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

Exabeam, a leader in SIEM and XDR, provides security operations teams with end-to-end Threat Detection, Investigation, and Response (TDIR) by leveraging a combination of user and entity behavioral analytics (UEBA) and security orchestration, automation, and response (SOAR) to allow organizations to quickly resolve cybersecurity threats. As the company looked to take its cybersecurity solution to the next level, Exabeam partnered with Google Cloud to unlock its ability to scale for storage, ingestion, and analysis of security data.

Harnessing the power of Google Cloud products including BigQuery, Dataflow, Looker, Spanner and Bigtable, the company is now able to ingest data from more than 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost-effective way. The scale and power of Google Cloud enables Exabeam customers to search multi-year data and detect threats in seconds

Google Cloud provides Exabeam with three critical benefits.

• Global scale security platform. Exabeam leveraged serverless Google Cloud data products to speed up platform development. The Exabeam platform supports horizontal scale with built-in resiliency (backed by 99.99% reliability) and data backups in three other zones per region. Also, multi-tenancy with tenant data separation, data masking, and encryption in transit and at rest are backed up in the data cloud products Exabeam uses from Google Cloud.

• Scale data ingestion and processing. By leveraging Google’s compute capabilities, Exabeam can differentiate itself from other security vendors that are still struggling to process large volumes of data. With Google Cloud, Exabeam can provide a path to scale data processing pipelines. This allows Exabeam to offer robust processing to model threat scenarios with data from more than 500 security and IT vendors in near-real time.

• Search and detection in seconds. Traditionally, security solutions break down data into silos to offer efficient and cost-effective search. Thanks to the speed and capacity of BigQuery, Security Operations teams can search across different tiers of data in near real time. The ability to search data more than a year old in seconds, for example, can help security teams hunt for threats simultaneously across recent and historical data.

Exabeam joins more than 700 tech companies powering their products and businesses using data cloud products from Google, such as BigQuery, Looker, Spanner, and Vertex AI. Google Cloud announced the Built with BigQuery initiative at the Google Data Cloud Summit in April, which helps Independent Software Vendors like Exabeam build applications using data and machine learning products. By providing dedicated access to technology, expertise, and go-to-market programs, this initiative can help tech companies accelerate, optimize, and amplify their success.

Google’s data cloud provides a complete platform for building data-driven applications like those from Exabeam — from simplified data ingestion, processing, and storage to powerful analytics, AI, ML, and data sharing capabilities — all integrated with the open, secure, and sustainable Google Cloud platform. With a diverse partner ecosystem and support for multi-cloud, open-source tools, and APIs, Google Cloud can help provide technology companies the portability and the extensibility they need to avoid data lock-in.

To learn more about Exabeam on Google Cloud, visit www.exabeam.com. Click here to learn more about Google Cloud’s Built with BigQuery initiative.

At AirAsia, we operate a fleet of more than 270 aircraft across 23 markets, fly to more than 150 destinations and carry 100m guests each year. We’ve also been named the world’s best low-cost carrier for 11 years running. To accomplish all of this, we rely heavily on our 22,000 Allstars (employees). As AirAsia co-founder Tony Fernandes likes to say, “it has always been about the people.” 

In my role as CIO, it’s critical that I give our Allstars the tools and technology they need to get their jobs done, while at the same time ensuring that our company’s data is protected and secure. While this is challenging enough in normal circumstances, we’re also in the midst of rapidly moving from legacy on-premises technologies to the cloud. Google Cloud has been a critical partner for us on this journey.

Identity challenges
AirAsia, like many other enterprises, has relied on a legacy on-premises directory for many years. As our company has quickly grown and expanded to new markets and regions, we’ve had to manage multiple servers across a number of on-premises data centers and the public cloud, which has proved costly and time-consuming.

Our Allstars, located all across Asia, need to easily access a number of legacy on-premises apps in addition to a growing number of SaaS apps. As a business, we also needed a more seamless integration between our HR system of record and our identity solution for user provisioning and lifecycle management. Solving these challenges with our existing on-premises directory was simply not feasible for us.

In recent years, we partnered with Google Cloud to help drive our digital transformation, including moving dozens of workloads and apps to Google Cloud Platform (GCP), deploying G Suite as our collaboration and productivity solution for all of our employees, and replacing thousands of Windows laptops with fast and secure Chromebooks.

We brought up our identity concerns with the Google Cloud team, and after a number of conversations, we decided to deploy Cloud Identity, Google’s cloud-based identity, and access management solution, to help address the identity challenges we were facing. 

Why Cloud Identity for AirAsia?
We ended up choosing Cloud Identity for a few key reasons. Here at AirAsia, we are eager to move to the cloud as quickly as possible, and moving identity management to the cloud is a key enabler of this and our broader digital transformation. Managing identities from the cloud also enables us to have a single identity and set of credentials for each employee, which they can use to access all of the applications they need to be productive, both in the cloud and on-premises. 

In addition, deploying Cloud Identity is a key step towards enabling the BeyondCorp (or zero trust) security model, which we feel is the best approach to strengthen our security posture and fight modern threats. Cloud Identity also integrates seamlessly with our existing technologies, which includes not only Google Cloud products like GCP, G Suite, and Chrome OS, but also third-party tools like Citrix, Papercut, and others.

And finally, Cloud Identity offered us significant cost and resource savings. With Cloud Identity in place, our IT department can spend less time worrying about managing multiple on-premises directory servers and can instead focus on delivering value to our Allstar employees.

About AgroStar

Launched as an on-premises ecommerce platform selling farm supplies in 2008, AgroStar turned to Google Cloud to expand its offering. Today one million small farmers in India use its mobile app to connect to a full-service platform that combines agronomy, data science, machine learning, and analytics to boost yields and improve income.

Industries: Industries: Agriculture, Forestry & Fishing

Location: India

AgroStar launched a multilingual mobile app using Google Cloud that is helping to boost crop yields and increase income for small farmers in India.

Google Cloud results

• Enables technology modules to speed loan processing, identify crop diseases, and enhance supply chain logistics
• Delivers high rates of analytics processing to serve more customers while improving response times by 85%
• Streamlines deployments with no downtime to free time and resources for new verticals
• Supports knowledge center updates for 1 million mobile devices in near real time by implementing Firebase

An app to boost crop yields and raise income
AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on Google Cloud, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When Google Cloud service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on Google Cloud.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to Google Cloud streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. Google Cloud has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for Google Cloud, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that Google Cloud offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

Contributors to this story

Pritesh Gudge: AgroStar Software Engineer. Pritesh was formerly a founder of Bauersafe, a farm protection solutions startup, and First Fit, a startup that focuses on mobility and functional techniques to promote general fitness and recovery. Pritesh earned a B.S. in Computer Science and Engineering at the Birla Institute of Technology and Science as well as degrees in Robotics and Deep Learning from Udacity.

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.