Other members of the Theta Enterprise Validator Node Program include Binance, Gumi Cryptos, and Blockchain Ventures.

The popular blockchain video delivery network, Theta Labs, proudly announced that, from today, Google Cloud is involved in its Enterprise Validator Node Program, which also involves Binance, Gumi Cryptos, and Blockchain Ventures. Through the program, enterprises can verify transactions in consonance with the consensus protocol of Theta.

Another big change for Theta is that Google Cloud will also become the preferred cloud provider. This means that clients will be able to deploy and run Theta nodes straight from Google Cloud Marketplace in just a couple of clicks, from all parts of the world. The launch of the Google Cloud Marketplace solution coincided with the launch of Theta Mainnet 2.0.

“Distributed ledger technology enables new business models that potentially transform the global digital economy, including the media & entertainment industry,” commented Google Cloud developer advocate, Allen Day. As for the current announcement, he noted:

“We’re impressed by Theta’s achievements in blockchain video and data delivery. We look forward to participating as an enterprise validator, and to providing Google Cloud infrastructure in support of Theta’s long-term mission and future growth.”

Theta is achieving real progress towards full decentralization of its security and governance in a timely manner. Google will run a validator node and provide security, stability, and reliability coming from the Google Cloud Platform. Theta and its media and entertainment, technology, gaming, and telecom partners add a high-performance decentralized micropayment network to the equation. The platform is easily able to scale to millions of simultaneous video spectators.

Google Cloud Marketplace will also provide users with access to Theta Mainnet 2.0 and the Theta Guardian network. For those not familiar with the platform, the latter is Theta’s one-of-a-kind decentralized layer of security and consensus run by members of the community. In addition, Theta.tv will also benefit from the partnership. Google Cloud will power the cloud infrastructure of the progressing first-party esports streaming site based on the Theta Network.

Mitch Liu, co-founder and CEO of Theta Labs, commented:

“We’re thrilled to deepen our partnership with Google Cloud across a number of key strategic areas to accelerate Theta adoption across industries. We welcome Google Cloud as an enterprise validator along with our other global partners to further strengthen the security and decentralization of our protocol. As we continue to build our network and streaming business, Google Cloud is the perfect partner to help us scale globally, with extensive geographical coverage offering ease-of-use, networking advantages, and platform performance.”

Every Company Is Now a Software Company

In today’s digital economy, the ability to build and deliver software quickly is no longer exclusive to technology firms. Organisations across every sector — retail, financial services, manufacturing, government — compete on software. Those that can transform rapidly and seize emerging opportunities ahead of rivals hold a decisive advantage.

This shift has driven demand for application development to record levels, according to the State of Application Development 2018 study published by OutSystems. Yet most organisations struggle to match that demand with their existing development capacity and tooling.

The same report found a growing emphasis on building customer-facing and partner-facing applications over internal tooling — a signal that software is now directly tied to business revenue, brand reputation, and competitive standing.

Why Speed to Market Is the Core Challenge

Getting to market first requires both technical velocity and organisational courage. Rapid experimentation — releasing minimal viable products, testing assumptions with real users, and iterating quickly — demands a development approach that is far faster than traditional hand-coded workflows.

Low-code platforms have emerged as a practical answer to this pressure. By replacing large amounts of manually written code with visual modelling, pre-built components, and automated scaffolding, they compress development cycles significantly and open contribution to a broader pool of developers.

Four Priorities Low-Code Platforms Support in Digital Transformation

According to OutSystems, the right low-code platform should move the needle on four interconnected priorities that IT organisations typically focus on during digital transformation programmes:

1. Enable Greater Innovation

Low-code development can be substantially faster than hand-coding for many application types. Visual prototyping supports design-thinking workshops, while fast creation of minimal viable products (MVPs) and iterative “test and learn” cycles bring developers and end users into close, productive collaboration earlier in the process.

2. Improve Agile Adoption

Siloed front-end, back-end, and mobile specialisms can slow agile teams down. Low-code platforms can reduce this fragmentation by giving developers a single integrated development environment (IDE) and a unified skill set, effectively turning specialised developers into full-stack contributors and making cross-functional agile squads more practical.

3. Architect Flexible Integration

Legacy systems rarely disappear overnight. A capable low-code platform should support REST and SOAP APIs, allow visual modelling of back-end services, and encourage a layered service architecture with composable applications, decoupled front-ends, and loosely coupled services. This approach preserves existing investments in slow-evolving core systems while enabling new digital experiences to be built on top of them.

4. Achieve DevOps Success

Low-code and DevOps are complementary, not competing, approaches. A well-designed low-code platform should provide comprehensive version control, support collaborative development across multi-developer and multi-team organisations, and offer proactive application performance monitoring — all of which underpin reliable, high-frequency software delivery.

Five Practical Ways to Accelerate Your Digital Transformation

Miguel Baltazar, Director of Transformation Services at OutSystems, identified five structured approaches that organisations can use to build momentum in their digital transformation programmes:

1. Design Customer Journey Maps

Customer journey maps are visual representations of how a customer interacts with your organisation across time and channels — from initial awareness through purchase, support, and ongoing engagement. Mapping these journeys helps teams empathise with customer motivations and pain points, set project goals grounded in real user needs, and define the right scope before a single line of code is written.

2. Create Clickable Prototypes

Interactive prototypes — even simple ones — give stakeholders a tangible sense of how an application will look and behave. They accelerate feedback loops, surface misaligned expectations early, and reduce the risk of building the wrong thing. Prototypes can be tested with real users, iterated rapidly, and validated against project goals before significant engineering investment is committed.

3. Build Multi-Disciplinary Teams

Effective digital delivery typically draws on six areas of expertise: business analysis, demand management and governance, software development, solution architecture, user experience design, and operations. Keeping individual teams small improves communication, preserves autonomy, and encourages innovation. Cross-functional squads that span these disciplines reduce handoff delays and build shared ownership of outcomes.

4. Organise a Hackathon

Hackathons — intensive collaborative sessions ranging from a few hours to several days — act as focused innovation labs. They work across industries: financial services, retail, government, and e-commerce organisations have all used them to rapidly prototype new ideas, surface hidden talent, and build internal momentum for transformation. OutSystems has published a hackathon planning guide that covers how to structure and run one effectively.

5. Adopt a Structured Digital Transformation Playbook

Ad hoc transformation efforts often stall without a repeatable methodology. A structured playbook — covering how to initiate, govern, and scale digital projects — gives teams a shared language and a step-by-step framework. The OutSystems Digital Transformation Playbook introduces what they term the “low-code digital factory” model, designed to guide organisations from initial projects through to scaled, continuous delivery.

Start Small, Then Scale

Organisations that succeed with low-code-driven transformation tend to follow a common pattern: start with a contained, high-visibility project; deliver a working application faster than stakeholders expect; then use that proof point to secure support for larger initiatives.

Logitech’s experience illustrates this well. Steven Schmidt, Enterprise Collaboration Manager at Logitech, used low-code development to build and iteratively modify a mission-critical product launch application. The speed of delivery and ease of modification were, in his words, what allowed the team to simply “do more.”

Starting small reduces political risk, builds internal confidence, and establishes repeatable delivery patterns that larger programmes can inherit. A project that goes live in a few weeks — rather than months — demonstrates the feasibility of a new approach far more effectively than any strategy document.

Key Takeaways

• Digital transformation is fundamentally a software delivery challenge: organisations that ship relevant applications faster win.
• Low-code platforms can significantly reduce development cycle times and broaden who can contribute to application delivery.
• Four platform capabilities matter most: enabling innovation, improving agile adoption, supporting flexible integration, and enabling DevOps practices.
• Structured approaches — journey mapping, prototyping, multi-disciplinary teams, hackathons, and a delivery playbook — provide repeatable frameworks for accelerating transformation.
• Starting small and delivering quickly builds the organisational trust and momentum needed to scale transformation programmes.

Cloud Moves from Buzzword to Business Foundation

By 2017, cloud computing had long outgrown its status as an enterprise-only talking point. Streaming media, collaborative tools, consumer storage services, and SaaS productivity suites had embedded cloud infrastructure into daily life. For enterprises, the shift was equally profound: cloud was no longer a risk to be managed but a competitive lever to be pulled. The question had moved from whether to adopt cloud to how fast and how deeply.

Six distinct trends were driving IT strategy conversations in 2017, each with meaningful implications for DevOps teams, architects, and technology leaders.

1. Cloud 2.0: From Storage to Intelligence

The first wave of enterprise cloud adoption focused heavily on lift-and-shift migration — moving existing workloads from on-premises data centers into cloud infrastructure to reduce capital expenditure. By 2017, that phase was maturing. Analysts at IDC described the market as moving beyond experimentation toward mass enterprise adoption, with the focus shifting from storing data in the cloud to extracting value from it.

This second era — informally called Cloud 2.0 — is characterized by machine learning pipelines, real-time analytics, anomaly detection, and AI-assisted decision-making built directly on top of cloud-resident data. Rather than treating the cloud as a cheaper data center, organizations began treating it as an intelligent platform. Google’s senior cloud leadership publicly framed the challenge for enterprise customers as: now that you are in the cloud, how do you operate at a fundamentally different level?

For DevOps teams, Cloud 2.0 meant building and operating data pipelines, integrating managed ML services, and instrumenting applications to feed analytics platforms — not just deploying and monitoring web servers.

2. Cloud Containers: Portable, Consistent, Scalable

Container adoption accelerated sharply in 2017, driven primarily by Docker’s toolchain maturity and the growing ecosystem around container orchestration — most notably Kubernetes, which was gaining significant enterprise traction. Containers offered a concrete solution to a persistent problem: environment inconsistency between development, staging, and production.

Key reasons for container adoption in this period included:

• Portability: A containerized application runs consistently across cloud providers and on-premises environments, reducing vendor lock-in risk.
• Density and cost efficiency: Containers share a host OS kernel, enabling far higher workload density per host compared to virtual machines.
• Speed: Container startup times are measured in milliseconds rather than the seconds or minutes required to boot a VM.
• Ecosystem: Public container registries and pre-built images accelerated application delivery.

Enterprise adoption was still in its early stages in 2017, but the trajectory was clearly toward containers becoming a default deployment unit for new cloud-native workloads.

3. Microservices: Decomposing the Monolith

Microservices architecture gained momentum alongside containers because the two technologies are complementary. A microservices approach decomposes a large application into small, independently deployable services, each responsible for a narrow business capability and communicating over well-defined APIs.

The cloud migration context made microservices particularly relevant in 2017. Organizations migrating legacy monolithic applications to the cloud faced a choice: lift-and-shift the monolith (fast but limited in benefit) or refactor toward microservices (slower but unlocking cloud-native scaling and resilience). A practical middle path involved wrapping specific capabilities of existing applications as microservices, incrementally strangling the monolith rather than rewriting it entirely.

When paired with containers and an orchestrator like Kubernetes, microservices deliver:

• Independent scaling of individual service components based on actual load
• Fault isolation so that a failure in one service does not cascade across the application
• Team autonomy, enabling parallel development and deployment without coordination overhead
• Technology heterogeneity, allowing each service to use the language or runtime best suited to its function

4. Cloud APIs: The Integration Fabric of the Digital Economy

APIs had become the connective tissue of modern cloud architectures by 2017. Cloud providers exposed virtually every platform capability through APIs, and SaaS vendors built their entire business models on API-first design. This created an ecosystem where composing new digital services from existing API-connected components was dramatically faster than building from scratch.

Several forces drove API growth in this period:

• Automation demand: Workflow automation platforms (popularized by tools like IFTTT — If This Then That) depended entirely on cloud APIs to connect disparate services.
• Digital transformation initiatives: Enterprises modernizing customer-facing channels needed APIs to surface back-end data and processes to web and mobile frontends.
• Partner ecosystems: Businesses used APIs to allow third-party developers to build on their platforms, extending reach without proportional internal engineering effort.

API management tooling — including gateways, developer portals, rate limiting, and versioning strategies — became a meaningful DevOps concern as API sprawl grew.

5. Serverless Computing: Abstracting Infrastructure Further

Serverless — or Function-as-a-Service (FaaS) — represented a further step in the abstraction of infrastructure concerns away from application developers. Platforms like AWS Lambda (launched 2014, but gaining enterprise traction in 2016–2017), Google Cloud Functions, and Azure Functions allowed developers to deploy discrete functions triggered by events, with the cloud provider handling all server provisioning, scaling, and availability.

For DevOps teams, serverless had a nuanced impact:

• Reduced operational surface area: No servers to patch, no OS to manage, no capacity to provision. Scaling is automatic and granular.
• Cost model shift: Billing is per invocation and execution duration rather than reserved capacity, which can significantly reduce cost for bursty or infrequent workloads.
• New operational challenges: Distributed tracing, cold start latency, function timeout limits, and vendor lock-in became active concerns that required new tooling and practices.
• NoOps narrative: Some advocates positioned serverless as enabling "NoOps" — eliminating operational overhead — though experienced practitioners recognized that operations responsibilities shift rather than disappear.

Serverless was well-suited to event-driven workloads: image processing, webhook handlers, scheduled data transformations, and API backends with variable traffic.

6. Cloud Security: A Shared Responsibility That Demands Active Engagement

Security remained the most cited barrier to cloud adoption in 2017, even as adoption accelerated. A common misconception was that moving to a major cloud provider transferred security responsibility to the provider. In practice, cloud providers operate on a shared responsibility model: the provider secures the underlying infrastructure, while the customer remains responsible for securing their data, identities, access controls, and application configurations.

Areas where cloud-native security capabilities were maturing in 2017 included:

• Identity and access management (IAM): Granular, policy-based access controls for cloud resources
• Encryption at rest and in transit: Managed key services and enforced TLS across platform services
• Security monitoring and logging: Centralized audit logs (e.g., AWS CloudTrail) enabling forensic investigation and compliance reporting
• Compliance certifications: Major providers held SOC 2, ISO 27001, PCI DSS, and HIPAA certifications, easing regulated-industry adoption

For DevOps teams, the practical implication was that security had to be embedded in the delivery pipeline — scanning container images, validating infrastructure-as-code configurations, and enforcing least-privilege IAM policies — rather than treated as a post-deployment concern.

Business Impact Across These Trends

These six trends were not independent. They converged to affect organizations across three dimensions:

Enhanced Customer Experience

Cloud 2.0 analytics and AI capabilities allowed organizations to process behavioral data at scale and deliver personalized, context-aware customer interactions. Real-time recommendation engines, adaptive pricing, and proactive support — all dependent on cloud-scale data processing — became differentiators rather than aspirations.

Reduced Total Cost of Ownership (TCO)

Moving from capital expenditure on physical hardware to operational expenditure on cloud services changes the cost structure fundamentally. Serverless and containers amplified this by improving utilization efficiency. Organizations that managed cloud spend actively — using reserved instances, right-sizing workloads, and eliminating idle resources — realized meaningful TCO reductions compared to equivalent on-premises infrastructure.

Improved Agility and Business Intelligence

Microservices and API-driven architectures allowed engineering teams to deploy changes independently and frequently, shortening the cycle from business idea to production feature. Cloud-based data warehouses and analytics platforms turned raw operational data into actionable intelligence, supporting faster and better-informed decision-making.

Strategic Considerations for Cloud Adoption

The trends above share a common implication: cloud adoption is not a one-time migration project but a continuous capability-building exercise. Organizations that treated cloud as a destination rather than a platform tended to underinvest in the skills, tooling, and governance frameworks needed to sustain and evolve their cloud posture.

Practical steps that aligned with leading practice in 2017 — and remain relevant today — include:

• Establishing a cloud center of excellence or governance function to maintain standards, evaluate new services, and share knowledge across teams
• Investing in workforce training so that engineering and operations staff can effectively use cloud-native services rather than replicating on-premises patterns in the cloud
• Adopting a multi-cloud or hybrid cloud strategy deliberately, with clear criteria for which workloads run where, rather than accumulating providers reactively
• Embedding security and cost management into CI/CD pipelines from the start rather than retrofitting controls later

Looking Beyond 2017

The trends identified in 2017 did not simply fade — they intensified. Kubernetes became the de facto container orchestration standard. Serverless platforms matured significantly. Multi-cloud and hybrid cloud architectures became the norm for large enterprises. Cloud-native security tooling evolved into a category of its own. And the data intelligence vision of Cloud 2.0 was realized through managed AI and ML platforms offered by every major provider.

For DevOps practitioners, understanding where these trends started provides useful context for the architectural decisions and platform capabilities that define cloud-native engineering today.