Over the past few years, many organizations have experienced the benefits of migrating their SAP solutions to Google Cloud. But this migration can do more than reduce IT maintenance costs and make data more secure. By leveraging BigQuery, SAP customers can complement their SAP investments and gain fresh insights by consolidating enterprise data and easily extending it with powerful datasets and machine learning from Google. 

BigQuery is a leading cloud data warehouse, fully managed and serverless, and allows for massive scale, supporting petabyte-scale queries at super-fast speeds. It can easily combine SAP data with additional data sources, such as Google Analytics or Salesforce, and its built-in machine learning lets users operationalize machine learning models using standard SQL — all at a comparatively low cost. 

If your SAP-powered organization is looking to supercharge its analytics with the strength of BigQuery, read on for considerations and recommendations for modeling with SAP data. These guidelines are based on our real-world implementation experience with customers and can serve as a roadmap to the analytics capabilities your business needs.

Considerations for data replication 

Like most technology journeys, this one should start with a business objective. Keeping your intended business value and goals in mind is critical to making the right decisions in the early steps of the design process.

When it comes to replicating the data from an SAP system into BigQuery, there are multiple ways to do it successfully. Decide which method will work best for your organization by answering these questions:

• Does your business need real-time data? Will you need to time travel into past data?
• Which external datasets will you need to join with the replicated data?
• Are the source structures or business logic likely to change? Will you be migrating the SAP source systems any time soon? For instance, will you be moving from SAP ECC to SAP S/4HANA?

You’ll also need to determine whether replication should be done on a table-by-table basis or whether your team can source from pre-built logic. This decision, along with other considerations such as licensing, will influence which replication tool you should use.

Replicating on a table-by-table basis
Replicating tables, especially standard tables in their raw form, allows sources to be reused and ensures more stability of the source structure and functional output. For example, the SAP table for sales order headers (VBAK) is very unlikely to change its structure across different versions of SAP, and the logic that writes to it is also unlikely to change in a way that affects a replicated table. 

Something else to consider: Reconciliation between the source system and the landing table in BigQuery is linear when comparing raw tables, which helps avoid issues in consolidation exercises during critical business processes, such as period-end closing. Since replicated tables aren’t aggregated or subject to process-specific data transformation, the same replicated columns can be reused in different BigQuery views. You can, for instance, replicate the MARA table (the material master) once and use it in as many models as needed. 

Replicating pre-built logic
If you replicate pre-built models, such as those from SAP extractors or CDS views, you don’t need to build the logic in BigQuery, since you’re using existing logic. Some of these extraction objects have embedded delta mechanisms, which may complement a replication tool that can’t handle deltas. This will save initial development time, but it can also lead to challenges if you create new columns, or if customizations or upgrades change the logic behind the extraction. 

It’s also important to note that different extraction processes may transform and load the same source columns multiple times, which creates redundancy in BigQuery and can lead to higher maintenance needs and costs. However, replicating pre-built models may still be a good choice, since doing so can be especially useful for logic that tends to be immutable, such as flattening a hierarchy, or logic that is highly complex.

How you approach replication will also depend on your long-term plans and other key factors — for example, the availability (and curiosity) of your developers, and the time or effort they can put into applying their SQL knowledge to a new data warehouse. 

With either replication approach, bear in mind when designing your replication process that BigQuery is meant to be an append-always database — so post-processing of data and changes will be required in both cases. 

Processing data changes

The replication tool you choose will also determine how data changes are captured (known as CDC – change data capture). If the replication tool allows for it (for example as SAP SLT does) the same patterns described in the CDC with BigQuery documentation also apply to SAP data. 

Because some data, like transactions, are known to be less static than others (e.g., master data), you need to decide what should be scanned in real time, what will require immediate consistency, and what can be processed in batches to manage costs. This decision will be based on the reporting needs from the business.

Consider the SAP table BUT000, containing our example master data for business partners, where we have replicated changes from an SAP ERP system:

In an append-always replication in BigQuery, all updates are received as new records. For example, deleting a record in the source will be represented as a new record in BigQuery with a deletion flag. This applies to whether the records are coming from raw tables like BUT000 itself or pre-aggregated data, as from a BW extractor or a CDS view.

Let’s take a closer look at data coming particularly from the partners “LUCIA” and “RIZ”. The operation flag tells us whether the new record in BigQuery is an insert (I), update (U) or deletion (D), while the timestamps help us identify the latest version of our business partner.

If we want to find the latest updated record for the partners LUCIA and RIZ, this is what the query would look like:

  SELECT partner,
        ARRAY_AGG(i1 ORDER BY i1.recordstamp DESC LIMIT 1) AS row
FROM SAP_ECC.but000 i1 
WHERE partner in ('LUCIA','RIZ')
    GROUP BY partner

With the following result:

After identifying stale records for “LUCIA” and “RIZ” business partners, we can proceed to deleting all stale records for “LUCIA” if we do not want to retain the history. In this example, we are using a different table to which the same replication has been done, for the purpose of comparison and to check that all stale records have been deleted for the selection made and that we only kept last updated records. For example:

  DELETE SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2
WHERE
i1.partner = i2.partner
and partner="LUCIA")

You can also use the following query to retrieve stale records for “LUCIA” partner before moving forward with deletion

  SELECT partner, operation_flag, recordstamp  FROM SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) 
AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2 
WHERE 
i1.partner = i2.partner
and partner="LUCIA")

Which produces all of the records, except the latest update:

Partitioning and clustering

To limit the number of records scanned in a query, save on cost and achieve the best performance possible, you’ll need to take two important steps: determine partitions and create clusters. 

Partitioning
A partitioned table is one that’s divided into segments, called partitions, which make it easier to manage and query your data. Dividing a large table into smaller partitions improves query performance and controls costs because it reduces the number of bytes read by a query.

You can partition BigQuery tables by:

• Time-unit column: Tables are partitioned based on a “timestamp,” “date,” or “datetime” column in the table.
• Ingestion time: Tables are partitioned based on the timestamp recorded when BigQuery ingested the data.
• Integer range: Tables are partitioned based on an integer column.

Partitions are enabled when the table is created, as in the example below.  A great tip is to always include the partition filter as shown on the left-hand side of the query.

Clustering
Clustering can be created on top of partitioned tables by applying the fields that are likely to be used for filtering. When you create a clustered table in BigQuery, the table data is automatically organized based on the contents of one or more of the columns in the table’s schema. The columns you specify are then used to colocate related data.

Clustering can improve the performance of certain query types — for example, queries that use filter clauses or that aggregate data. It makes a lot of sense to use them for large tables such as ACDOCA, the table for accounting documents in SAP S/4HANA. In this case, the timestamp could be used for partitioning, and common filtering fields such as the ledger, company code, and fiscal year could be used to define the clusters.

A great feature is that BigQuery will also periodically recluster the data automatically.

Materialized views

In BigQuery, materialized views are precomputed views that periodically cache the results of a query for better performance and efficiency. BigQuery uses precomputed results from materialized views and, whenever possible, reads only the delta changes from the base table to compute up-to-date results quickly. Materialized views can be queried directly or can be used by the BigQuery optimizer to process queries to the base table.

Queries that use materialized views are generally completed faster and consume fewer resources than queries that retrieve the same data only from the base table. If workload performance is an issue, materialized views can significantly improve the performance of workloads that have common and repeated queries. While materialized views currently only support single tables, they are very useful common and frequent aggregations like stock levels or order fulfillment.

Further tips on performance optimization while creating select statements can be found in the documentation for optimizing query computation.

Deployment pipeline and security

For most of the work you’ll do in BigQuery, you’ll normally have at least two delivery pipelines running — one for the actual objects in BigQuery and the other to keep the data staging, transforming, and updated as intended within the change-data-capture flows. Note that you can use most existing tools for your Continuous Integration / Continuous Deployment (CI/CD) pipeline — one of the benefits of using an open system like BigQuery. But, if your organization is new to CI/CD pipelines, this is a great opportunity to gradually gain experience. A good place to start is to read our guide for setting up a CI/CD pipeline for your data-processing workflow.  

When it comes to access and security, most end-users will only have access to the final version of the BigQuery views. While row and column-level security can be applied, as in the SAP source system, separation of concerns can be taken to the next level by splitting your data across different Google Cloud projects and BigQuery datasets. While it’s easy to replicate data and structures across your datasets, it’s a good idea to define the requirements and naming conventions early in the design process so you set it up properly from the start. 

Start driving faster and more insightful analytics

The best piece of advice we can give you is this: Try it yourself. Anyone with SQL knowledge can get started using the free BigQuery tier. New customers get $300 in free credits to spend on Google Cloud during the first 90 days. All customers get 10 GB storage and up to 1 TB queries/month, completely free of charge. In addition to discovering the massive processing capabilities, embedded machine learning, multiple integration tools, and cost benefits, you’ll soon discover how BigQuery can simplify your analytics tasks. 

If you need additional assistance, our Google Cloud Professional Services Organization (PSO) and Customer Engineers will be happy to help show you the best path forward for your organization. For anything else, contact us at cloud.google.com/contact.

Large Machine Learning (ML) models – such as large language models, generative AI, and vision models – are dramatically increasing the number of trainable parameters and are achieving state-of-the-art results. Increasing the number of parameters results in the model being too large to fit on a single VM instance thus demands distributed compute to spread the model across multiple nodes. Google Kubernetes Engine (GKE) has built-in support for NCCL Fast Socket, to help improve the time to train large ML models with distributed, multi-node clusters.

Enterprises are looking for faster and cheaper performance to train their ML models. With distributed training, communicating gradients across nodes is a performance bottleneck. Optimizing inter-node latency is critical to reduce training time and costs. Distributed training uses collective communication as a transport layer over the network between the multiple hosts. Collective communication primitives such as all-gather, all-reduce, broadcast, reduce, reduce-scatter, and point-to-point send and receive are used in distributed training in Machine Learning. 

The NVIDIA Collective Communication Library (NCCL) is commonly used by popular ML frameworks such as TensorFlow and PyTorch. It is a highly optimized implementation for high bandwidth and low latency between NVIDIA GPUs. Google developed a proprietary version of NCCL called NCCL Fast Socket to optimize performance for deep learning on Google Cloud.

NCCL Fast Socket uses a number of techniques to achieve better and more consistent NCCL performance.

• Use of multiple network flows to attain maximum throughput. NCCL Fast Socket introduces additional optimizations over NCCL’s built-in multi-stream support, including better overlapping of multiple communication requests.
• Dynamic load balancing of multiple network flows. NCCL can adapt to changing network and host conditions. With this optimization, straggler network flows will not significantly slow down the entire NCCL collective operation.
• Integration with Google Cloud’s Andromeda virtual network stack.This increases overall network throughput by avoiding contentions in virtual machines (VMs).

We tested (NVIDIA NCCL tests) the performance of NCCL Fast Socket vs NCCL on various machine shapes with 2 node GKE clusters.

The following chart shows the results. For each machine shape, the NCCL performance without Fast Socket is normalized to 1. In each case, using NCCL Fast Socket demonstrated increased performance in a range of 1.3 to 2.6 times faster internetwork communication speed.

As a built-in feature, GKE users can take advantage of NCCL Fast Socket without changing or recompiling their applications, ML frameworks (such as TensorFlow or PyTorch), or even the NCCL library itself. To start using NCCL Fast Socket, create a node pool that uses the plugin with the --enable-fast-socket and --enable-gvnic flags. You can also update an existing node pool using gcloud container node-pools update.

gcloud container node-pools create NODEPOOL_NAME \
    --accelerator type=ACCELERATOR_TYPE, count=ACCELERATOR_COUNT \
    --machine-type=MACHINE_TYPE \
    --cluster=CLUSTER_NAME \
    --enable-fast-socket \
    --enable-gvnic

To achieve better network throughput with NCCL, Google Virtual NICs (gVNICs) must be enabled when creating VM instances. For detailed instructions on how to use gVNICs, please refer to the gVNIC guide. 

To verify that NCCL Fast Socket has been enabled, view the kube-system pods:

kubectl get pods -n kube-system

And the output should b similar to:

NAME                         READY   STATUS    RESTARTS   AGE
fast-socket-installer-qvfdw  2/2     Running   0          10m
fast-socket-installer-rtjs4  2/2     Running   0          10m
fast-socket-installer-tm294  2/2     Running   0          10m

To learn more visit GKE NCCL Fast Socket documentation. We look forward to hearing how NCCL Fast Socket improves your ML Training experience on GKE.

We’re excited to announce that Google Cloud Next returns on October 11–13, 2022. 

Join us for keynotes from industry luminaries and engage live with Google developers. Explore dynamic content across various learning levels, and dive deep into technologies and solutions spanning the Google Cloud and Google Workspace portfolios. Participate in breakout sessions, demos, and hands-on training. Hear from the world’s leading companies about their digital transformation journeys. You’ll have opportunities to connect with experts, get inspired, and boost your skills. We can’t wait to see you at Next ’22!

It’s too early to determine how the event experience will span the digital and physical worlds, so please stay tuned for updates as we plan with the health and safety of the attendees in mind. In the meantime, mark October 11–13 in your calendar, and visit our event site for updates. For more inspiration, rediscover Next ’21, now available on demand.

For far too long, cloud infrastructure has focused on raw speeds and feeds of building blocks such as VMs, containers, networks, and storage. Today, Moore’s law is slowing, and the burden of picking the right combination of infrastructure components increasingly falls on IT.

At Google Cloud we are committed to removing that burden. We’ve engineered golden paths from silicon to the console, with a recipe of purpose-built infrastructure, prescriptive architectures, and an ecosystem to deliver workload-optimized infrastructure. And at this year’s Google Cloud Next, we made some exciting new announcements across key workloads.

In this post, we will put the new Google Cloud releases and capabilities in the context of popular workloads: From AI/ML to high performance computing and data analytics, to SAP, VMware, and mainframes.

Powering AI/ML workloads

No single technology has the potential to drive more transformation than AI and ML. At Next, we announced several new AI-based services: Translation Hub and DocAI services, and the OpenXLA Project, an open-source ecosystem of ML compiler technologies co-developed by a consortium of industry leaders.

To build these innovative services, you also need a powerful infrastructure platform. Today, we announced the following additions to our compute offerings:

• Cloud TPU v4 Pods – Now in General Availability (GA), Google’s advanced machine learning infrastructure is based on the world’s largest publicly available ML hub in Oklahoma and offers up to 9 exaflops of peak aggregate compute. Developers and researchers can use TPU v4 to train increasingly sophisticated models to power workloads such as large-scale natural language processing (NLP), recommendation systems, and computer vision algorithms in a cost effective and sustainable way. In June 2022, Cloud TPU v4 recorded the fastest training times on five MLPerf 2.0 benchmarks, with up to 80% better performance and up to 50% lower cost of training than the next best available alternative.

• A2 Ultra GPU VM instances – Now GA, you can use A2 Ultra GPU VM instances with Compute Engine, Google Kubernetes Engine and Deep Learning VMs. A2 Ultra GPUs have the largest and fastest GPU memory of the Google Cloud portfolio and are optimized for large AI/ML and HPC workloads with use cases such as AI assistants, recommendation systems, and autonomous driving. Powered by NVIDIA A100 Tensorcore GPU with 80 GBs of GPU memory, A2 Ultra delivers 25% higher throughput on inference and 2x higher performance on HPC simulations than original A2 machine shapes.

Learn more about A2 Ultra and hear how you can accelerate your ML development and learn from our customers Uber and Cohere in breakout session MOD300.

Boost HPC and data-intensive workloads

Customers rely on Google Cloud to help them run data-intensive workloads such as HPC and Hadoop. The new C3 machine series (currently in Preview) is the first in the public cloud to include the new 4th Gen Intel Xeon processor and Google’s custom Intel Infrastructure Processing Unit that enables 200Gbps, low-latency networking. Learn more about top HPC best practices in breakout session MOD106.

You can pair the C3 with Hyperdisk (currently in Private Preview), our new generation block storage, which offers 80% higher IOPS per vCPU for high-end database management system (DBMS) workloads when compared to other hyperscalers. Data workloads such as Hadoop and databases may no longer need oversized compute instances to benefit from high IOPS. Learn more about Hyperdisk in breakout session MOD206.

Enable new streaming experiences

Media and entertainment customers want streaming optimized workloads that build on our innovation in edge and our global network. Here are some new developments to support streaming use cases:

• Cloud CDN, our original content delivery networking offering, now offers Dynamic compression to reduce the size of responses transferred from the edge to a client, significantly helping to accelerate page load times and reduce egress traffic.
• Media CDN, introduced earlier this year, now supports the Live Stream API to ingest and package source content into HTTP-Live Streaming and DASH formats for optimized live streaming. We are also enabling two new developer-friendly integrations in Preview for Media CDN: Dynamic Ad Insertion with Google Ad Manager, which provides customized video ad placements, and third-party Ad Insertion using our Video Stitcher API for personalized ad placement. With these options, content producers can introduce additional monetization and personalization opportunities to their streaming services.
• For advanced customization, we are introducing the Preview of Network Actions for Media CDN, a fully managed serverless solution based on open-source web assembly that enables programmability for customers to deploy their own code directly in the request/response path at the edge.

With Media CDN, customers like Paramount+ are able to deliver a high-quality experience on the same Google infrastructure we’ve tested and tuned to serve YouTube’s 2 billion users globally.

“Streaming is one of the key growth areas for Paramount Global. When we migrated traffic onto Media CDN, we observed consistently superior performance and offload metrics. Partnering with Google Cloud enables us to provide our subscribers with the highest quality viewing experience.” — Chris Xiques, SVP of Video Technology Group, Paramount Global

Bringing Google Cloud to your workloads

For customers in regulated markets and in countries with strict sovereignty laws, Google Cloud has a spectrum of offerings to help them achieve varying degrees of sovereignty. Sovereign Controls by T-Systems is now GA, and Local Controls by S3NS, the Thales-Google partnership, is now available in Preview. You can expect more region and market announcements in the coming months. And for the most stringent sovereignty needs, we offer Google Distributed Cloud Hosted for a disconnected Google Cloud footprint deployed at the customer’s chosen site.

We are also expanding functionality for existing offerings to give you even more options to run your cloud where you want, how you want.

• Anthos, our cloud-centric container platform to run modern apps anywhere consistently at scale, now has a more robust user interface and an upgraded fleet management experience. Create, update, and reconfigure your Anthos clusters the same way from one dashboard or command-line interface, wherever your clusters run. New fleet management capabilities let you manage growing fleets of container clusters across clouds, on-premises, and at the edge and for different use cases (isolate dev from prod, apply fleet-specific security controls, enforce configurations fleet-wide). And we are pleased to announce the GA of virtual machine support on Anthos clusters for retail edge environments. Learn more about how Anthos can help you run modern applications anywhere in breakout session MOD208.

• Google Distributed Cloud Edge GPU-Optimized Config, is now GA in server-rack form factors powered by 12 Nvidia T4 GPUs. GDC Edge is designed to enable low-latency and high-performance hybrid workloads as a complement to your primary Google Cloud environment or as an independent edge deployment. We’re seeing early adoption by customers and partners for workloads such as augmented reality and retail self-checkout. In addition, software partners such as 66degrees are taking advantage of GDC Edge GPU optimization to provide real-time insights on in-store product availability, while Ipsotek is using machine intelligence at the edge to perform crowd and foot-traffic analysis in large locations such as malls, airports and railway stations. Learn more about GDC Edge and new partner validated solutions here, or watch breakout session MOD207 to hear how you can modernize your data center and accelerate your edge.

Infrastructure building blocks for cloud-first workloads

For most new projects, developers prefer them to be cloud-first optimized workloads, and nearly half of all developers use containers today. Google Kubernetes Engine (GKE) is the most automated and scalable container management service on the market today, and when you use GKE Autopilot, developers can get started faster than other leading cloud providers. At Next ‘22, we’re excited to unveil:

• A new workshop to help you discover how to unlock efficiency and innovation with a GKE Autopilot — sign up to get started.
• A new security posture management dashboard in GKE that provides opinionated guidance on Kubernetes security along with bundled tools to expertly help improve the security posture of your Kubernetes clusters and workloads.

When building scalable web applications or running background jobs that require lightweight batch processing, developers are increasingly turning to serverless technologies. We’re helping developers choose serverless with numerous updates to Cloud Run, our managed serverless compute service.

• With new Cloud Run integrations, Cloud Run and Google Cloud services work together smoothly. For example, configuring domains with a Load Balancer or connecting to a Redis Cache is now as easy as a single click, with more scenarios on the way.
• Cloud Run customized health checks for services is now available in Preview, providing user-defined HTTP and TCP Startup probes at the container level. This capability allows Cloud Run users to define criteria as to when their application has started and is ready to start serving traffic, and is particularly useful for applications that might require additional startup time on their first initialization.
• To make continuous deployment easier for our customers, we added an integration between Cloud Deploy, our fully managed continuous deployment service, and Cloud Run. With this integration in place, you can do continuous deployment through Cloud Deploy directly to Cloud Run, with one-click approvals and rollbacks, enterprise security and audit, and built-in delivery metrics. Learn more on Cloud Deploy web page.

Learn more about how to build next-level web applications with Cloud Run in breakout session BLD203.

You also need confidence in the building blocks that make up your workloads from the outset. To help get you started, we introduced Software Delivery Shield, which provides a comprehensive suite of tools offering a fully managed, end-to-end solution that helps protect your software supply chain. The Software Delivery Shield launch also included the following announcements:

• Cloud Workstations, currently in Preview, provides fully-managed development environments built to meet the needs of security-sensitive enterprises. With Cloud Workstations, developers can access secure, fast, and customizable development environments via a browser anytime and anywhere, with consistent configurations and customizable tooling. To learn more about Cloud Workstations, please visit the web page and check out breakout session BLD100.
• A new partnership with JetBrains provides fully managed Jetbrains IDEs as part of Cloud Workstations. This integration can give developers access to several popular IDEs with minimal management overhead for their admin teams.

Read more about Software Delivery Shield.

Open source and open standards are an important part of building applications. With that, we are excited to announce that Google has joined the Eclipse Adoptium Working Group, a consortium of leaders in the Java community working to establish a higher quality, developer-centric standard for Java distributions. Also, we are making Eclipse Temurin available across Google Cloud products and services. Eclipse Temurin provides Java developers a higher quality developer experience and more opportunities to create integrated, enterprise-focused solutions, with the openness they deserve.

Pioneering new technology with Web3 optimized infrastructure

It’s incredible to see the energy in Web3 right now and the focus on developing the broader benefits, use cases, and core capabilities of blockchain. We are helping customers with scalability, reliability, security, and data, so they can spend the bulk of their time on innovation in the Web3 space — unlocking deep user value and building the next app to attract a billion users to the ecosystem.

Companies like Near, Nansen, Solana, Blockdaemon, Dapper Labs & Sky Mavis use Google Cloud’s infrastructure. Yesterday, we announced a strategic partnership with Coinbase to better serve the growing Web3 ecosystem.

“We could not ask for a better partner to execute our vision of building a trusted bridge into the Web3 economy and accelerating broader growth and adoption of blockchain technology. I started Coinbase with a desire to create a more accessible financial system for everyone, and Google’s history of supporting open source and decentralized ecosystems made this a natural fit. Our partnership marks a major inflection point and, together, we are removing barriers to blockchain adoption and accelerating innovation.” — Brian Armstrong, CEO, Coinbase

Lift and transform traditional workloads

Not all workloads were born in the cloud — or have completed their journey to it. Google Cloud offers a variety of programs and capabilities to help optimize traditional workloads for a new cloud era, regardless of whether you’re looking to migrate it as-is, do light optimizations, or fully modernize and transform:

• Migration Center – Now in Preview. For organizations looking to migrate to the cloud and transform their businesses, Migration Center can reduce the complexity, time, and cost by providing an integrated migration and modernization experience. It brings together our assessment, planning, migration, and modernization tooling in one centralized location with a shared data platform so you can proceed faster, more intelligently, and more easily through your journey. Learn more at the Migration Center webpage.

• Google Cloud VMware Engine – Google Cloud is the first VMware partner to market with VMware Cloud Universal support, simplifying the migration of on-premises VMware VMs to VMware Engine in the cloud. With a cloud market-leading 99.99% availability SLA in a single site, VMware Engine is helping large organizations like Carrefour move to Google Cloud.

• Dual Run for Google Cloud – Now in Preview, this first-of-its-kind solution helps eliminate many of the common risks associated with mainframe modernizations by letting customers simultaneously run workloads on their existing mainframes and their modernized version on Google Cloud. This means customers can perform real-time testing and quickly gather data on performance and stability with no disruption to their business. Once satisfied that the performance of the two systems is functionally equivalent, the customer can make the Google Cloud environment the system of record, and operate the existing mainframe system as needed, typically as a backup. Learn more about Dual Run in this press release or on our Mainframe Modernization webpage.

• Google Cloud Workload Manager – Now in Preview for SAP workloads, and available in the Google Cloud console, Workload Manager provides automated analysis of your enterprise systems on Google Cloud to help continuously improve system quality, reliability, and performance. Google Cloud Workload Manager evaluates your SAP workloads by detecting deviations from documented standards and best practices to help proactively prevent issues, continuously analyze workloads, and simplify system troubleshooting.

Learn more about how organizations like Global Payments and Loblaw Technology have migrated with ease and speed in breakout session MOD104.

Protect all your workloads

The foundation of any workload is storage, and this year we expanded the number of supported regions with our Cloud Storage dual-region buckets (GA), so you can ensure that your workloads are protected. In the event of an outage, your application can easily access the data in the alternate region. You can add Turbo replication (GA) with your dual-region buckets. Turbo replication is backed by a 15-minute Recovery Point Objective (RPO) SLA.

Also, we recently announced Google Cloud’s Backup and DR Service (GA). This service is a fully integrated data-protection solution for critical applications and databases that lets you centrally manage data protection and disaster recovery policies directly within the Google Cloud console, and fully protect databases and applications with a few mouse clicks.

Learn more about Storage best practices in breakout session MOD206.

Migrate, observe, and secure network traffic

We also announced many new capabilities and enhancements to the Cloud Networking portfolio that help customers migrate, modernize, secure, and observe workloads traveling to and in Google Cloud:

• Private Service Connect helps simplify migrations by giving more control to users and integrations with 5 new partners.
• Network Intelligence Center can monitor the network for you with enhanced capabilities like the Performance Dashboard that will give you latency visibility between Google Cloud and the Internet.
• We expanded our Cloud Firewall product line and introduced two new tiers: Cloud Firewall Essentials and Cloud Firewall Standard.

For more information on what’s new with networking, read this blog and check out breakout session MOD205.

Optimize costs

Our customers’ workloads also require technical and commercial options that can deliver the best return on their investments. Here are some new enhancements that can help:

• Flex CUDs – GA coming soon, Flexible Committed Use Discounts help you save up to 46% off on-demand Compute Engine VM pricing, in exchange for a one- or three-year commitment. Like standard CUDs, you can apply Flex CUDs across projects within the same billing account, to VMs of different sizes, and across operating systems. Learn more.
• Batch – Now GA, Batch is a fully managed service that helps you run batch jobs easily, reliably, and at scale. Without having to install any additional software, Batch dynamically and efficiently manages resource provisioning, scheduling, queuing, and execution, freeing you up to focus on analyzing results. There’s no charge for using Batch, and you only pay for the resources used to complete the tasks.

Learn more about how you can optimize for cost savings with Google Cloud in MOD103.

Optimize for sustainability

Any new workloads you develop should have the smallest possible carbon footprint. Google Cloud Carbon Footprint helps you measure, report, and reduce your cloud carbon emissions, and is now GA. Since introducing Carbon Footprint last year, we added features that cover Scope 1, 2 and 3 emissions, and provided role-based access to other users such as sustainability teams. Active Assist’s carbon emissions estimates related to removing unattended projects are also now GA. Learn how to build a more sustainable cloud with lower carbon emissions in MOD103, and start using Carbon Footprint today.

“At Box, we’re focused on reducing our carbon footprint and we’re excited for the visibility and transparency the Carbon Footprint tool will provide as we continue our work to operate sustainably.” — Corrie Conrad, VP Communities and Impact and Executive Director of Box.org at Box

“At SAP we’re working to achieve net-zero by 2030, making it crucial to measure carbon emissions all along our value chain. Collaborating with Google Cloud on Carbon Footprint ensures that accurate emissions data is available in a timely manner, helping our many Solution Areas make more sustainable decisions.” — Thomas Lee, SVP and Head of Multicloud Products and Services at SAP

Designed around your workloads

These are only a few examples of the many golden paths we are enabling for your workloads. We strive to be the ‘open infrastructure cloud’ that is the most intuitive to consume because everything is designed around your workloads, providing tremendous TCO benefits.

To get even more information on all of this and more check out the Modernize breakout session track and these “What’s New” sessions available on demand at Next ’22:

• MOD105 to learn about new infrastructure solutions for enterprise architects and developers.
• BLD106 to learn what’s new to help developers build, deploy, and run applications.
• OPE100 to learn about the biggest announcements for DevOps teams, sysadmins, and operators.