Since the time Steve Ballmer went ballistic professing his love for developers, it has been a virtual mantra in the technology industry that developer adoption is key to the success of a given platform. On the face of it – Platform as a Service(PaaS) is a boon to enterprise developers who are tired of the inefficiencies of old school application development environments & stacks. Further, a couple of years ago, PaaS seemed to be the flavor of the future given the focus on Cloud Computing. This blogpost focuses on the advantages of the generic PaaS approach while discussing its lagging slow rate of adoption in the cloud computing market – as compared with it’s cloud cousins – IaaS (Infrastructure as a Service) and SaaS (Software as a Service).
Platform as a Service (PaaS) as the foundation for developing Digital, Cloud Native Applications…
Call them Digital or Cloud Native or Modern. The nature of applications in the industry is slowly changing. So are the cultural underpinnings of the development process and culture themselves- from waterfall to agile to DevOps. At the same time, Cloud Computing and Big Data are enabling the creation of smart data applications. Leading business organizations are cognizant of the need to attract and retain the best possible talent – often competing with the FANGs (Facebook, Amazon, Netflix & Google).
Couple all this with the immense industry and venture capital interest around container oriented & cloud native technologies like Docker – you have a vendor arms race in the making. And the prize is to be chosen as the standard for building industry applications.
Thus, infrastructure is enabling but in the end- it is the applications that are Queen or King.
That is where PaaS comes in.
Enter Platform as a Service (PaaS)…
Platform as a Service (PaaS) is one of the three main cloud delivery models, the other two being IaaS (Infrastructure such as compute, network & storage services) and SaaS (Business applications delivered over a cloud). A collection of different cloud technologies, PaaS focuses exclusively on application development & delivery. PaaS advocates a new kind of development based on native support for concepts like agile development, unit testing, continuous integration, automatic scaling, while providing a range of middleware capabilities. Applications developed on these can be deployed out as services & managed across thousands of application instances.
In short, PaaS is the ideal platform for creating & hosting digital applications. What can PaaS provide that older application development toolchains and paradigms cannot?
While the overall design approach and features vary across every PaaS vendor – there are five generic advantages from a high level –
- PaaS enables a range of Application, Data & Middleware components to be delivered as API based services to developers on any given Infrastructure as a Service (IaaS). These capabilities include- Messaging as a service, Database as a service, Mobile capabilities as a service, Integration as a service, Workflow as a service, Analytics as a service for data driven applications etc. Some PaaS vendors also provide ability to automate & manage APIs for business applications deployment on them – API Management.
- PaaS provides easy & agile access to the entire suite of technologies used while creating complex business applications. These range from programming languages to application server (and lightweight) runtimes to programming languages to CI/CD toolchains to source control repositories.
- PaaS provides the services which enables a seamless & highly automated manner of building the complete life cycle of building and delivering web applications and services on the internet. Industry players are infusing software delivery processes with practices such as continuous delivery (CD) and continuous integration (CI). For large scale applications such as those built in web scale shops, financial services, manufacturing, telecom etc – PaaS abstracts away the complexities of building, deploying & orchestrating infrastructure thus enabling instantaneous developer productivity. This is a key point – with it’s focus on automation – PaaS can save application and system administrators precious time and resources in managing the lifecycle of elastic applications
- PaaS enables your application to be ‘kind of cloud’ agnostic & can enable applications to be run on any cloud platform whether public or private. This means that a PaaS application developed on Amazon AWS can easily be ported to Microsoft Azure to VMWare vSphere to Red Hat RHEV etc
- PaaS can help smoothen organizational Culture and Barriers – The adoption of a PaaS forces an agile culture in your organization – one that pushes cross pollination among different business, dev and ops teams. Most organizations are just now beginning to go bimodal for greenfield applications can benefit immensely from choosing a PaaS as a platform standard.
The Barriers to PaaS Adoption Will Continue to Fall In 2017..
In general, PaaS market growth rates do not seem to line up well when compared with the other broad sections of the cloud computing space, namely IaaS (Infrastructure as a Service) and SaaS (Software as a Service). 451 Research’s Market Monitor forecasts that the total market for cloud computing (including PaaS, IaaS and infrastructure software as a service (ITSM, backup, archiving) – will hit $21.9B in 2016 more than doubling to $44.2bB by 2020. Of that, some analyst estimates contend that PaaS will be a relatively small $8.1 billion.
(Source – 451 Research)
The advantages that PaaS confers have sadly also caused its relatively low rate of adoption as compared to IaaS and SaaS.
The reasons for this anemic rate of adoption include, in my opinion –
- Poor Conception of the Business Value of PaaS – This is the biggest factor holding back explosive growth in this category. PaaS is a tremendously complicated technology & vendors have not helped by stressing on the complex technology underpinnings (containers, supported programming languages, developer workflow, orchestration, scheduling etc etc) as opposed to helping clients understand the tangible business drivers & value that enterprise CIOs can derive from this technology. Common drivers include increased time to market for digital capabilities, man hours saved in maintaining complex applications, ability to attract new talent etc. These factors will vary for every customer but it is up to frontline Sales teams to help deliver this message in a manner that is appropriate to the client.
- Yes, you can do DevOps without PaaS but PaaS helps a long way – Many Fortune 500 organizations are drawing up DevOps strategies which do not include a PaaS & are based on a simplified CI/CD pipeline. This is to the detriment of both the customer organization & the industry as PaaS can vastly simplify a range of complex runtime & lifecycle services that would otherwise need to be cobbled together by the customer as the application moves from development to production. There is simply a lack of knowledge in the customer community about where a PaaS fits in a development & deployment toolchain.
- Smorgasbord of Complex Infrastructure Choices – The average leading PaaS includes a range of open source technologies ranging from containers to runtimes to datacenter orchestration to scheduling to cluster management tools. This makes it very complex from the perspective of Corporate IT – not just it terms of running POCs and initial deployments but also to manage a highly complex stack. It is incumbent on the open source projects to abstract away the complex inner workings to drive adoption -whether by design or by technology alliances.
- You don’t need Cloud for PaaS but not enough Technology Leaders get that – This one is perception. The presence of an infrastructural cloud computing strategy is not a necessary condition for PaaS.
- The false notion that PaaS is only fit for massively scalable, greenfield applications – Industry leading PaaS’s (like Red Hat’s OpenShift) support a range of technology approaches that can help cut technical debt. They donot limit deployment on an application server platform such as JBOSS EAP or WebSphere or WebLogic, or a lightweight framework like Spring.
- PaaS will help increase automation thus cutting costs – For developers of applications in Greenfield/ New Age spheres such as IoT, PaaS can enable the creation of thousands of instances in a “Serverless” fashion. PaaS based applications can be composed of microservices which are essentially self maintaining – i.e self healing and self scalable up or down; these microservices are delivered (typically) by IT as Docker containers using automated toolchains. The biggest requirement in large datacenters – human involvement – is drastically reduced if PaaS is used – while increasing agility, business responsiveness and efficiencies.
My goal for this post was to share a few of my thoughts on the benefits of adopting a game changing technology. Done right, PaaS can provide a tremendous boost to building digital applications thus boosting the bottom line. Beginning 2017, we will witness PaaS satisfying critical industry use cases as leading organizations build end-to-end business solutions that covers many architectural layers.
Illustration: Business- IT Relationship (Image src – Pat.it)
Previous posts in this blog have discussed the fact that technological capabilities now make or break business models. It is critical for IT to operate in a manner that maximizes their efficiency while managing costs & ultimately delivering the right outcomes for the organization.
It is clear and apparent to me that the relationship lines of business (LOBs) have with their IT teams – typically central & shared – is completely broken at a majority of large organizations. Each side cannot seem to view either the perspective or the passions of the other. This dangerous dysfunction usually leads to multiple complaints from the business. Examples of which include –
- IT is perceived to be glacially slow in providing infrastructure needed to launch new business initiatives or to amend existing ones. This leads to the phenomenon of ‘Shadow IT’ where business applications are run on public clouds bypassing internal IT
- Something seems to be lost in translation while conveying requirements to different teams within IT
- IT is too focused on technological capabilities – Virtualization, Middleware, Cloud, Containers, Hadoop et al without much emphasis on business value drivers
So what are the top asks that Business has for their IT groups? I wager that there are five important focus areas –
- Transact in the language of the business –Most would agree that there has been too much of a focus on the technology itself – how it works, what the infrastructure requirements are to host applications – cloud or on-prem, data engines to ingest and process it etc etc . The focus needs to be on customer needs that drive business value for an organization’s customers, partners, regulators & employees. Technology at it’s core is just an engine and does not exist in a vacuum. The most vibrant enterprises understand this ground reality and always ensure that business needs drive IT and not the other way around. It is thus highly important for IT leadership to understand the nuances of the business to ensure that their roadmaps (long and medium term) are being driven with business & competitive outcomes in mind. Examples of such goals are a common organization wide taxonomy across products, customers, logistics, supply chains & business domains. The shared emphasis on both business & IT should be on goals like increased profitability per customer, enhanced segmentation of both micro and macro customer populations with the ultimate goal of increasing customer lifetime value (CLV).
- Bi-Modal or “2 Speed” IT et al need to be business approach centric – Digital business models that are driving agile web-scale companies offer enhanced customer experiences built on product innovation and data driven business models. They are also encroaching into the domain of established industry players in verticals like financial services, retail, entertainment, telecommunications, transportation and insurance by offering contextual & trendy products tailored to individual client profiles. Their savvy use of segmentation data and realtime predictive analytics enables the delivery of bundles of tailored products across multiple delivery channels (web, mobile, point of sale, Internet, etc.). The enterprise approach has been to adopt a model known as Bi-Modal IT championed by Gartner. This model envisages two different IT camps – one focused on traditional applications and the other focused on innovation. Whatever be the moniker for this approach – LOBs need to be involved as stakeholders from the get-go & throughout the process of selecting technology choices that have downstream business ramifications. One of the approaches that is working well is increased cross pollination across both teams, collapsing artificial organizational barriers by adopting DevOps & ensuring that business has a slim IT component to rapidly be able to fill in gaps in IT’s business knowledge or capability.
- Self Service Across the board of IT Capabilities – Shadow IT (where business goes around the IT team) is not just an issue with infrastructure software but is slowly creeping up to business intelligence and advanced analytics apps. The delays associated with provisioning legacy data silos combined with using tools that are neither intuitive nor able to scale to deal with the increasing data deluge are making timely business analysis almost impossible to perform. Insights delivered too late are not very valuable. Thus, LOBs are beginning to move to a predominantly online SaaS (Software As A Service) model across a range of business intelligence applications. Reports, visual views of internal & external datasets are directly served to internal consumers based on data uploaded into a cloud based BI provider. These reports and views are then directly delivered to end users. IT needs to enable this capability and make it part of their range of offerings to the business.
- Help the Business think Analytically – Business Process Automation (BPM) and Data Driven decision making are proven approaches used at data-driven organizations. When combined with Data and Business Analytics, this tends to be a killer combination. Organizations that are data & data metric driven are able to define key business processes that provide native support for key performance indicators (KPIs) that are critical and basic to their functioning. Applications developed by IT need to be designed in such a way that these KPIs can be communicate and broadcast across the organization constantly. Indeed a high percentage of organizations now have senior executive in place as the champion for BPM, Business Rules and Big Data driven analytics. These applications are also mobile native so that they can be provided access through a variety of mobile platforms for field based employees & back into the corporate firewall.
- No “Us vs Them” mentality – it is all “Us” – None of the above are only possible if the entire organization operates on an agile basis in order to collaborate across the value chain. Cross functional teams across new product development, customer acquisition & retention, IT Ops, legal & compliance must collaborate in short work cycles to close the traditional business & IT innovation gap. One of chief goals of agile methodologies is to close the long-standing gap between the engineers who develop and test IT capability and business requirements for such capabilities. Using traditional app dev methodologies, it can take months to design, test and deploy software – which is simply unsustainable.
Business & IT need to collaborate. Period. –
The most vibrant enterprises that have implemented web-scale practices not only offer “IT/Business As A Service” but also have instituted strong cultures of symbiotic relationships between customers (both current & prospective), employees , partners and developers etc.
No business today has much time to innovation—especially in the age of IT consumerization where end users accustomed to smart phone apps that are often updated daily. The focus now is on rapidly developing business applications to stay ahead of competitors that can better harness technology’s amazing business capabilities.
The ongoing digital transformation in key verticals like financial services, manufacturing, healthcare and telco has incumbent enterprises fending off a host of new market entrants. Enterprise IT’s best answer is to increase the pace of innovation as a way of driving increased differentiation in business processes. Though data analytics & automation remain the lynchpin of this approach – software defined infrastructure (SDI) built on the notions of cloud computing has emerged as the main infrastructure differentiator & that for a host of reasons which we will discuss in this two part blog.
Software Defined Infrastructure (SDI) is essentially an idea that brings together advances in a host of complementary areas spanning both infrastructure software, data as well as development environments. It supports a new way of building business applications. The core idea in SDI is that massively scalable applications (in support of diverse customer needs) describe their behavior characteristics (via configuration & APIs) to underlying datacenter infrastructure which simply obeys those commands in an automated fashion while abstracting away the underlying complexities.
SDI as an architectural pattern was originally made popular by the web scale giants – the so-called FANG companies of tech — Facebook , Amazon , Netflix and Alphabet (the erstwhile Google) but has begun making it’s way into the enterprise world gradually.
- Cost of hardware infrastructure is typically growing at a high percentage every year as compared to growth in the total IT budget. Cost pressures are driving an overall re look at the different tiers across the IT landscape.
- Infrastructure is not completely under the control of the IT-Application development teams as yet. Business realities that dictate rapid app development to meet changing business requirements
- Even for small, departmental level applications, still needed to deploy expensive proprietary stacks which are not only cost and deployment footprint prohibitive but also take weeks to spin up in terms of provisioning cycles.
- Big box proprietary solutions leading to a hard look at Open Source technologies which are lean and easy to use with lightweight deployment footprint.Apps need to dictate footprint; not vendor provided containers.
- Concerns with acquiring developers who are tooled on cutting edge development frameworks & methodologies. You have zero developer mindshare with Big Box technologies.
Key characteristics of an SDI –
- Applications built on a SDI can detect business events in realtime and respond dynamically by allocating additional resources in three key areas – compute, storage & network – based on the type of workloads being run.
- Using an SDI, application developers can seamlessly deploy apps while accessing higher level programming abstractions that allow for the rapid creation of business services (web, application, messaging, SOA/ Microservices tiers), user interfaces and a whole host of application elements.
- From a management standpoint, business application workloads are dynamically and automatically assigned to the available infrastructure (spanning public & private cloud resources) on the basis of the application requirements, required SLA in a way that provides continuous optimization across the life cycle of technology.
- The SDI itself optimizes the entire application deployment by both externally provisioned APIs & internal interfaces between the five essential pieces – Application, Compute, Storage, Network & Management.
The SDI automates the technology lifecycle –
Consider the typical tasks needed to create and deploy enterprise applications. This list includes but is not limited to –
- onboarding hardware infrastructure,
- setting up complicated network connectivity to firewalls, routers, switches etc,
- making the hardware stack available for consumption by applications,
- figure out storage requirements and provision those
- guarantee multi-tenancy
- application development
- updates, failover & rollbacks
- compliance checking etc.
Illustration: The different tiers of Software Defined Infrastructure
The core of the software defined approach are APIs. APIs control the lifecycle of resources (request, approval, provisioning,orchestration & billing) as well as the applications deployed on them. The SDI implies commodity hardware (x86) & a cloud based approach to architecting the datacenter.
The ten fundamental technology tenets of the SDI –
1. Highly elastic – scale up or scale down the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real time
2. Highly Automated – Given the scale & multi-tenancy requirements, automation at all levels of the stack (development, deployment, monitoring and maintenance)
3. Low Cost – Oddly enough, the SDI operates at a lower CapEx and OpEx compared to the traditional datacenter due to reliance on open source technology & high degree of automation. Further workload consolidation only helps increase hardware utilization.
4. Standardization – The SDI enforces standardization and homogenization of deployment runtimes, application stacks and development methodologies based on lines of business requirements. This solves a significant IT challenge that has hobbled innovation at large financial institutions.
5. Microservice based applications – Applications developed for a SDI enabled infrastructure are developed as small, nimble processes that communicate via APIs and over infrastructure like messaging & service mediation components (e.g Apache Kafka & Camel). This offers huge operational and development advantages over legacy applications. While one does not expect Core Banking applications to move over to a microservice model anytime soon, customer facing applications that need responsive digital UIs will need definitely consider such approaches.
6. ‘Kind-of-Cloud’ Agnostic – The SDI does not enforce the concept of private cloud, or rather it encompasses a range of deployment options – public, private and hybrid.
7. DevOps friendly – The SDI enforces not just standardization and homogenization of deployment runtimes, application stacks and development methodologies but also enables a culture of continuous collaboration among developers, operations teams and business stakeholders i.e cross departmental innovation. The SDI is a natural container for workloads that are experimental in nature and can be updated/rolled-back/rolled forward incrementally based on changing business requirements. The SDI enables rapid deployment capabilities across the stack leading to faster time to market of business capabilities.
8. Data, Data & Data – The heart of any successful technology implementation is Data. This includes customer data, transaction data, reference data, risk data, compliance data etc etc. The SDI provides a variety of tools that enable applications to process data in a batch, interactive, low latency manner depending on what the business requirements are.
9. Security – The SDI shall provide robust perimeter defense as well as application level security with a strong focus on a Defense In Depth strategy.
10. Governance – The SDI enforces strong governance requirements for capabilities ranging from ITSM requirements – workload orchestration, business policy enabled deployment, autosizing of workloads to change management, provisioning, billing, chargeback & application deployments.
“Dream no small dreams for they have no power to move the hearts of men.” — Goethe
- The Digital Mesh –
The rise of the machines has been well documented but enterprises are waking up to the possibilities only recently. Massive data volumes are now being reliably generated from diverse sources of telemetry as well as endpoints at corporate offices (as a consequence of BYOD). The former devices include sensors used in manufacturing, personal fitness devices like FitBit, Home and Office energy management sensors, Smart cars, Geo-location devices etc. Couple these with the ever growing social media feeds, web clicks, server logs and more – one sees a clear trend forming which Gartner terms the Digital Mesh. The Digital Mesh leads to an interconnected information deluge which encompasses classical IoT endpoints along with audio, video & social data streams. This leads to huge security challenges and opportunity from a business perspective for forward looking enterprises (including Governments). Applications will need to combine these into one holistic picture of an entity – whether individual or institution.
- Information of Everything –
The IoT era brings an explosion of data that flows across organizational, system and application boundaries. Look for advances in technology especially in Big Data and Visualization to help consumers harness this information in the right form enriched with the right contextual information.In the Information of Everything era, massive amounts of efforts will thus be expended on data ingestion, quality and governance challenges.
- Ambient User Experiences –
Mobile applications first begun forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example Banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk etc. The average enterprise user is familiar with BYOD in the age of self service. The Digital Mesh only exacerbates this gap in user experiences as information consumers navigate applications as they consume services across a mesh that is both multi-channel as well as provides Customer 360 across all these engagement points.Applications developed in 2016 and beyond must take an approach to ensuring a smooth experience across the spectrum of endpoints and the platforms that span them from a Data Visualization standpoint.
- Autonomous Agents and Things –
Smart machines like robots,personal assistants like Apple Siri,automated home equipment will rapidly evolve & become even more smarter as their algorithms get more capable and understanding of their own environments. In addition, Big Data & Cloud computing will continue to mature and offer day to day capabilities around systems that employ machine learning to make predictions & decisions. We will see increased application of Smart Agents in diverse fields like financial services,healthcare, telecom and media.
- Advanced Machine Learning –
Most business problems are data challenges and an approach centered around data analysis helps extract meaningful insights from data thus helping the business It is a common capability now for many enterprises to possess the capability to acquire, store and process large volumes of data using a low cost approach leveraging Big Data and Cloud Computing. At the same time the rapid maturation of scalable processing techniques allows us to extract richer insights from data. What we commonly refer to as Machine Learning – a combination of of econometrics, machine learning, statistics, visualization, and computer science – extract valuable business insights hiding in data and builds operational systems to deliver that value. Data Science has evolved to a new branch called “Deep Neural Nets” (DNN). DNN Are what makes possible the ability of smart machines and agents to learn from data flows and to make products that use them even more automated & powerful. Deep Machine Learning involves the art of discovering data insights in a human-like pattern. The web scale world (led by Google and Facebook) have been vocal about their use of Advanced Data Science techniques and the move of Data Science into Advanced Machine Learning.
- 3D Printing Materials –
3D printing continues to evolve and advance across a wide variety of industries.2015 saw a wider range of materials including carbon fiber, glass, nickel alloys, electronics & other materials used in the 3D printing process . More and more industries continue to incorporate the print and assembly of composite parts constructed using such materials – prominent examples including Tesla and SpaceX. We are at the beginning of a 20 year revolution which will lead to sea changes in industrial automation.
- Adaptive Security –
A cursory study of the top data breaches in 2015 reads like a “Who’s Who”of actors in society across Governments, Banks, Retail establishments etc. The enterprise world now understands that an comprehensive & strategic approach to Cybersecurity has now far progressed from being an IT challenge a few years ago to a business imperative. As Digital and IoT ecosystems evolve to loose federations of API accessible and cloud native applications, more and more assets are at danger of being targeted by extremely well funded and sophisticated adversaries. For instance – it is an obvious truth that data from millions of IoT endpoints requires data ingest & processing at scale. The challenge from a security perspective is multilayered and arises not just from malicious actors but also from a lack of a holistic approach that combines security with data governance, audit trails and quality attributes. Traditional solutions cannot handle this challenge which is exacerbated by the expectation that in an IoT & DM world, data flows will be multidirectional across a grid of application endpoints. Expect to find applications in 2016 and beyond incorporating Deep Learning and Real Time Analytics into their core security design with a view to analyzing large scale data at a very low latency.
- Advanced System Architecture –
The advent of the digital mesh and ecosystem technologies like autonomous agents (powered by Deep Neural Nets) will make increasing demands on computing architectures from a power consumption, system intelligence as well as a form factor perspective. The key is to provide increased performance while mimicking neuro biological architectures. The name given this style of building electronic circuits is neuromorphic computing. Systems designers will have increased choice in terms of using field programmable gate arrays (FPGAs) or graphics processing units (GPUs). While both FGPAs and GPUs have their pros and cons, devices & computing architectures using these as a foundation are both suited to deep learning and other pattern matching algorithms leveraged by advanced machine learning. Look for more reductions in form factors at less power consumption while allowing advanced intelligence in the IoT endpoint ecosystem.
- Mesh App and Service Architecture
The micro services architecture approach which combines the notion of autonomous, cooperative yet loosely coupled applications built as a conglomeration of business focused services is a natural fit for the Digital Mesh. The most important additive and consideration to micro services based architectures in the age of the Digital Mesh is what I’d like to term – Analytics Everywhere. Applications in 2016 and beyond will need to recognize that Analytics are pervasive, relentless, realtime and thus embedded into our daily lives. Every interaction a user has with a micro services based application will need a predictive capability built into the application architecture itself. Thus, 2016 will be the year when Big Data techniques are no longer be the preserve of classical Information Management teams but move to the umbrella Application development area which encompasses the DevOps and Continuous Integration & Delivery (CI-CD) spheres.
- IoT Architecture and Platforms
There is no doubt in anyone’s mind that IoT (Internet Of Things) is a technology megatrend that will reshape enterprises, government and citizens for years to come. IoT platforms will complement Mesh Apps and Service Architectures with a common set of platform capabilities built around open communication, security, scalability & performance requirements. These will form the basic components of IoT infrastructure including but not limited to machine to machine interfaces,location based technology, micro controllers , sensors, actuators and the communication protocols (based on an all IP standard).
The Final Word –
One feels strongly that Open Source will drive the various layers that make up the Digital Mesh stack (Big Data, Operating Systems, Middleware, Advanced Machine Learning & BPM). IoT will be a key part of Digital Transformation initiatives.
However, the challenge for developing Vertical capabilities on these IoT platforms is three fold. Specifically in areas of augmenting micro services based Digital Mesh applications- which are largely lacking at the time of writing:
- Data Ingest in batch or near realtime (NRT) or realtime from dynamically changing, disparate and physically distributed sensors, machines, geo location devices, clickstreams, files, and social feeds via highly secure lightweight agents
- Provide secure data transfer using point-to-point and bidirectional data flows in real time
- Curate these flows with Simple Event Processing (SEP) capabilities via tracing, parsing, filtering, joining, transforming, forking or cloning of data flows while adding business context to these flows. As mobile clients, IoT applications, social media feeds etc are being brought onboard into existing applications from an analytics perspective, traditional IT operations face pressures from both business and development teams to provide new and innovative services.
The creation of these smart services will further depend on the vertical industries that these products serve as well as requirements for the platforms that host them. E.g industrial automation, remote healthcare, public transportation, connected cars, home automation etc.
Finally, 2016 also throws up some interesting questions around Cyber Security, namely –
a. Can an efficient Cybersecurity be a lasting source of competitive advantage;
b. Given that most breaches are long running in nature where systems are slowly compromised over months. How does one leverage Big Data and Predictive Modeling to rewire and re-architect creaky defenses?
c. Most importantly, how can applications implement security in a manner that they constantly adapt and learn;
If there were just a couple of sentences to sum up Gartner’s forecast for 2016 in a succinct manner, it would be “The emergence of the Digital Mesh & the rapid maturation of IoT will serve to accelerate business transformation across industry verticals. The winning enterprises will begin to make smart technology investments in Big Data, DevOps & Cloud practices to harness these changes “.
Previous posts in this blog have commented on the financial services industry as increasingly undergoing a gradual makeover if not outright transformation – both from a business and IT perspective. This is being witnessed across the spectrum that makes up this crucial vertical – Retail & Consumer Banking, Stock Exchanges, Wealth Management/ Private Banking & Cards etc.
The regulatory deluge (Basel III, Dodd Frank, CAT Reporting, AML & KYC etc) and the increasing sophistication of cybersecurity threats have completely changed the landscape that IT finds itself in – compared to even five years ago.
Brett King writes in his inimitable style about the age of the hyper-connected consumer i.e younger segments of the population who expect to be able to bank from anywhere, be it from a mobile device or via the Internet from their personal computers instead of just walking into a physical branch.
Further multiple Fintechs (like WealthFront, Kabbage, Square, LendingClub, Mint.com, Cyptocurrency based startups etc) are leading the way in pioneering a better customer experience. For an established institution that has huge early mover advantage, the ability to compete with innovative players by using fresh technology approaches is critical to engage customers.
All of these imperatives place a lot of pressure on Enterprise FS IT to move from an antiquated command and control model to being able to deliver on demand services with the speed of an Amazon Web Services.
These new services are composed of Applications that encompass paradigms ranging from Smart Middleware, Big Data, Realtime Analytics, Data Science, DevOps and Mobility. The common business thread to deploying all of these applications is to be able to react quickly and expeditiously to customer expectations and requirements.
Enter the Software Defined Datacenter (SDDC). Various definitions exist for this term but I wager that it means – “a highly automated & self-healing datacenter infrastructure that can quickly deliver on demand services to millions of end users, internal developers without imposing significant headcount requirements on the enterprise“.
Let’s parse this below.
The SDDC encompasses SDC (Software Defined Compute) , SDS (Software Defined Storage), SDN (Software Defined Networking), Software Defined Applications and Cloud Management Platforms (CMP) into one logical construct as can be seen from the below picture.
The core of the software defined approach are APIs. APIs control the lifecycle of resources (request, approval, provisioning,orchestration & billing) as well as the applications deployed on them. The SDDC implies commodity hardware (x86) & a cloud based approach to architecting the datacenter.
The ten fundamental technology differentiators of the SDDC –
1. Highly elastic – scale up or scale down the gamut of infrastructure (compute – VM/Baremetal/Containers, storage – SAN/NAS/DAS, network – switches/routers/Firewalls etc) in near real time
2. Highly Automated – Given the scale & multi-tenancy requirements, automation at all levels of the stack (development, deployment, monitoring and maintenance)
3. Low Cost – Oddly enough, the SDDC operates at a lower CapEx and OpEx compared to the traditional datacenter due to reliance on open source technology & high degree of automation. Further workload consolidation only helps increase hardware utilization.
4. Standardization – The SDDC enforces standardization and homogenization of deployment runtimes, application stacks and development methodologies based on lines of business requirements. This solves a significant IT challenge that has hobbled innovation at large financial institutions.
5. Microservice based applications – Applications developed for a SDDC enabled infrastructure are developed as small, nimble processes that communicate via APIs and over infrastructure like service mediation components (e.g Apache Camel). This offers huge operational and development advantages over legacy applications. While one does not expect Core Banking applications to move over to a microservice model anytime soon, customer facing applications that need responsive digital UIs will need definitely consider such approaches.
6. ‘Kind-of-Cloud’ Agnostic – The SDDC does not enforce the concept of private cloud, or rather it encompasses a range of deployment options – public, private and hybrid.
7. DevOps friendly – The SDDC enforces not just standardization and homogenization of deployment runtimes, application stacks and development methodologies but also enables a culture of continuous collaboration among developers, operations teams and business stakeholders i.e cross departmental innovation. The SDDC is a natural container for workloads that are experimental in nature and can be updated/rolled-back/rolled forward incrementally based on changing business requirements. The SDDC enables rapid deployment capabilities across the stack leading to faster time to market of business capabilities.
8. Data, Data & Data – The heart of any successful technology implementation is Data. This includes customer data, transaction data, reference data, risk data, compliance data etc etc. The SDDC provides a variety of tools that enable applications to process data in a batch, interactive, low latency manner depending on what the business requirements are.
9. Security – The SDDC shall provide robust perimeter defense as well as application level security with a strong focus on a Defense In Depth strategy. Further data at rest and in motion shall be
10. Governance – The SDDC enforces strong governance requirements for capabilities ranging from ITSM requirements – workload orchestration, business policy enabled deployment, autosizing of workloads to change management, provisioning, billing, chargeback & application deployments.
So how is doing SDDC at the moment? Most major banks have initiatives in place to gradually evolve their infrastructures to an SDI paradigm. Bank of America (for one) have been vocal about their approach in using two stacks, one Open Source & OpenStack based and the other a proprietary stack.
To sum up the core benefit of the SDDC approach, it brings a large enterprise closer to web scale architectures and practices.
The business dividends of the latter include –
1. Digital Transformation – Every large Bank is under growing pressure to transform lines of business or their entire enterprise into a digital operation. I define digital in this context as being able to – “adapt high levels of automation while enabling the business to support multiple channels by which products and services can be delivered to customers. ”
Further the culture of digital encourages constant innovation and agility resulting high levels of customer & employee satisfaction.”
2. Smart Data & Analytics – Techniques that ensure that the right data is in the hands of the right employee at the right time so that contextual services can be offered in real time to customers. This has the effect of optimizing existing workflows while also enabling the creation of new business models.
3. Cost Savings – Oddly enough, the move to web-scale only reduces business and IT costs. You not only end up doing more with less employees due to higher levels of automation but also are able to constantly cut costs due to adopting technologies like Cloud Computing which enable one to cut CapEx and OpEx. Almost all webscale IT is dominated by open source technologies & APIs, which are much more cost effective than proprietaty platforms.
4. A Culture of Collaboration – The most vibrant enterprises that have implemented web-scale practices not only offer “IT/Business As A Service” but also have instituted strong cultures of symbiotic relationships between customers (both current & prospective), employees , partners and developers etc.
5. Building for the Future – The core idea behind implementing web-scale architecture and data management practices is “Be disruptive in your business or be disrupted by competition”. Web-scale practices enable the building of business platforms around which ecosystems can be created and then sustained based on increasing revenue.
To quote wikipedia, a widespread transition to the SDDC will take years:
Enterprise IT will have to become truly business focused, automatically placing application workloads where they can be best processed. We anticipate that it will take about a decade until the SDDC becomes a reality. However, each step of the journey will lead to efficiency gains and make the IT organization more and more service oriented.
The virtuous loop encouraged by constant customer data & feedback enables business applications (and platforms) to behave like agile & growing organisms – SDDC based architectures offer them the agility to get there.