Reaping Business and Operational Benefits by Moving Analytics and Machine Learning to Hybrid Cloud
Chapter 1: Why Hybrid Cloud and Why Now?
Hybrid cloud lets businesses deploy workloads and data on a mix of on-premises, private cloud, or public cloud infrastructure. That gives businesses great flexibility and allows them to optimize workloads by selectively matching infrastructure to needs. And as such, hybrid cloud is ideally suited to the evolving infrastructure needs of modern business
How much interest is there in hybrid cloud? Some industry surveys found hybrid cloud adoption is on the rise. Roughly half (48 percent) of the respondents in one survey plan to migrate 50 percent or more of their applications to a cloud this year. That aligns with the findings of another survey conducted last year that found that more than a third (38 percent) of the organizations already had a hybrid cloud strategy in place.
What’s driving the interest in hybrid? Hybrid cloud goes well with the embracement of cloud-native development and the composable enterprise. New applications are being built as an assembly of components based on microservices and APIs. Similarly, old monolithic applications are being deconstructed, offering up particular services, code, or data for use in new applications. A hybrid cloud strategy allows businesses to run different elements of such distributed applications where they would run best in the most cost-effective manner.
Digital transformation is another driver for the adoption of hybrid cloud. Digital transformation is about developing innovative offerings quickly and meeting customer availability and app performance expectations. A hybrid cloud strategy makes that all possible so that software developers can iterate as quickly as possible without having to wait for IT to set up servers, storage, and networking on-premises. Yet, it allows a business to keep apps and data that need to be secured on-premises.
Hybrid cloud also gives businesses the ability to place and shift workloads and data to the rig right infrastructure as requirements evolve. For example, it might make sense to move data on a public cloud back on-premises or to a private cloud if data privacy regulations are strengthened in a particular region. Similarly, an AI or ML model might be proven out on-premises but moved to a highly-scalable public cloud when deployed in production so that the needed compute power is available.
Such deployment flexibility is increasingly essential. Some applications will be best targeted to one type of cloud early in its development life cycle, only to have that shift to another later on. For example, it is quite common for a new consumer application to have wildly varying demand when first released but over time to have its usage settle. A hybrid approach gives a business the option to use a public cloud to quickly scale and meet surges in demand but then bring an app back on-premises when demand plateaus to reduce costs.
Past, present, and future
The old adage “what’s old is new again” rings true for hybrid cloud. Back in 2009, the National Institute of Standards and Technology (NIST) started developing a working definition of cloud computing. After 16 iterations, it finalized its work, noting that multiple deployment models included private, public, and hybrid, which together offered a new way to deliver services.
The NIST touted reasons for using hybrid cloud back then were that “cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” And it noted that organizations that used cloud were more likely to reap the promised benefits of cloud—cost savings, energy savings, rapid deployment, and customer empowerment.
It is safe to say those insights hold true today. But what of the future? Hybrid cloud reflects enterprise IT’s overall and ongoing shift from a centralized to a distributed model. And perhaps the best indication of hybrid cloud’s value going forward is the synergies it brings with edge.
Edge is exploding. Sensors, IoT devices, and other data-producing units provide status and operational data for everything from an IT device to equipment on a production line to a pallet in a tracker trailer. Rapid analysis of that data is key to many aspects of business operations.
As such, there is a great overlap between hybrid cloud and edge. Hybrid cloud is about running work-loads in the best possible environments; edge computing is about bringing the environment to a workload and its associated data.
A last word
Hybrid deployments are the new norm. Most businesses are shifting workloads to the cloud or building new cloud-native applications from scratch. With few exceptions (e.g., pure cloud-native startups), workloads and data will run and reside on a combination of on-premises systems, private clouds, and public cloud services.
Melding these deployment options gives businesses the ability to support digital transformation efforts while also scaling existing applications and services.
Chapter 2: How Hybrid Cloud Can Be Used For Analytics and Machine Learning
Analytics and machine learning (ML) are some of the fastest-growing workloads today. Businesses want to use insights derived from both for decision support, customer engagement, cost reduction, risk management, and more.
Unfortunately, businesses looking to unlock the power of analytics and ML face significant challenges. Many lack staff with expertise or training in analytics and ML. Most do not have the infrastructure for analytics and ML. Specifically, they do not have the capacity to store data needed to build and train models, nor do they have the compute power to run the models and workloads.
Making matters more complicated is that there are distinct phases in rolling out analytics and ML projects. They include building/training, tuning/testing, and deploying/running in production. Each stage has distinct compute and storage requirements. For example, the building/training stage typically uses large datasets, the tuning/testing stage needs many high-performance computing cores or instances, and the deploying/running stage needs reliable compute power that delivers results or insights in a timely manner upon which businesses can take action.
And those needs may vary greatly over time. For instance, a financial services firm might want to refine an analytics or ML-based model or application by incorporating an additional economic dataset and retrain it using that new data. Or a business might make a production version of an analytics and ML model, application, or tool available to a small set of users and, once proven of value, opt to expand its use to the entire company.
Hybrid cloud steps in
The three big public cloud providers, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud
Platform (GCP), want developers and data scientists to develop, test, and deploy machine learning
models on their clouds. Each offers multiple data storage options, including serverless databases,
data warehouses, data lakes, and NoSQL datastores. They offer popular machine learning frameworks,
including TensorFlow and PyTorch. All three offer a growing number of capabilities to support the full
analytics and ML life cycle.
Even with these options, most businesses want to keep some aspects of their analytics and ML projects on-premises. For example, a business might rely on a massive customer dataset that resides on a legacy system. It would be impractical (and most likely highly expensive) to migrate such a dataset to a public cloud. The smart move would be to keep the data where it is.
That’s where hybrid cloud plays a role. Hybrid cloud can be used to run different cloud infrastructures in an interoperable fashion to optimize a single workload. How does that help with analytics and ML? A hybrid cloud approach lets businesses essentially pick and choose the various aspects of their analytics and ML stages that they want to keep on-premises and which they want to use in the cloud. For instance, a business might rely on cloud storage to accommodate the large volumes of data needed to train models. Or they might opt to use analytics and ML-specific instances to accelerate certain workloads.
The cloud services in each area (data storage, compute power, analytics and ML acceleration via special processors, and more) provide the capabilities to scale up or down to meet the elastic demands of analytics and ML workloads.
Getting the most from hybrid cloud
Hybrid cloud offers many benefits for analytics and ML, and it is well-suited to meet the varying demands over time. However, hybrid cloud environments can be fairly complex. And that can lead to problems.
There are several issues at hand. Many tools that help manage and troubleshoot infrastructure or workload performance are designed for one environment. A business might have a grasp of what’s going on on-premises. But that does not extend to the public cloud. Additionally, while cloud providers may offer their own tools and management systems, most will not provide any help for on-premises workloads or other cloud services.
The reason this is a problem is that when building, testing, and deploying analytics and ML on a hybrid platform, these disparate tools make it very hard to troubleshoot problems, identify root cause problems, spot issues in the making, or notice anomalies that are pre-cursors to security and performance problems.
What’s needed is a solution that provides visibility across all clouds. An ideal solution would help businesses build, manage, govern, and optimize a complex hybrid cloud environment. It should also combine the management of the hardware resources of multiple public clouds with a private cloud in an on-premises data center.
Those are the basics. Increasingly, what is also needed in such a solution is the ability to orchestrate IT processes and provide cloud and cost governance, all in an automated manner. Enter Vertica, a Micro Focus line of business. Vertica’s SQL database and in-database machine learning support the entire predictive analytics process with massively parallel processing and a familiar SQL interface. And it allows companies to deploy machine learning in hybrid deployments. The bottom
line: you can operationalize ML and scale ML with Vertica.
Chapter 3: How to Prepare and Move Your Analytics and Machine Learning Projects to Hybrid Cloud
After a brief dip due to the impact of the pandemic, business analytics services resumed double-digit growth in 2021 and 2022, according to IDC. Why? There is a great need to improve business outcomes using insights from analytics and other techniques, including machine learning.
What most businesses find when undertaking new analytics and machine learning (ML) projects is that their current infrastructure is not up to the task. The projects typically need compute and storage capabilities that are not typically available in most businesses. And in many cases, they need access to new analytics and ML tools and technologies.
The situation is a perfect storm for hybrid cloud. Hybrid cloud gives businesses the flexibility to quickly scale compute and storage infrastructure. It also allows the ability to mix and match, meaning a business can selectively use cloud or stay on-premises for different aspects of their workloads and data as needed. And they change the mix over time to meet evolving requirements.
Deciding on capabilities, technologies, and more
There are many variables in play when undertaking analytics and ML projects. Adequate compute, storage, and tools must be in place to achieve a project’s business objectives. With hybrid cloud, there are many options available.
Take compute resources. If a business has made a substantial investment in on-premises high-performance computing capabilities, it can retain that investment and use the systems to analyze data on-premises or using cloud services.
In contrast, if a business does not have the existing compute infrastructure, a hybrid cloud approach to analytics and ML allows it to leverage cloud compute instances and resources. That saves the huge CapEx spending that would be needed to install the capabilities on-premises. And it gives the business access to newer technologies such as GPUS and ARM processors that it might not have experience with using. Such an approach also helps a business get projects going much faster than would be the case when building an on-premises compute infrastructure.
There are similar issues with storage where hybrid cloud can play an important role. Again, a business may have an existing on-premises database, its CRM or ERP system, for example. It may want to run sophisticated analytics against that database to improve operations or enhance the customer experience. With a hybrid cloud approach, the data can remain on-premises. And the analytics can be run either on-premises or using a cloud service.
Alternatively, a business might have a large cloud database or a third-party cloud database that it wants to use in an analytics project. With hybrid cloud, the database can remain where it is, hosted by a provider, and the analytics can be run on-premises or using a public cloud service.
Hybrid delivers the flexibility needed today.
The constant theme with hybrid is that businesses can retain what works, keeping workloads and storage where they are, and still get the insights their analytics projects are designed to deliver.
Why? Modern analytics and ML projects must address variables with regard to compute, storage, and tools. That alone is challenging enough, but there is even more to consider. Perhaps the most challenging aspect of many projects today is that requirements change greatly over the lifetime of a project.
For example, a company might need vast compute and storage resources to train a machine learning application. In a typical scenario, a large image dataset might be used to train the application. But once the model is trained, much less compute and storage capacity are needed. In traditional approaches, a business would have built the compute and storage infrastructure to conduct the initial phase of the project. And then that capacity would sit idle once the training is done. Hybrid cloud provides the flexibility to scale using public cloud services during that initial stage. And once the work is done, the instances and cloud resources can be dialed down.
Additional factors to consider
One particularly new area of focus that lends itself well to a hybrid approach is the growing use of external data. Many companies are now analyzing their own datasets but are then complementing that analysis over time with third-party data. A hybrid cloud approach allows that data to be used when needed to boost the quality of the analytics.
Examples abound in numerous fields. For example, a financial services organization might use third-party data to better understand its audience and target prospective customers. Or a healthcare organization might combine anonymized patient records with data from a fitness app vendor to better estimate the efficacy of a program based on the extent to which a person exercises.
One additional aspect to consider is the analytics and ML software and tools themselves. If a business has heavily invested in such solutions, it can retain them and run them wherever they work best. But many businesses are starting from scratch with modern analytics and ML projects. In such cases, they may lack the internal expertise to select, deploy, manage, and use these tools. In such cases, public cloud analytics and ML offerings can help.
For example, modern databases offer capabilities, tools, and support that are more advanced than many on-premises technologies. Many major database vendors and cloud providers offer numerous analytics and ML solutions. And more importantly, they have large ecosystems that can greatly help speed projects along. For instance, many have complementary tools for ingesting and preparing data for analysis. And some have extensive collections of tools that let a business build end-to-end hybrid cloud analysis pipelines.
Teaming with a technology partner
A good example of this is the solution from Vertica, a Micro Focus line of business. The Vertica SQL database and in-database machine learning solutions support the entire predictive analytics process
with massively parallel processing and a familiar SQL interface.
The availability of in-database machine learning offers several advantages. First, data scientists do not have to move data and wait for results when the analysis is run on poorer-performing compute platforms. That alone saves time. But with an in-database machine learning capability, processing power can be scaled as needed.
A last word
The one thing constant about modern analytics and ML projects is change. Everything is subject to change. Business objectives often change over time. Compute and storage requirements change overtime. New data is frequently added to the mix. New compute technologies and analysis methods become available over time. Hybrid cloud offers many benefits for analytics and ML, and it is well-suited to meet the varying demands over time.
By using a solution that converges databases and analytics by virtue of in-database machine learning, businesses eliminate the need to move and prep data. They can then create concise end-to-end work-flows allowing predictive analytics to be operationalized. That is a true benefit of the convergence of databases and machine learning.
Chapter 4: The Economic Impact of Transitioning to Hybrid Cloud for Analytics and Machine Learning
Businesses today are making analytics and machine learning a central part of their operations to help with everything, including improving efficiencies, hyper-personalizing customer services, and more. The dynamic nature of the workloads leads many businesses to move to hybrid cloud.
Hybrid cloud offers great scalability, cost savings, and the ability to move workloads to platforms that are optimized for the compute, storage, and data management needs of analytics and ML-intensive operations. In particular, performance and speed to results can be vastly improved when using a modern database that offers capabilities, tools, and support that are more advanced than many on-premises technologies.
That is an area where working with a company like Vertica, a Micro Focus line of business, comes into play. The Vertica SQL database and in-database machine learning solutions support the entire predictive analytics process with massively parallel processing and a familiar SQL interface.
Enabling a hybrid approach to analytics and ML
Vertica allows wide-scale use of analytics and ML throughout a business. That, in turn, helps deliver significant economic value to a business. Unfortunately, many companies need help with their
particular move to hybrid.
Addressing geographically incurred latency
Another issue is how to make use of data and databases that already exists but may not be geographically close to cloud resources. The issue here is that when moving workloads to the cloud, latency due to geographic separation may be a problem. In particular, latency becomes especially important when analytics and ML routines are run on highly-optimized platforms such as Vertica’s.
To address this issue, the Vertica platform leverages technology from Vcinity. Vcinity uses patented technology to enable the Vertica platform to process geographically dispersed data across hybrid cloud environments as if Vertica and its data are co-located. It delivers LAN-like performance regardless of distance/latency and enables applications to access data, where and when it is created.
Use case examples show what’s possible
Use case examples show what’s possible Vertica’s unified analytics platform, combined with other offerings and capabilities delivered via partnerships, have use in a broad range of applications. In all cases, the business reaps significant benefits. Some examples include:
B2C marketing: Netcore is a global Martech product company that helps B2C brands create digital customer experiences with a range of products that help in acquisi-tion, engagement, and retention. Netcore’s clients use its solutions to plan, execute, and monitor marketing cam-paigns across different channels such as Email, SMS, App, WhatsApp, and so on. Given limited budgets, the key ROI challenge for clients is to target the right customers, at the right time, on the right channels, and with the right message to maximize response rates and conversions.
To assist its clients, Netcore created Raman—an AI platform that analyzes huge datasets of historical and recent customer behavior to deliver smarter customer segmentation, improved targeting, and sophisticated predictive modeling.
As clients rapidly adopted Raman, Netcore was able to maintain the analytics performance customers expected and required using Vertica’s analytics platform’s capabilities. In particular, the company’s database was able to handle write- and read-intensive workloads in parallel without any lag or drop-in efficiency. In contrast, its existing implementations of MySQL and MongoDB were unable to handle this workload efficiently, leading to slower model refresh and analysis. One additional benefit of teaming with Vertica is that the solution easily scales without performance degradation.
Analytically-driven businesses: Vertica teamed with H3C to deliver the benefits of cloud-native analytics to enterprise data centers. Specifically, Vertica and H3C integrated their offerings to help analytically-driven companies to elastically scale capacity and performance as data volumes grow and as machine learning initiatives become a business imperative – all from within hybrid environments. Vertica with H3C ONEStor enables businesses to adopt hybrid cloud for analytics wherever their data resides. Combining these two technologies offers fast analytics while simplifying data protection with easy backup and replication features.
The combined offering delivers high-performance analytics and machine learning with enterprise- grade object storage to enable organizations to address scalability needs for now and in the future, leverage the separation of compute and storage architecture to address varying dynamic workload requirements, and simplify database operations.
eWallet app: Vertica is working with Vietnam’s largest e-wallet company, MoMo, to provide data analytics and machine learning for MoMo’s all-in-one super app, which is used for e-wallet and other FinTech services. The Vertica Unified Analytics Platform provides the company with actionable analytical insights.
MoMo needed a solution that offered the highest performance at extreme scale, the broadest analytical and machine learning capabilities, and complete support for multi-cloud and hybrid deployment to accommodate any future growth needs. Vertica met all of these requirements.
To put the requirements into perspective, as of May 2022, MoMo had 31 million users in Vietnam with 2 PB+ of data. It expects that data volume to double next year and the number of users to double overthe next two years. Vertica provides MoMo with the flexibility to run its analytical workloads in the cloud, on-prem, as well as in hybrid environments, providing them with deployment flexibility regardless of where their future needs take them. Another factor when choosing Vertica was that the unified analytics platform combines the strengths of the data warehouse and the data lake ecosystem – all in one — ensuring high performance, scalable analytics, and machine learning, delivered at an overall lower total cost of ownership.
A last word
Hybrid cloud is well-suited to the dynamic demands of modern analytics and machine learning workloads. Increasingly, businesses are finding that one essential element of a hybrid environment for these workloads is a modern database.
The Vertica Unified Analytics Platform is just such a database. It is based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and end-to-end in-database machine learning.
As such, Vertica enables many businesses to easily apply these powerful functions to the largest and most demanding analytical workloads, arming businesses and their customers with predictive business insights faster than other analytical databases or data warehouses in the market.
Critical to being part of a hybrid environment, Vertica provides its Unified Analytics Platform as SaaS on AWS, across all major public clouds, and on-premises data centers as a BYOL (bring your own license) model.
Learn more: https://www.vertica.com/what-is/hybrid-cloud/