCs increases, activities such as peering and firewall policies also increase. This diminishes the central network control that Shared VPCs offer, so the network team should plan to manage the policies in each VPC separately.

Hybrid scenarios – for example, one Shared VPC for the production environment and one Shared VPC for all non-production systems — are also possible. This arrangement allows network inspection between production and non-production systems, and limits the number of central network administration layers to two.

Configuring the networking environment for multiple SAP systems can be a complex process. Thanks to Google Cloud’s Shared Virtual Clouds and other tools, SAP administrators can create scalable, secure networks that provide logic, resilience, and visibility to their cloud deployments.Learn more about these networking capabilities and our full offerings for SAP customers.

Editor’s note: The post is part of a series highlighting our awesome partners, and their solutions, that are Built with BigQuery

In the field of producing engaging video content such as ads, many marketers ignore the power of data to improve their creative efforts to meet the consumers’ need for personalized messages. The demand for creative tech to efficiently personalize is real as marketers need personalized video Ads to reach their audience with the right message at the right time. Data, Insights and Technology are the main ingredients to deliver this value while ensuring security and privacy requirements are met. The Connected-Stories team partnered with Google Cloud to build a platform for Ad personalization. Google Data Cloud and BigQuery are at the forefront to assimilate data, leverage ML models, create personalized ads, and capitalize on real-time intelligence as the core features of the Connected-Stories NEXT platform.

Connected-Stories NEXT is an end-to-end creative management platform to develop, serve, and optimize interactive video and display ads that scale across any channel. The platform ingests first-party data to create custom ML models, measure numerous third-party data points to help brands develop unique customer journeys and create videos that their data signals can drive. An intelligent feedback loop passes real-time data back, enabling brands to make data-driven and actionable video ads that take the brand’s campaigns to the next level.

The core use case of the NEXT platform revolves around collecting user’s interaction data and optimizing for precision and speed to create an actionable Ad experience that is personalized for each user. The platform processes complex data points to create interactive data visualizations that allow for accurate analysis. The platform uses Vertex AI to access managed tools, workflows, and infrastructure to build, deploy, and scale ML models that have improved the accuracy to identify segments for further analysis.

The platform ingests 200M data events with peaks and valleys of activity. These events are processed to generate dashboards that enable users to visualize metrics based on filters in real-time. These dashboards have high performance requirements in terms of a responsive user interface under constantly changing data dimensions.

Google Cloud’s serverless stack coupled with limitless data cloud infrastructure has been the core to the NEXT platform’s data-driven innovation. The growing volume of data ingested, streamed and processed were scaled uniformly across the compute, storage and analytical layers of solution. A lean development team at Connected-Stories were able to focus all-in on the solution, while the serverless stack scaled, lowered attack service in terms of security and optimized the cost footprint through pay-as-you-go features.

BigQuery has been the backbone to support the vast amounts of data spreading over multiple geos resulting in workloads running at petabyte scale. BigQuery’s fully managed serverless architecture, real-time streaming, built-in machine learning and rich business intelligence capabilities distinguishes itself from a cloud data warehouse. It is the foundation needed to approach data and serve users in an unlimited number of ways. For an application with zero tolerance for failure, given its fully managed nature, BigQuery handles replication, recovery, data distributed optimization and management.

The platform’s requirements include the need for low maintenance, constantly ingesting and refreshing data and smart-tuning of aggregated data. These capabilities can be implemented by BigQuery’s materialized views feature. Materialized views are useful for precomputed views that regularly cache query results for better performance. These views possess the innate feature to read only the delta change from base tables and calculate the up-to-date aggregations. Materialized views impart faster outputs and consume fewer resources while reducing the cost footprint.

Some key considerations in using Google cloud and focusing on the Serverless stack include: quick onboarding to development, prototyping in short sprints and ease of preparing data in a rapidly changing environment. Typical considerations around low code / no code include data transformation, aggregation and reduced deployment time. These considerations are fulfilled through using serverless capabilities within Google Cloud such as PubSub, Cloud Storage, Cloud Run, Cloud Composer, Dataflow and BigQuery as described in the Architecture diagram below. The use of each of these components and services are described below.

1. Input/Ingest: At a high-level, microservices hosted in Cloud Run collect and aggregate incoming Ads events.
2. Enrichment: The output of this stage is a Pub-Sub message enriched with more attributes based on a pre-configured campaign.
3. Store: a Cloud Dataflow streaming job to create text files in Cloud Storage buckets.
4. Trigger: Cloud Composer triggers the spark jobs based on text files to process and group them to produce desired output as one record per impression, a logical group of events.
5. Deploy: Cloud Build is then used to automate all deployments.

Thus far, all Google cloud managed services work together to ingest, store and trigger the orchestration, all of which are scalable based on configurations including autoscaling capabilities.

1. Visualization: A visualization tool reads data from BigQuery to compute pre-aggregations required for each dashboard.
2. Data Model Evolution considerations: Though the solution served the purpose of creating pre-aggregations, as the data model evolved by adding a column or creating a new table, it led to recreating pre-aggregations and querying the data again. Alternatively, creating aggregate tables as an extra output of current ETLs seemed like a viable option. However, this would increase the cost and complexity of jobs. A similar situation to reprocess or update aggregated tables would occur as data is updated.

Precomputed views of data that is periodically cached are critical to reach the audience with the right message at the right time.

1. Performance: In order to increase the performance of the platform, we need to have regularly precomputed views of the data, cached .
2. Materialized Views: Consumers of these views needed faster response times, to consume fewer resources and output only the changes in comparison to a base table. BigQuery Materialized views were used to solve this very requirement. Materialized views have been highly leveraged to optimize the design resulting in lesser maintenance and access to fresh data with high performance with a relatively low technical investment in creating and maintaining SQL code.
3. Dashboards: Application dashboards pointing to the Materialized views are highly performant and provide a view into fresh data.
4. Custom Reports with Vertex AI Notebooks: Vertex AI notebooks directly read data from BigQuery to produce custom reports for a subset of customers. Vertex AI has been hugely beneficial to data analysts, where an environment with pre-installed libraries simplifies the readiness to use. Vertex AI Workbench notebooks are used to share these reports within the team allowing them to work always on the cloud without having the need to download data at any time. Besides, it increases the velocity to develop and test ML models faster.

The NEXT platform has yielded benefits such as customers having the ability to create unique consumer journeys powered by AI / ML personalization triggers, using first-party data and business intelligence tools to capitalize on real-time creative intelligence, which is a dashboard to measure campaign performance for cross-functional teams to analyze the impact of Ad content experience at a granular level. All of these while ensuring controlled access to data to enrich data without moving across clouds. The NEXT platform can keep up with increased demands for agility, scalability and reliability through the underlying usage of Google Cloud.

Partnering with Google, in the context of the Google Built with BigQuery program has surfaced the differentiated value in areas of creating interactive personalized Ads by using real-time data. In addition, by sharing this data across organizations as assets, ML models have fueled higher levels of innovation. Connected-Stories plan to deepen the penetration into the entire spectrum of services offered in the AI/ML area to enhance core functionality and provide newer capabilities to the platform.

Click here to learn more about Connected-Stories NEXT Platform capabilities.

The Built with BigQuery Advantage for ISVs

Through Built with BigQuery, launched in April ‘22 as part of Google Data Cloud Summit, Google is helping tech companies like Connected-Stories co-innovate in building applications that leverage Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:

• Get started fast with a Google-funded, pre-configured sandbox.
• Accelerate product design and architecture through access to designated technical experts from the ISV Center of Excellence who can share insights from key use cases, architectural patterns, and best practices encountered in the field.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

The Google Data Cloud spectrum of products and specifically BigQuery give ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge and expanding partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in and exercise choice.

We thank the Google Cloud and Connected-Stories team members who co-authored the blog: Connected-Stories: Luna Catini, Marketing Director, Google: Sujit Khasnis, Cloud Partner Engineering

Today, ML-driven innovation is fundamentally transforming computing, enabling entirely new classes of internet services. For example, recent state-of-the-art lage models such as PaLM and Chinchilla herald a coming paradigm shift where ML services will augment human creativity. All indications are that we are still in the early stages of what will be the next qualitative step function in computing. Realizing this transformation will require democratized and affordable access through cloud computing where the best of compute, networking, storage, and ML can be brought to bear seamlessly on ever larger-scale problem domains.

Today’s release of MLPerf™ 2.0 results from the MLCommons® Association highlights the public availability of the most powerful and efficient ML infrastructure anywhere. Google’s TPU v4 ML supercomputers set performance records on five benchmarks, with an average speedup of 1.42x over the next fastest non-Google submission, and 1.5x vs our MLPerf 1.0 submission. Even more compelling — four of these record runs were conducted on the publicly available Google Cloud ML hub that we announced at Google I/O. ML Hub runs out of our Oklahoma data center, which uses over 90% carbon-free energy.

Let’s take a closer look at the results.

Performance at scale…and in the public cloud

Our 2.0 submissions1, all running on TensorFlow, demonstrated leading performance across all five benchmarks. We scaled two of our submissions to run on full TPU v4 Pods. Each Cloud TPU v4 Pod consists of 4096 chips connected together via an ultra-fast interconnect network with an industry-leading 6 terabits per second (Tbps) of bandwidth per host, enabling rapid training for the largest models.

Hardware aside, these benchmark results were made possible in no small part by our work to improve the TPU software stack. Scalability and performance optimizations in the TPU compiler and runtime, including faster embedding lookups and improved model weight distribution across the TPU pod, enabled much of these improvements, and are now widely available to TPU users. For example, we made a number of performance improvements to the virtualization stack to fully utilize the compute power of both CPU hosts and TPU chips to achieve peak performance on image and recommendation models. These optimizations reflect lessons from Google’s cutting-edge internal ML use cases across Search, YouTube, and more. We are excited to bring the benefits of this work to all Google Cloud users as well.

Translating MLPerf wins to customer wins

Cloud TPU’s industry-leading performance at scale also translates to cost savings for customers. Based on our analysis summarized in Figure 3, Cloud TPUs on Google Cloud provide ~35-50% savings vs A100 on Microsoft Azure (see Figure 3). We employed the following methodology to calculate this result:2

We compared the end-to-end times of the largest-scale MLPerf submissions, namely ResNet and BERT, from Google and NVIDIA. These submissions make use of a similar number of chips — upwards of 4000 TPU and GPU chips. Since performance does not scale linearly with chip count, we compared two submissions with roughly the same number of chips.

To simplify the 4216-chip A100 comparison for ResNet vs our 4096-chip TPU submission, we made an assumption in favor of GPUs that 4096 A100 chips would deliver the same performance as 4216 chips.

For pricing, we compared our publicly available Cloud TPU v4 on-demand prices ($3.22 per chip-hour) to Azure’s on-demand prices for A1003 ($4.1 per chip-hour). This once again favors the A100s since we assume zero virtualization overhead in moving from on-prem (NVIDIA’s results) to Azure Cloud.

The savings are especially meaningful given that real-world models such as GPT-3 and PaLM are much larger than the BERT and ResNet models used in the MLPerf benchmark: PaLM is a 540 billion parameter model, while the BERT model used in the MLPerf benchmark has only 340 million parameters — a 1000x difference in scale. Based on our experience, the benefits of TPUs will grow significantly with scale and make the case all the more compelling for training on Cloud TPU v4.

Have your cake and eat it too — a continued focus on sustainability

Performance at scale must take environmental concerns as a primary constraint and optimization target. The Cloud TPU v4 pods powering our MLPerf results run with 90% carbon-free energy and a Power Usage Efficiency of 1.10, meaning that less than 10% of the power delivered to the data center is lost through conversion, heat, or other sources of inefficiency. The TPU v4 chip delivers 3x the peak FLOPs per watt relative to the v3 generation. This combination of carbon-free energy and extraordinary power delivery and computation efficiency makes Cloud TPUs among the most efficient in the world.4

Making the switch to Cloud TPUs

There has never been a better time for customers to adopt Cloud TPUs. Significant performance and cost savings at scale as well as a deep-rooted focus on sustainability are why customers such as Cohere, LG AI Research, Innersight Labs, and Allen Institute have made the switch. If you are ready to begin using Cloud TPUs for your workloads, please fill out this form. We are excited to partner with ML practitioners around the world to further accelerate the incredible rate of ML breakthroughs and innovation with Google Cloud’s TPU offerings.

1. MLPerf™ v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 2.0-2010, 2.0-2012, 2.0-2098, 2.0-2099, 2.0-2103, 2.0-2106, 2.0-2107, 2.0-2120. The MLPerf name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use is strictly prohibited. See www.mlcommons.org for more information.
2. MLPerf v1.0 and v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 1.0-1088, 1.0-1090, 1.0-1092, 2.0-2010, 2.0-2012, 2.0-2120.
3. ND96amsr A100 v4 Azure VMs, powered by eight 80 GB NVIDIA Ampere A100 GPUs (Azure’s flagship Deep Learning and Tightly Coupled HPC GPU offering with CentOS or Ubuntu Linux) is used for this benchmarking
4. Cost to train is not an official MLPerf metric and is not verified by MLCommons Association. Azure performance is a favorable estimate as described in the text, not an MLPerf result. Computations are based on results from MLPerf v2.0 Training Closed. Retrieved from https://mlcommons.org/en/training-normal-20/ 29 June 2022, results 2.0-2012, 2.0-2106, 2.0-2107.

Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities. 

However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features. 

That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones. 

With Intelligent Products Essentials, manufacturers can:

• Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
• Manage and update products over-the-air: Deploy updates to products in the field,  gather performance insights and evolve capabilities over time with monetization opportunities.
• Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.

In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:

• Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
• Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
• Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
• AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.

Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.

Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve. 

“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”

Customers adopting Intelligent Products Essentials

GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:

• Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
• Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
• Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.

“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&

Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences. 

“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1. 

Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.

“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap

Intelligent Product Essentials is available today. To learn more, visit our website.