As their name reflects, DueDil provides due diligence services ranging from customer-specific risk evaluations and selections to customer onboarding and real-time risk monitoring for leading financial services, high-growth tech and insurance companies. Founded in 2009, the company helps more than 3,000 enterprise users from over 400 clients to not only understand with whom they’re doing business, but to do so with increased efficiency and in compliance with regulatory requirements. 

Due diligence services have evolved in recent years, both because of new regulations and new technologies supplanting legacy systems and processes, many of which relied until recently on pen-and-paper workflows or exhaustive spreadsheet work. DueDil knew this technology transformation represented an opportunity to replace manual processes with automation–but it also recognized a second opportunity: to not merely process data but also activate it by connecting information in disparate IT systems and generating data-driven insights delivered at scale.  

To capitalize on this opportunity, the company built its Business Information Graph, or B.I.G., a platform that maps approximately 300 million connections among companies. B.I.G. ingests billions of data points, and is refreshed multiple times per day, to surface unique insights about business’s relationships, such as fraud risks. The results that B.I.G. drives often speak for themselves: some DueDil customers onboard partners up to 80% faster, perform risk verification up to 18 times faster, and reduce time spent on manual portfolio checks by up to 80%. 

What powers all of this transformation? Application Programming Interfaces (APIs). 

“From a go-to-market standpoint, our product is an API,” said Denis Dorval, DueDil COO, in a recent webcast, explaining that customers can directly tap B.I.G.’s resources for themselves, and build atop them for their own needs, via DueDil’s API. 

Choosing an API management platform to deliver fast, secure, and scalable APIs

To execute on their vision of connecting B2B ecosystems for better insights and efficiency, DueDil looked for a cloud provider that could fulfill several specific criteria. They needed robust management for the APIs with which their internal developers leverage different systems for new use cases and process automations, as well as for the productized API they offer to customers. They needed sophisticated analytics and abundant processing power to crunch through billions of data points. And, they needed enterprise-grade security, scalability, and agility to underpin it all. Last but not least, the company prioritized a smooth transition; DueDil did not want the user experience to suffer as it switched providers.

“The stability of Google Cloud’s Apigee API management platform and the strength of its services stood out”, said DueDil’s Engineering Manager, Robert Cicero. 

“Apigee is a resilient and agile platform, fulfilling our need to build APIs quickly, safely, and at scale,” he remarked, noting that he appreciated that many of Apigee’s API security defense tools and policies work out-of-the-box. For instance, Apigee’s JSON threat detection policies, custom policies, and authentication and authorization processes can be deployed instantly and add minimal latency, meaning DueDil can stop security threats before they enter its network while still avoiding the risk of service lags.

Today, DueDil has five internal services that facilitate business due diligence, all exposed via Apigee. They also use Apigee’s monetization feature to drive API consumption. This said, because DueDil’s go-to-market strategy is fast-paced and client-oriented, they most often use Apigee to rapidly prototype APIs for their clients, so they can understand what a specific API would look like and how it would behave. This allows DueDil, its partners, and its customers to spend more time delivering value from insights rather than getting bogged down in building backend systems. 

Moreover, Apigee made it simpler to also connect to other Google Cloud services, such as BigQuery, Google Data Studio, and Google Cloud Storage. Apigee acts as a central nervous system among systems, giving DueDil not only the ability to connect systems and automate processes but also insight and visibility into how its B.I.G. services are being used by partners and customers. 

Plus, added Cicero, “the migration to Apigee was seamless, with arguably our biggest win being that no one knew that we had switched API management providers to Apigee.”  

Leveraging APIs to provide self-service while enforcing security and governance policies

Moving forward, DueDil plans to leverage Apigee to give staff members and clients more privileges, visibility, and opportunity to create and edit apps in a self-service manner, without needing to rely on an IT department or endure long approvals processes. Harnessing APIs to open up B.I.G. and other capabilities to more teams across the company will also allow DueDil to move faster and include more people in the innovation process. Leveraging Apigee API management capabilities, DueDil also intends to dive deeper and experiment with other Google Cloud products and services, including Cloud Function, Cloud Pub/Sub, and more.

“At the end of the day, every company goes about due diligence a little differently. The only way that we at DueDil are able to provide something that is configurable and dynamic to diverse businesses is if we use platforms that can adapt, too,” said Cicero. “Apigee gives us the agility required to create and deliver for a wide variety of businesses.”

Google Cloud, today, works across banking, capitalmarkets, insurance, and payments worldwide to solve their most challenging problems. Click here to learn more about how Google Cloud Apigee API management can help you design, secure, analyze, and scale APIs anywhere with visibility and control. To try Apigee API management for free, click here.

This week, the Vera C. Rubin Observatory is launching the first preview of its new Rubin Science Platform (RSP) for an initial cohort of astronomers. The observatory, which is located in Chile but managed by the U.S. National Science Foundation’s NOIRLab in Tucson, AZ and SLAC in California, is jointly funded by the NSF and the U.S. Department of Energy. The platform provides an easy-to-use interface to store and analyze the massive datasets of the Legacy Survey of Space and Time (LSST), which will survey a third of the sky each night for ten years, detecting billions of stars and galaxies, and millions of supernovae, variable stars, and small bodies in our Solar System.

The LSST datasets are unprecedented in size and complexity, and will be far too large for scientists to download to their personal computers for analysis. Instead, scientists will use the RSP to process, query, visualize, and analyze the LSST data archives through a mixture of web portal, notebook, and other virtual data analysis services. An initial launch with simulated data, called Data Preview 0, builds on the Rubin Observatory’s three-year partnership with Google to develop an Interim Data Facility (IDF) on Google Cloud to prototype hosting of the massive LSST dataset. This agreement marks the first time a cloud-based data facility has been used for an astronomy application of this magnitude.

Bringing the stars to the cloud

For Data Preview 0, the IDF leverages Cloud Storage, Google Kubernetes Engine (GKE), and Compute Engine to provide the Rubin Observatory user community access to simulated LSST data in an early version of the RSP. The simulated data were developed over several years by the LSST Dark Energy Science Collaboration to imitate five years of an LSST-like survey over 300 square degrees of the sky (about 1,500 times the area of the moon). The resulting images are very realistic: they have the same instrumental characteristics, such as pixel size and sensitivity to photons, that are expected from the Rubin Observatory’s LSST Camera, and they were processed with an early version of the LSST Science Pipelines that will eventually be used to process LSST data. “This will be the first time that these workloads have ever been hosted in a cloud environment. Researchers will have an opportunity to explore an early version of this platform,” says Ranpal Gill, senior manager and head of communications at the Rubin Observatory.

Broadening access for more researchers

Over 200 scientists and students with Rubin Observatory data rights were selected to participate in Data Preview 0 from a pool of applicants that represents a wide range of demographic criteria, regions, and experience level. Participants will be supported with resources such as tutorials, seminars, communication channels, and networking opportunities—and they will be free to pursue their own science at their own pace using the data in the RSP. 

“The revolutionary nature of the future LSST dataset requires a commensurately innovative system for data access and analysis paired with robust support for scientists,” says Melissa Graham, lead community scientist for the Rubin Observatory and research scientist in the astronomy department at the University of Washington. “I’m personally excited to enhance my own skills by using the RSP’s tools for big data analysis, while also helping others to learn and to pursue their LSST-related science goals during Data Preview 0.” 

At the same time, the fact that the RSP is hosted in the cloud provides researchers at smaller institutions access to state-of-the-art astronomy infrastructure that is comparable to that of the largest national research centers.

The launch benefits the observatory too: the development team can learn what researchers are interested in while also testing and debugging the platform. Graham says that “the platform is still in active development so researchers using it will be able to follow along in the progress, and provide feedback on ways that we can optimize the development of the tools.”

Next steps

The LSST aims to begin the ten-year survey in 2023-24 and expects it to include 500 petabytes of data. Through the cloud, Google aims to help make this extraordinary project scalable and accessible to researchers everywhere. To learn more about Data Preview 0, watch this video.

Want to ramp up your own research in the cloud? We offer research credits to academics using Google Cloud for qualifying projects in eligible countries. You can find our application form on Google Cloud’s website or contact our sales team.

Most large enterprises started with their own data centers and developed in-house solutions to meet their specific business needs, regulations, and industry specifications. These solutions were based on traditional applications from legacy vendors like Oracle and Microsoft — or even mainframes — and from key technology providers like SAP and VMware. During the last decade, organizations started their digital transformation journey by migrating their IT infrastructure, applications, and data to the cloud to reduce operating costs and gain efficiencies as their needs scaled.

The next step in the journey was to take advantage of cloud-based data analytics, services and scalability to drive business growth and revenue. Rather than offer generic “industry clouds” which require multi-year IT deployments and significant customization, Google Cloud has responded to customer needs by developing production-ready industry solutions that address specific use cases with repeatability across organizations.

These industry solutions are grounded in Google Cloud’s differentiated capabilities, including AI, ML, and data analytics. With these, you can dramatically reduce implementation time and realize value from the cloud more quickly with deep insights into your customers and more efficient interactions with your suppliers and partners.

Here are some highlights from the That Digital Show Podcast where Umesh Vemuri, VP of Global Strategic Customers and Industries at Google Cloud, discusses his industry strategy.

Why Google Cloud solutions?

“We are uniquely positioned to help enterprises with solutions based on our deep understanding of customer needs that are supported by our technologies. There are three examples that come to mind.
The first is our understanding of the consumer from the standpoint of running an ecommerce platform. We have the solutions to run digital platforms at a very large scale, including the engineering practices we bring to support organizations during important high traffic events like Black Friday and Cyber Monday.

The second example deals with media and entertainment as a whole. We operate the world’s largest streaming service and support the largest digital platforms currently out there. That means that whatever problems our media customers are experiencing, there’s a good chance we’ve already seen them and know how to deal with them.

Finally, there is our experience with AI and big data. Our leadership in deploying our AI and big data healthcare technologies has given us the experience to solve complex problems — such as techniques to support evidence-based selection, drug therapy, and molecular profiling.”

B to B to C

“Google is a B to B organization, I always like to add that we are also B to B to C, because the concern the consumer ultimately has is their experiences and the journey that they’re on. Connecting the dots between the business and the consumer experience is really critical and a real big differentiator in how we could better serve our customers.

For example, take Ford Motors. Really think about the challenges they have — traditional problems around manufacturing, core I. T. modernization and information, and how to remove costs. But then when you really think about the core of the business, how do you actually make this incredible experience for drivers of Ford vehicles? What do you want to do from an infotainment perspective? What do you want to do in speech-to-text conversion?

And suddenly your business is really a direct-to-consumer experience. Ultimately, all the infrastructure and technology is designed to provide the consumer — who’s ultimately buying that vehicle — with an amazing experience that will maintain their loyalty.

From this example, we can see this kind of linkage in every industry: from retail and e-commerce, media, and direct streaming to healthcare with direct telehealth.”

Solutions that transform the consumer experience

“The consumer’s expectations are constantly shifting and we have to be able to provide the technologies, the structure, and the solutions to our customers to be able to meet those changing expectations at that consumer level.

So first, we want to be very clear on the industry segments that we’re going to focus on and what we believe our clear differentiation will be. So we’ve focussed on ten industries including: retail, financial services, manufacturing, telecommunications, media and entertainment, healthcare and life sciences, education, government, supply chain and logistics and gaming.

Second, we really have to be very prescriptive about the solution pillars in the areas where our customers tell us we have challenges. We want to build solutions that solve not only today’s issues, but the problems of the future.

And third, in those pillars, we have to be very clear on what are the specific use cases that we think have high value for our customers. Then make these available as a catalog of actual products and production-ready solutions that we and our partners in the ecosystem provide.”

Google Cloud industry solutions focus on our top ten industries where we can provide differentiated value to organizations. Whether it’s discovery in retail, AI-enabled call centers, or automotive tools for connected cars, we are delivering production-ready solutions that are ready to implement with minimum customization.

Compute Engine is a customizable compute service that lets you create and run virtual machines on Google’s infrastructure. You can create a Virtual Machine (VM) that fits your needs. Predefined machine types are pre-built and ready-to-go configurations of VMs with specific amounts of vCPU and memory to start running apps quickly. With Custom Machine Types, you can create virtual machines with the optimal amount of CPU and memory for your workloads. This allows you to tailor your infrastructure to your workload. If requirements change, using the stop/start feature you can move your workload to a smaller or larger Custom Machine Type instance, or to a predefined configuration.

Machine types

In Compute Engine, machine types are grouped and curated by families for different workloads. You can choose from general-purpose, memory-optimized, compute-optimized and accelerator-optimized families. 

• General-purpose machines are used for Day-to-day computing at a lower cost and for balanced price/performance across a wide range of VM shapes. The use cases that best fit here are web serving, app serving, back office applications, databases, cache, media-streaming, microservices, virtual desktops, development environments.
• Memory-Optimized machine are recommended for ultra high-memory workloads such as in-memory analytics and large in-memory databases such as SAP HANA 
• Compute-Optimized machines are recommended for ultra high performance workloads such as High Performance Computing (HPC), Electronic Design Automation (EDA), gaming, video transcoding, single-threaded applications.
• Accelerator-Optimized machines are optimized for high performance computing workloads such as Machine learning (ML), Massive parallelized computations and High Performance Computing (HPC)

How does it work?

You can create a VM instance using a boot disk image, a boot disk snapshot, or a container image. The image can be a public operating system (OS) image or a custom one. Depending on where your users are you can define the zone you want the virtual machine to be created in. By default all traffic from the internet is blocked by the firewall and you can enable the HTTP(s) traffic if needed. 

Use snapshot schedules (hourly, daily, or weekly) as a best practice to back up your Compute Engine workloads. Compute Engine offers live migration by default to keep your virtual machine instances running even when software or hardware update occurs. Your running instances are migrated to another host in the same zone instead of requiring your VMs to be rebooted. 

Availability

For High Availability (HA) Compute Engine offers automatic failover to other regions or zones in event of a failure. Managed instance groups (MIGs) help keep the instances running by automatically replicating instances from a predefined image. They also provide application based autohealing health checks. If an application is not responding on a VM, the auto healer automatically recreates that VM for you. Regional MIGs let you spread app load across multiple zones. This replication protects against zonal failures. MIGs work with load balancing services to distribute traffic across all of the instances in the group. 

Compute Engine offers autoscaling to automatically add or remove VM instances from a managed instance group based on increases or decreases in load. Autoscaling lets your apps gracefully handle increases in traffic, and it reduces cost when the need for resources is lower. You define the autoscaling policy for automatic scaling based on the measured load, CPU utilization, requests per second or other metrics.

Active Assist’s new feature, predictive autoscaling, helps improve response times for your applications–When you enable predictive autoscaling, Compute Engine forecasts future load based on your Managed Instance Group’s (MIG) history and scales it out 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. That makes predictive autoscaling ideal for those apps with long initialization times and whose workloads vary predictably with daily or weekly cycles. For more information, see How predictive autoscaling works or check if predictive autoscaling is suitable for your workload, and to learn more about other intelligent features, check out Active Assist.

Pricing

You pay for what you use. But you can save cost by taking advantage of some discounts! Sustained use saving are automatic discounts applied for running instances for a significant portion of the month. If you know your usage upfront, you can take advantage of committed use discounts which can lead up to significant savings without any upfront cost. And by using short lived preemptive instances you can save up to 80%, they are great for batch jobs and fault tolerant workloads. You can also optimize resource utilization with automatic recommendations. For example if you are using a bigger instance for a workload that can run on a smaller instance you can save costs applying these recommendations.

Security

Compute Engine provides you default hardware security. Using Identity and Access Management (IAM) you just have to ensure that proper permissions are given to control access to your VM resources. All the other basic security principles apply, if the resources are not related and don’t require network communication amongst themselves, consider hosting them on different VPC networks. By default, users in a project can create persistent disks or copy images using any of the public images or any images that project members can access through IAM roles. You may want to restrict your project members so that they can create boot disks only from images that contain approved software that meet your policy or security requirements. You can define an organization policy that only allows Compute Engine VMs to be created from approved images. This can be done by using the Trusted Images Policy to enforce images that can be used in your organization. 

By default all VM families are Shielded VMs. Shielded VMs are virtual machine instances that are hardened with a set of easily configurable security features to ensure that when your VM boots, it’s running a verified bootloader and kernel — is the default for everyone using Compute Engine, at no additional charge. For more details on Shielded VMs refer to the documentation here.

For additional security, you also have the option to use Confidential VM to encrypt your data in use, while it’s being processed in Compute Engine. For more details on Confidential VM refer to the documentation here.

Use cases

There are many use cases Compute Engine can serve in addition to running websites and databases. You can also migrate your existing systems onto Google Cloud, with Migrate for Compute Engine, enabling you to run stateful workloads in the cloud within minutes rather than days or weeks. Windows, Oracle or VMware applications have solution sets enabling a smooth transition to Google Cloud. To run windows applications either bring your own license leveraging Sole-tenant nodes or using the included licenced images. 

Conclusion

Whatever your application use case may be, from legacy enterprise applications to digital native applications, Compute Engine’s families will fit it. For a more in-depth look into Compute Engine check out the documentation. 

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev

Our customers tell us they don’t just want to migrate their applications from point A to point B, they want to modernize their applications with cloud-native technologies and techniques, wherever those applications may be. 

Today, we’re excited to tell you about a variety of new customers that are using Anthos to transform their application portfolio, as well as new cloud migration, API management, and application development offerings:

• New customers leveraging Anthos for a variety of on-prem, cloud and edge use cases
• The general availability of Migrate for Anthos
• Apigee hybrid in general availability
• The general availability of Cloud Code

Accelerating app modernization with Anthos

Anthos was the first open app modernization platform to offer a unified control plane and service delivery across diverse cloud environments—managed cloud, on-premises and edge. Since it became generally available in the spring, organizations across a variety of industries and geographies have turned to Anthos to bring the benefits of cloud, containers and microservices to their applications. 

According to the findings from Forrester’s Total Economic Impact study, customers adopting Anthos have seen up to 5x return on investment based on the savings from ongoing license and support costs, and the incremental savings from operations and developer productivity. For one customer in the financial services industry, rolling out new features and updates to their core banking application used to take at least a quarter. Now with Anthos, they were able to eliminate months long development and release cycles, and roll out on a weekly basis. That’s a 13x improvement on time to market. 

This week, several new European Anthos customers will take the stage at Next UK to talk about how they’re using Anthos to transform their IT operations. 

Kaeser Kompressoren SE of Coburg, Germany, is a provider of compressed air products and services. The company needed a consistent platform to deploy and manage existing on-prem SAP workloads, like SAP Data Hub, and also wanted to be able to tap into other services running in Google Cloud to get more value from those environments. 

“Application modernization is enabling business innovation for Kaeser,” said Falko Lameter, CIO. “To gain better insights from data, we knew we needed to incorporate advanced machine learning and data analytics in all our applications. We chose Google Cloud’s Anthos because it offered the flexibility to incrementally modernize our legacy application on-premises without business disruption, while allowing us to run other applications on Anthos in Google Cloud and take advantage of its managed data analytics and ML/AI services.”

Then there’s Denizbank. Based in Turkey, Denizbank provides a variety of commercial banking services, and established the first Digital Banking Department in Turkey in 2012. Denizbank turned to Anthos for an open application modernization platform to help it develop its next-generation mobile banking applications.

“We operate in 11 different countries and have to comply with various regulatory requirements like data locality and sovereignty, which mandates some or all applications to reside on premises in certain countries, while the rest of the apps can move to the cloud in other countries,” said Dilek Duman, COO of DenizBank. “We chose Google Cloud’s Anthos for its flexibility to modernize our existing application investments with ease, and to deliver AI/ML powered software faster while improving operational security and governance. Anthos gives us the ability to have a unified management view of our hybrid deployments, giving us a consistent platform to run our banking workloads across environments.” 

Anthos is even starting to be deployed to edge locations, where, thanks to its 100% software-based design, it can run on any number of hardware form factors. We’re in advanced discussions with customers in telecommunications, retail, manufacturing and entertainment about using Anthos for edge use cases, as well as with global hardware OEMs.

Move and modernize with Migrate for Anthos

In addition to leveraging cloud technology for their on-premises environments with Anthos, customers also want to simultaneously migrate to the cloud and modernize with containers. That’s why we’re happy to announce the general availability of Migrate for Anthos, which provides a fast, low-friction path to convert physical servers or virtual machines from a variety of sources (on-prem, Amazon AWS, Microsoft Azure, or Google Compute Engine) directly into containers in Anthos GKE.

Migrate for Anthos makes it easy to modernize your applications without a lot of manual effort or specialized training. After upgrading your on-prem systems to containers with Migrate for Anthos, you’ll benefit from a reduction in OS-level management and maintenance, more efficient resource utilization, and easy integration with Google Cloud services for data analytics, AI and ML, and more. 

DevFactory aims to offload repetitive tasks in software development so that dev teams can focus on coding and productivity. As advocates for optimization through containers, they found Migrate for Anthos a key way to help deliver on their goals:  

“We usually see less than 1% resource utilization in data centers. Migrate for Anthos is a remarkable tool that allows us to migrate data center workloads to the cloud in a few simple steps,” said Rahul Subramaniam, CEO, Devfactory. “By automatically converting servers and virtual machines into containers with Migrate for Anthos, we get better resource utilization and dramatically reduced costs along with managed infrastructure in the end state, which makes this a very exciting and much-needed solution.” 

Migrate for Anthos is available at no additional cost, and can be used with or without an Anthos subscription.

API-first, everywhere, with Apigee hybrid 

To drive modernization and innovation, enterprises are increasingly adopting API-first approaches to connecting services across hybrid and multi-cloud environments. To address the need for hybrid API management, we’re announcing the general availability of Apigee hybrid, giving you the flexibility to deploy your API runtimes in a hybrid environment, while using cloud-based Apigee capabilities such as developer portals, API monitoring, and analytics. Apigee hybrid can be deployed as a workload on Anthos, giving you the benefits of an integrated Google Cloud stack, with Anthos’ automation and security benefits. 

Gap Inc. uses Apigee to publish, secure, and analyze APIs and easily onboard the development teams working with those APIs. Apigee hybrid will help Gap Inc. overcome the traditional tradeoffs between on-premises and cloud, providing the best of both worlds.   

“With Apigee hybrid, we can have an easy to manage, localized runtime for scenarios where latency or data sensitivity require it. At the same time, we can continue to enjoy all the benefits of Apigee such as Apigee’s developer portal and its rich API-lifecycle management capabilities,” said Patrick McMichael, Enterprise Architect at Gap Inc. 

Simplifying the developer experience

Google Cloud application development tools are designed to help you simplify creating apps for containers and Kubernetes, incorporate security and compliance into your pipelines, and scale up or down depending on demand, so you only pay for what you use. 

With these goals in mind, last week, we announced the general availability of Cloud Run and Cloud Run for Anthos. Cloud Run is a managed compute platform on Google Cloud that lets you run serverless containers on on a fully managed environment or on Anthos. With Cloud Run fully managed, you can easily deploy and run stateless containers written in any language, and enjoy serverless benefits such as automatic scale up and scale down and pay-for-use—without having to manage the underlying infrastructure. 

Cloud Run for Anthos, meanwhile, brings those same serverless developer experience to Anthos managed clusters, giving developers access to a modern, serverless compute platform while their organization modernizes its on-prem environment with Kubernetes. 

Easier Kubernetes development with Cloud Code

Today, we’re excited to announce the general availability of another important member of the Google Cloud application development stack: Cloud Code, which lets developers write, debug and deploy code to Google Cloud or any Kubernetes cluster through extensions to popular Integrated Developer Environments (IDEs) such as Visual Studio Code and IntelliJ. 

Developers are most productive while working in their favorite IDE. By embracing developers’ existing workflow and tools, Cloud Code makes working with Kubernetes feel like you are working with a local application, while preserving the investment you’ve made to configure your tools to your own specific needs. Cloud Code dramatically simplifies the creation and maintenance of Kubernetes applications.

In addition, Cloud Code speeds up development against Kubernetes by extending the edit-debug-review “inner loop” to the cloud. You get rapid feedback on your changes, ensuring that they’re of high quality. And when it comes to moving code to the production environment, Cloud Code supports popular continuous integration and delivery (CI/CD) tools like Cloud Build. 

Finally, with Cloud Code, diagnosing issues does not require a deep understanding of Kubernetes, thanks to connected debuggers and cluster-wide logging that help you address issues all from the context of your favorite tool. 

Toward modern, efficient applications

Application modernization means a lot of things to a lot of people. Depending on your environment, it can mean updating VMs to containers and Kubernetes, it can mean moving them to the cloud, or it can mean distributing them to edge locations and unifying workloads with consistent API and service management. For others, application modernization means using cloud-native tools and concepts like serverless and CI/CD. Whatever your definition, we can help you realize your business and modernization goals, achieving greater agility while improving overall governance.