10 Reasons that Make Google Cloud the Champion of IaaS
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When you choose to run your business on Google Cloud you benefit from the same planet-scale infrastructure that powers Google’s products such as Maps, YouTube, and Workspace.
We have picked 10 ways in which Google Cloud Infrastructure services outshine alternatives in the market in how they simplify your operations, save money, and secure your data.
1. Custom Machine Types means no wasted resources
Compute Engine offers predefined machine types that you can use when you create a VM instance. A predefined machine type has a preset number of vCPUs and a preset amount of memory; each type is billed at a set price as described on the Compute Engine pricing page.
If predefined machine types don’t meet your needs, you can create a VM instance with a custom number of vCPUs and custom amount of memory, effectively building a custom machine type. Custom machine types are available only for general-purpose machine families. When you create a custom machine type, you are deploying a custom machine type from the E2, N2, N2D, or N1 machine family on GCP. No other leading cloud vendor offers custom machine types so extensively.
Custom machine types are a good idea for workloads that aren’t a good fit for the predefined machine types and for workloads that require more processing power or memory but don’t need all of the upgrades provided by the next machine type level. This translates into lower operating costs. They are also useful for controlling software licensing costs that are based on the number of underlying compute cores.
Jeremy Lloyd, Infrastructure and Application Modernization Lead at Appsbroker, a Google partner:
“Custom machine types coupled with Google’s StratoZone data center discovery tool provides Appsbroker with the flexibility we need to provide cost efficient virtual machines matched to a virtual machine’s actual utilization. As a result, we are able to keep our customers’ operating costs low while still providing the ability to scale as needed.”
2. Compute Engine Virtual Machines are optimized for scale-out workloads
For scale-out workloads, T2D, the first instance type in the Tau VM family, is based on 3rd Gen AMD EPYC processors and leapfrogs VMs for scale-out workloads of any leading public cloud provider today, both in terms of performance and price-performance. Tau VMs offer 56% higher absolute performance and 42% higher price-performance compared to general-purpose VMs from any leading public cloud vendor (source). The x86 compatibility provided by these AMD EPYC processor-based VMs gives you market-leading performance improvements and cost savings, without having to port your applications to a new processor architecture. Sign up here if you are interested in trying out T2D instances in Preview.
For SAP HANA, Google Cloud has demonstrated with SAP how we can run the world’s largest scale-out HANA system in the public cloud (96TB). With such innovation, you are covered as your business grows exponentially.
3. Largest single node GPU-enabled VM
Google is the only public cloud provider to offer up to 16 NVIDIA A100 GPUs in a single VM, making it possible to train very large AI models. Users can start with one NVIDIA A100 GPU and scale to 16 GPUs without configuring multiple VMs for single-node ML training, without crossing the VM layer.
Additionally, customers can choose smaller GPU configurations—1, 2, 4 and 8 GPUs per VM—providing the flexibility to scale their workload as needed.
The A2 VM family was designed to meet today’s most demanding applications—workloads like CUDA-enabled machine learning (ML) training and inference, for example. This family is built on the A100 GPU which offers up to 20x the compute performance compared to the previous generation GPU and comes with 40 GB of high-performance HBM2 GPU memory. To speed up multi-GPU workloads, the A2 VMs use NVIDIA’s HGX A100 systems to offer high-speed NVLink GPU-to-GPU bandwidth that delivers up to 600 GB/s. A2 VMs come with up to 96 Intel Cascade Lake vCPUs, optional Local SSD for workloads requiring faster data feeds into the GPUs and up to 100 Gbps of networking. A2 VMs provide full vNUMA transparency into the architecture of underlying GPU server platforms, enabling advanced performance tuning. Google Cloud offers these GPUs globally.
4. Non-disruptive maintenance means you worry less about planned downtime
Compute Engine offers live migration (non-disruptive maintenance) to keep your virtual machine instances running even when a host system event, such as a software or hardware update, occurs. Google’s Compute Engine live migrates your running instances to another host in the same zone without requiring your VMs to be rebooted. Live migration enables Google to perform maintenance that is integral to keeping infrastructure protected and reliable without interrupting any of your VMs. When a VM is scheduled to be live-migrated, Google provides a notification to the guest that a migration is imminent.
Live migration keeps your instances running during:
- Regular infrastructure maintenance and upgrades
- Network and power grid maintenance in the data centers
- Failed hardware such as memory, CPU, network interface cards, disks, power, and so on. This is done on a best-effort basis; if a hardware component fails completely or otherwise prevents live migration, the VM crashes and restarts automatically and a hostError is logged.
- Host OS and BIOS upgrades
- Security-related updates
- System configuration changes, including changing the size of the host root partition, for storage of the host image and packages
Live migration does not change any attributes or properties of the VM itself. The live migration process transfers a running VM from one host machine to another host machine within the same zone. All VM properties and attributes remain unchanged, including internal and external IP addresses, instance metadata, block storage data and volumes, OS and application state, network settings, network connections, and so on. This has the benefit of reducing operational and maintenance overhead, helps you build a more robust security posture where infrastructure can be consciously revamped from a known good state and minimizes risks for advanced persistent threats.
Refer to Lessons learned from a year of using live migration in production on Google Cloud from the Google engineering team.
5. Trusted Computing: Shielded VMs guard you against advanced, persistent attacks
Establishing trust in your environment is multifaceted, involving hardware and firmware, as well as host and guest operating systems. Unfortunately, threats like boot malware or firmware rootkits can stay undetected for a long time, and an infected virtual machine can continue to boot in a compromised state even after you’ve installed legitimate software.
Shielded VMs can help you protect your system from attack vectors like:
- Malicious guest OS firmware, including malicious UEFI extensions
- Boot and kernel vulnerabilities in the guest OS
- Malicious insiders within your organization
To guard against these kinds of advanced persistent attacks, Shielded VMs use:
- Unified Extensible Firmware Interface (UEFI) BIOS: Helps ensure that firmware is signed and verified
- Secure and Measured Boot: Helps ensure that a VM boots an expected, healthy kernel
- Virtual Trusted Platform Module (vTPM): Establishes root-of-trust, underpins Measured Boot, and prevents exfiltration of vTPM-sealed secrets
- Integrity Monitoring: Provides tamper-evident logging, integrated with Stackdriver, to help you quickly identify and remediate changes to a known integrity state
The Google approach allows customers to deploy Shielded VMs with only a simple click, thereby easing implementation.
6. Confidential Computing encrypts data while in use
Google Cloud was a founding member of the Confidential Computing Consortium. Along with encryption of data in transit and at rest using customer-managed encryption keys (CMEK) and customer-supplied encryption keys (CSEK), Confidential VM adds a “third pillar” to the end-to-end encryption story by encrypting data while in use. Confidential Computing uses processor-based technology that allows data to be encrypted in use while it is being processed in the public cloud. Confidential VM allows you to to encrypt memory in use on a Google Compute Engine VM by checking a single checkbox.
All Confidential VMs support the previously mentioned Shielded VM features under the covers—you can think of Shielded VM as helping to address VM integrity, while Confidential VM addresses the memory encryption aspect which relies on CPU features. With the confidential execution environments provided by Confidential VM and AMD Secure Encrypted Virtualization (SEV), Google Cloud keeps customers’ sensitive code and other data encrypted in memory during processing. Google does not have access to the encryption keys. In addition, Confidential VM can help alleviate concerns about risk related to either dependency on Google infrastructure or Google insiders’ access to customer data in the clear.
See what Google Cloud partners say about Confidential Computing here.
7. Advanced networking delivers full-stack networking and security services with fast, consistent, and scalable performance
Google Cloud’s network delivers low latency, reduces operational costs and ensures business continuity, enabling organizations to seamlessly scale up or down in any region to meet business needs. Our planet-scale network uses advanced software-defined networking and security with edge caching services to deliver fast, consistent, and scalable performance. With 28 regions, 85 zones, and 146 PoPs connected by 16 subsea fiber cables around the world, Google Cloud’s network offers a full stack of layer 1 to layer 7 services for enterprises to run their workloads anywhere. Enterprises can be assured that they have best-in-class networking and security services connecting their VMs, containers, and bare metal resources in hybrid and multi-cloud environments with simplicity, visibility, and control.
Google Cloud’s network has protected customers from one of the world’s largest DDoS attacks at 2.54 Tbps. With our multi-layer security architecture and products such as Cloud Armor, our customers ran their business with no disruptions. Furthermore, our recent integration of Cloud Armor with reCAPTCHA Enterprise adds best-in-class bot and fraud management to prevent volumetric attacks. Cloud Armor is deployed with our Cloud Load Balancer and Cloud CDN, extending the secure benefits at the network edge for traffic coming into Google Cloud so customers have security, performance, and reliability all built in. Furthermore, we are excited to offer Cloud IDS in preview, which was co-developed with security industry leader, Palo Alto Networks, to run natively in Google Cloud.
Our advanced networking capabilities also extends to GKE and Anthos networking. With the GKE Gateway controller, customers can manage internal and external HTTPS load balancing for a GKE cluster or a fleet of GKE clusters with multi-tenancy while maintaining centralized admin policy and control. Unlike other Kubernetes offerings, we offer eBPF dataplane which brings powerful tooling such as Kubernetes network policy and logging to GKE. eBPF is known to kernel engineers as a “superpower” for its unique architecture to load and unload modules in kernel space, and now this capability is built in with Google Cloud networking.
For observability and monitoring, our customers deploy Network Intelligence Center, Google Cloud’s comprehensive network monitoring, verification and optimization platform. With four key modules in Network Intelligence Center, and several more to come, we are working towards realizing our vision of proactive network operations that can predict and heal network failures, driven by AI/ML recommendations and remediation. Network Intelligence Center provides unmatched visibility into your network in the cloud along with proactive network verification. Centralized monitoring cuts down troubleshooting time and effort, increases network security and improves the overall user experience.
8. Regional Persistent Disk for High Availability
Regional Persistent Disk is a storage option that provides synchronous replication of data between two zones in a region. Regional Persistent Disks can be a great building block if you need to ensure high availability of your critical applications as they offer cost-effective durable storage and replication of data between two zones in the same region.
Regional Persistent Disks are also easy to set up within the Google Cloud Console. If you are designing robust systems or high availability services on Compute Engine, Regional Persistent Disks combined with other best practices such as backing up your data using snapshots enable you to build an infrastructure that is highly available and recoverable in a disaster. Regional Persistent Disks are also designed to work with regional managed instance groups. In the unlikely event of a zonal outage, Regional Persistent Disks allow continued I/O through failover of your workloads to another zone. Regional Persistent Disks can help meet zero RPO and near-zero RTO requirements and other stringent SLAs that your critical applications might require by maximizing application availability and protection of data during events such as host/VM failures and zonal outages.
9. Cloud Storage’s single namespace for dual-region and multi-region means managing regional replication is incredibly simple
Similar to how Persistent Disk makes data more available by replicating data across zones, Cloud Storage provides similar benefits for object storage. Cloud Storage within a region is cross-zone by definition, reducing the risk that a zonal outage would take down your application. Cloud Storage adds to this by also providing a cross-region option that can protect against a regional outage and gets your data closer to distributed users. This comes in the form of Dual-region or Multi-region settings for a bucket. These are the simplest to implement cross-region replication offerings in the industry—just a simple button or API call to enable them. In addition to being simple to implement, they offer an added advantage of using a single bucket name that spans regions.
This is unique in the industry. Competitive offerings currently require setting up and managing two distinct buckets, one in each region and they don’t offer the strong consistency properties Cloud Storage offers across regions. Operations and app development are burdened by this design. Google’s single namespace approach dramatically simplifies application development (the app runs on single region or dual/multi-region without any changes), and provides simpler application restarts and testing for DR.
10. Predictive autoscaling
Customers use predictive autoscaling to improve response times for applications with long initialization times or for applications with workloads that vary predictably with daily or weekly cycles. When you enable predictive autoscaling, Compute Engine forecasts future load based on your Managed Instance Group’s history and scales out the MIG’s in advance of predicted load, so that new instances are ready to serve when the load arrives. Without predictive autoscaling, an autoscaler can only scale a group reactively, based on observed changes in load in real time.
With predictive autoscaling enabled, the autoscaler works with real-time data as well as with historical data to cover both the current and forecasted load. Forecasts are refreshed every few minutes (faster than competing clouds) and consider daily and weekly seasonality, leading to more accurate forecasts of load patterns.
For more information, see How predictive autoscaling works and Checking if predictive autoscaling is suitable for your workload.
These are just a few examples of customer-centric innovation that set Google Cloud infrastructure apart. Bring your applications and let the platform work for you.
Get started by learning about your options for migration, or talk to our sales team to join the thousands of customers who have embarked upon this journey.
Acknowledgement
Special thanks to Dheeraj Konidena (Google) for contributing to this article.
Your DW Need Scaling Up? Try What This Company Did: It Can Run 25,000 Events a Second
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With access to more data than ever before, companies have never been better positioned to adopt precision marketing methods and target the right customers at the right time. Emarsys, a digital marketing platform, enables its clients to collect, analyze, and act on a wide variety of data. From websites to mobile apps to emails, Emarsys’ customers can handle data from all its digital channels on a single, easy-to-use platform. Emarsys also makes sure that customers receive the highest quality data possible, making for smarter decisions and better business practices.
“We were close to the limits of our internal data warehouse, scalability-wise. We didn’t want to get to the point where we’d have to delete data or cancel projects. In Google Cloud we saw a platform that could scale with our ambitions and be optimized for AI and real-time solutions.”
—Levente Otti, Head of Data, Emarsys
Since launching as an email solutions provider in 2000, Emarsys has grown into the world’s largest independent digital marketing platform, with more than 2,500 clients worldwide and reaching more than 1.4 billion people. By 2016, the company felt that its existing data warehouse platform was close to its limits, affecting not just day-to-day operations but also important strategic goals.
“We were close to the limits of our internal data warehouse, scalability-wise. We didn’t want to get to the point where we’d have to delete data or cancel projects,” says Levente Otti, Head of Data at Emarsys. “In Google Cloud, we saw a platform that could scale with our ambitions and be optimized for AI and real-time solutions.”
Minimal maintenance, unlimited scale with Google Cloud
Digital marketing is a highly competitive environment. Emarsys works alongside big players with a huge market share on the one hand and smaller, specialist companies on the other. It has thrived by successfully combining the all-inclusive offerings of the former with the agility of the latter, constantly looking for ways to innovate and improve. In recent years, the company had started to feel that the ability to handle large quantities of data was no longer enough. The next challenge was speed. “We truly believe that in the future, everything will be done in real time, including data processing, analytics, and AI predictive models,” Levente says.
At the start of 2016, Emarsys’ existing data warehouse was a software-as-a-service solution running on-premises, which required hardware and software maintenance in order to keep up with the company’s growing appetite for data-heavy use cases such as prediction and analytics. The existing platform had proven its worth processing large amounts of data in batches, but its real-time capabilities were limited. Moreover, Emarsys had begun to experiment with AI technology, but found that its data warehouse couldn’t scale to accommodate some of the more resource-intensive processes, such as training the predictive models. The company decided that it needed a new, cloud-based data platform.
After evaluating some of the leading cloud providers, Emarsys chose Google Cloud for its mature AI capabilities and its ease of use. “With the other solutions, we still had to rent virtual machines and hardware and be responsible for maintenance. At the time, Google Cloud was the only provider that could take that management overhead away from us, while keeping customers accounted for on every query level,” Levente says.
To implement its new data platform, Emarsys teamed up with Google Cloud Partner Aliz. Over a series of meetings, workshops, and architecture reviews, Aliz helped Emarsys navigate the Google Cloud ecosystem to find the right products for the solution it was looking for. “Aliz really helped us set off in the right direction,” explains Levente.
With Google BigQuery, we can run queries which process terabytes of data, in seconds. We can also develop our own user-defined functions, incorporating Bayesian statistics into our predictive algorithms. That means we can take into account historical data, resulting in much more accurate predictions in a scalable way within seconds.”
—Levente Otti, Head of Data, Emarsys
Emarsys’ new data platform would actually be two: one platform for batch processing data and one for real-time analysis and interactions. Firstly, a proprietary publishing component gathered all the data points from Emarsys’ various channels including the website, mobile, emails, and custom events. With Cloud Pub/Sub and Cloud Dataflow, Emarsys transported and processed the data into BigQuery, which allows for further work and reviews that take into account errors or delayed events. After this, the data was exported to the main batch processing platform, which ran on BigQuery. For the real-time analytics, Emarsys used Cloud Bigtable to access data and Cloud Dataflow to pipeline it into the real-time platform, which could communicate with AI components or interaction components via an API to deliver real-time interactions with customers.
On top of the overall data infrastructure, Emarsys built a new AI platform with Google Cloud components. Training the predictive models had been an issue in the past due to the large number of resources required, so Emarsys chose to use Google Kubernetes Engine clusters, which can scale up and down on demand, without the need for hardware configuration or management. The trained models were held securely in Cloud Storage. From here, they were integrated with BigQuery for power and flexibility, allowing Emarsys to improve not just the speed of its AI predictions but also the quality.
“With Google BigQuery, we can run queries which process terabytes of data, in seconds,” shares Levente. “We can also develop our own user-defined functions incorporating Bayesian statistics into our predictive algorithms. That means we can take into account historical data, resulting in much more accurate predictions in a scalable way within seconds.”
Real-time insight, long-term satisfaction
Google Cloud enabled Emarsys to build a scalable data and AI platform that delivers powerful, actionable insights in real time. According to Levente, the company wanted to spend less time managing overload and more time considering how it should handle data. An immediate result of the new platform has been that data is now available in a scalable way, without hardware additions and management.
“With Google Cloud, we’ve been able to build a truly real-time data platform. The norm used to be daily batch processing of data. Now, if an event happens, marketing actions can be executed within seconds, and customers can react immediately. That makes us very competitive in our market.”
—Levente Otti, Head of Data, Emarsys
The clear and innovative pricing schemes of Google Cloud have also brought a new level of accountability to Emarsys’ costs in a way that wasn’t possible with its on-premises infrastructure. “Now that we only pay for what we use, we can assign costs to specific customers or queries, which has a huge impact on our pricing and product development strategies,” says Levente.
Thanks to the power of BigQuery and the scale at which it can handle data, Emarsys can now apply its analytics and AI tools to their full potential. “It’s very important to enable our clients to create the best possible experience for customers,” says Levente. At the same time, the company has cut its AI platform costs by 70% with Kubernetes while increasing scalability compared to the previous solution. The whole data platform was built to be scalable, and its first big test came during the retail peak of Black Friday, when it comfortably handled 250,000 events per second. “Perhaps the biggest impact on the business came with the real-time nature of the new platform,” says Levente.
“With Google Cloud, we’ve been able to build a truly real-time data platform,” he explains. “The norm used to be daily batch processing of data. Now, if an event happens, marketing actions can be executed within seconds, and customers can react immediately. That makes us very competitive in our market.”
Since implementing the new platform, Emarsys has continued to innovate with it and is about to release a new Real-Time Decision Framework, which will provide customers with even more real-time products and tools. The company continues to work with Aliz and Google Cloud, exploring other products such as Google BigQuery ML and TensorFlow to improve its AI processes. “We had a problem that we wanted to tackle now, and for us, Google Cloud was the best way of doing that,” says Levente. “But it was also about looking ahead. We felt that Google Cloud offered us the best way of future-proofing our platform.”
Lending DocAI Shortens Borrowers’ Journey on Roostify
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The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.
No time to spare: Overcoming document processing challenges with AI
Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.
As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process.
Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.
Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.
Full integration of AI solutions
Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.
Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:
Here are the steps for processing data:
- Receives document processing request from the client.
- API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
- Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service.
- If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service.
- If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
- Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
- LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
- Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
- LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
- If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
- If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
- Finally, Data stored in the GCP bucket will be deleted.
All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information. Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging. For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.
Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place.
Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.
Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.
Fast track end-to-end deployment with Google Cloud AI Services (AIS)
Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.
The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.
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Journey to Transformation and Modernization with Google’s Distributed Cloud
Google Cloud has been leading the way of helping businesses make most from their cloud investments to drive digital transformation through modern application platforms that cater to today’s customer needs. Watch the video from the Next ’21 to explore three areas where companies are supported by Google Cloud throughout their cloud evolution journey–cloud migration and modernization, extension of services and engineering practices to hybrid and multicloud environments, and delivery of high performance with planet scale distributed infrastructure. Also, learn how Google Cloud is equipped for more complex and unique use cases, from datacenter to the edge. Hear the strategies and customer stories that can help your business modernize people, processes, and applications to fully leverage Google’s distributed cloud!
Google and NetApp Partnership Expansion to Ease Customers’ Cloud Migration
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As organizations continue to evolve to meet the needs of their stakeholders, they are increasingly looking to adopt the cloud and digitally transform their business. Since our partnership with NetApp began in 2018, we’ve been focused on delivering the infrastructure, tools, and services to help customers on their cloud migration journey and benefit from their data to better serve their customers.
“Customers are looking to adopt flexible and scalable solutions that meet the needs of their business,” said Ronen Schwartz, SVP & GM, Cloud Volumes, NetApp. “Our long-standing partnership with Google Cloud continues to deliver innovative solutions for customers as they look to migrate and run their business critical applications in the cloud while unlocking the power of their data with industry-leading cloud services.”
Innovative, industry-first solutions have become the standard for our partnership. From the general availability of NetApp Cloud Volumes Service (CVS) for Google Cloud in 2019, to our collaboration on new architectures in 2020 with the release of the software-defined CVS powered by Google Kubernetes Engine (GKE), we continue to deliver first-of-its-kind enterprise file storage services together. In 2021, the pace of innovation has only increased—together, we delivered volume replication powered by SnapMirror, which efficiently copies volume data from one Google Cloud region to another for data consistency, development, testing, or disaster recovery.
We’ve also made our services available globally—you can now run Cloud Volumes ONTAP in all 28 Google Cloud regions, and CVS is now available in 19 regions, allowing you to access our services wherever you are around the globe. All of this is backed up by a 99.99% SLA for CVS, enabling high availability for your enterprise workloads.
Hundreds of customers including MailerLite, a leader in marketing automation, have benefitted from our joint services. MailerLite was looking to move millions of files to the cloud without disruption as they were facing availability, management, and maintenance challenges as their business scaled. By leveraging NetApp CVS on Google Cloud, MailerLite was able to migrate over 100 million files to Google Cloud in about an hour, removing their ongoing infrastructure overhead, saving developer hours, and eliminating the scalability limitations they previously experienced.
Expanding our partnership
Last week at Google Cloud Next ‘21, we announced we are expanding our strategic partnership in two ways to make it even easier for customers to migrate and run business critical applications on Google Cloud. First, NetApp will provide storage infrastructure for the newly announced Google Distributed Cloud Hosted, where data resides in customer-owned data centers and colocation facilities. This enables a safe and secure way to modernize an on-premises deployment while still meeting data security and privacy requirements.
Additionally, for customers looking to migrate their VMware workloads to Google Cloud, we announced preview access for NetApp Cloud Volumes as datastores for Google Cloud VMware Engine. By providing customers with the choice to scale compute and storage independently, customers can now reduce costs and complexity when migrating and running storage-bound workloads. This also allows customers to address regional disaster recovery use cases, making it easier and more cost-effective to move on-premises workloads into Google Cloud. To get started, you can sign up for preview access using the Registration for Preview Access form.
Looking forward
We’re excited for what’s to come with our partnership and committed to delivering value to customers across industries. Google Cloud is a Gold sponsor at this week’s NetApp Insight, and we invite you to join our spotlight session featuring Google Cloud and NetApp leaders. Additionally, we are leading multiple breakout sessions where you will hear from our product team on how you can accelerate cloud adoption with Google Cloud and NetApp. You can also learn more and get started today by visiting our NetApp on Google Cloud website.
7 Common Myths About Cloud Computing That Must Be Banished
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No discussion about the emerging technologies that are shaping our future is complete without a mention of cloud computing. Unfortunately, too many of these discussions are filled with misinformation and fear-mongering. And while it’s always advisable to approach new technologies with a degree of healthy skepticism, this should always be backed by facts.
Here are seven of the most common myths about cloud computing that you shouldn’t take at face value:
An On-Premises Data Centre is More Secure Than the Cloud
One of the first concerns cloud skeptics cite as the case against it is the possibility of a data breach or cyber attack. However, the overwhelming majority of professionals agree that cloud computing offers much more security than an on-premises data centre. Not only do cloud solutions come with the requisite infrastructure, but they’re also maintained by some of the finest security personnel in the world, who work for cloud service providers.
Cloud Computing is Too Expensive
The concerns surrounding the cost of cloud computing have been blown vastly out of proportion. While the cost of implementation varies from one business to another, cloud computing’s ‘pay-as-you-go’ model helps prevent unnecessary spending on infrastructure. In fact, some providers offer users their service without any up-front costs or termination fees. However, it is up to an enterprise to evaluate whether this model will be more cost-effective for their requirements, as opposed to building a data centre.
Migration to the Cloud Will Result in Extended Downtime
There’s a reason that the old adage ‘Time is money’ is as popular as it is. Irrespective of its industry, geography, size or nature, no organisation can afford more than the bare minimum downtime. However, cloud migration doesn’t have to be synonymous with lost time and money. In fact, most popular cloud service providers offer seamless, live migration, and may only result in negligible downtime where the existing servers are extremely outdated.
My Business is Too Small for Cloud Computing
Cloud computing was once thought of as the prerogative of big companies that have massive computing power and infrastructure needs. However, a quick glance through the pros and cons of the technology shows that this isn’t the case. In fact, small and medium organisations might just find it to be more useful. Not only does cloud adoption save the cost of building a data centre, but also the human resource expenditure that comes with having to maintain it. Moreover, offerings like Google Apps for Business consolidate a multitude of services into one platform, making them easier to implement and maintain.
Cloud Computing is Only Good for Storage and Analytics
Data storage and analytics are among the cloud’s most popular uses. However, this doesn’t mean that the technology is only of use to technology and data science departments. Figures from 2011 showed that the bulk of cloud spending in the Asia Pacific region is concentrated in the functions of customer service, marketing, sales, manufacturing and human resource management. Seamless flow of accurate and up-to-date information is department-agnostic, and if leveraged in the right way, can be a game-changer for every organisation.
I Won’t Have Enough Control on the Cloud
Cloud solutions aren’t as rigid as most people assume. Between the public, private and hybrid cloud, there are a host of options that businesses can choose from when it comes to cloud migration. Each of these offers a different degree of customisation and flexibility, which companies can pick from, on the basis of their requirements and skill level.
Cloud Computing is Digital Transformation
Deploying cloud solutions can put organisations on the fast track to success in the digital age. However, it is far from the only component that is needed to make the big transition. It’s not unheard of for vendors and IT professionals to make cloud computing seem like the be-all and end-all solution to all of a business’ digital woes. This is why, senior management should have a clearly defined purpose and realistic expectations in mind before migrating to the cloud.
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