Originally published in 2019, this article reflects the cloud computing landscape as it stood at the start of that year. Many of these predictions proved accurate and laid the groundwork for trends that remain central to enterprise DevOps today. The original analysis draws on reporting from Analytics Insight.
By early 2019, cloud computing had moved well beyond its origins as a low-cost alternative to on-premises servers. It had become the default platform for enterprise digital transformation. The combination of low-cost infrastructure and high-value managed services was accelerating cloud adoption faster than most analysts had predicted even a few years earlier. Here are the ten trends that were expected to define cloud computing in 2019.
1. Enterprise Cloud Spending Would Surge
The expectation was that 2019 would see a significant jump in enterprise cloud budgets, driven by two converging needs: modernizing legacy applications and extracting value from years of accumulated data. Organizations were expected to invest heavily in SaaS platforms while simultaneously adopting cloud-native technologies — particularly Kubernetes, containers, and serverless functions — to support new architectures and faster release cycles.
The underlying logic was straightforward: once leadership recognized cloud as a competitive differentiator rather than just a cost-saving measure, budget constraints eased and adoption accelerated across the board.
2. Multi-Cloud and Hybrid-Cloud Orchestration Would Gain Momentum
By 2019, many enterprises had already committed to both on-premises infrastructure and at least one public cloud provider. The resulting hybrid and multi-cloud environments introduced serious operational complexity — managing costs, enforcing policies, and maintaining visibility across disparate platforms became full-time engineering challenges.
The prediction was that dedicated orchestration platforms would emerge to address this complexity. Rather than teams juggling separate consoles for AWS, Azure, GCP, and private data centers, unified orchestration layers would provide a single pane of glass for cost tracking, workload placement, and governance. This trend proved accurate and has since matured into a category of its own, with tools like Terraform, Crossplane, and vendor-native control planes filling this role.
3. Open-Source Would Drive Technology Democratisation
The democratisation of cloud infrastructure tools was already underway by 2019, led largely by the open-source ecosystem. Technologies that had previously required specialized vendors — hypervisors, cloud management platforms, container runtimes, service meshes — were becoming available as community-maintained open-source projects.
Developer communities were identified as the primary driver of this shift. As developers gained more influence over infrastructure decisions (a trend closely related to the rise of DevOps culture), open-source tools became the default starting point for new projects, reducing lock-in and lowering the cost of experimentation.
4. Specialized Cloud Service Models Would Proliferate
The cloud service model had already expanded well beyond IaaS, PaaS, and SaaS by 2019. The prediction was that increasingly specialized service categories would gain traction, including:
- iPaaS (Integration Platform as a Service) — for connecting disparate applications and data sources
- dPaaS (Data Platform as a Service) — managed data pipeline and analytics environments
- BaaS (Blockchain as a Service) — hosted distributed ledger capabilities
- FaaS (Function as a Service) — serverless compute, exemplified by AWS Lambda and Azure Functions
- mBaaS (Mobile Backend as a Service) — managed backends for mobile application developers
This fragmentation reflected a broader shift: cloud providers were no longer competing solely on raw infrastructure price, but on the depth and breadth of managed services they could offer to reduce developer toil.
5. Full-Stack Developers Would Need Data Engineering Skills
As organizations moved more of their workloads to the cloud and adopted data-driven decision making, the boundaries between application development and data engineering began to blur. The prediction was that full-stack engineers who understood distributed data processing frameworks — particularly Apache Spark — would become significantly more valuable.
This reflected a real skills gap: many development teams knew how to build APIs and front-end interfaces, but lacked the background to design efficient data pipelines or work with large-scale streaming and batch processing systems. Cloud platforms lowered the barrier to entry for these capabilities, but developers still needed a conceptual foundation to use them effectively.
6. Cloud-Based AI and Machine Learning Would Accelerate
The major public cloud providers had begun embedding AI and machine learning services into their platforms around 2017. By 2019, these offerings had matured into comprehensive ecosystems covering managed model training, inference APIs, AutoML tooling, and pre-built cognitive services for vision, speech, and natural language processing.
The expectation was that 2019 would see accelerated enterprise adoption, as organizations that had been evaluating or piloting AI capabilities would move toward production deployments. This prediction aligned with the broader digital transformation narrative: companies that delayed adoption risked falling behind competitors who were using AI to optimize operations, personalize customer experiences, and reduce costs.
7. Organizations Would Create Dedicated Digital Skills Roles
A recurring challenge for enterprises adopting cloud technology was the gap between available training programs and the practical skills their engineering teams actually needed. Generic certification courses often failed to align with a team’s specific technology stack or business context, leaving a disconnect between training investment and measurable outcomes.
The prediction was that organizations would respond by creating dedicated Digital Skills Officer roles — similar in concept to a Chief Learning Officer — positioned to work alongside CTOs and CIOs. These roles would be responsible for translating technical roadmaps into targeted learning programs, ensuring that training efforts produced engineers capable of executing on the organization’s specific cloud strategy rather than passing generic exams.
8. Apache Spark and Data Processing Expertise Would Become a Core Engineering Skill
Related to prediction five, this point emphasized the specific importance of Apache Spark as a framework that full-stack and generalist engineers would increasingly encounter. As cloud-based data platforms made it easier to spin up Spark clusters without managing underlying infrastructure, the expectation was that familiarity with Spark’s programming model — DataFrames, RDDs, streaming APIs — would become a standard part of the cloud engineer’s toolkit rather than a specialized data engineering niche.
This prediction reflected the growing importance of real-time and batch analytics as first-class features of cloud-native applications, rather than separate analytical workloads handled by a separate data team.
9. Container Adoption Would Accelerate, Especially in Microservices
Containers package an application with all of its dependencies — libraries, binaries, configuration — into a single portable unit that runs consistently across environments. By 2019, containers had moved from early-adopter experimentation to mainstream enterprise consideration, driven in large part by Docker’s popularity and the rapid maturation of Kubernetes as an orchestration standard.
The prediction was that 2019 would see continued acceleration, particularly in microservices architectures where containers provide an ideal deployment unit: lightweight, independently deployable, and portable across cloud providers and on-premises infrastructure. Managed Kubernetes services from AWS (EKS), Google (GKE), and Azure (AKS) were all either recently launched or expanding, lowering the operational barrier to container adoption significantly.
10. Cloud Would Extend and Enhance Legacy Applications
Early cloud narratives often assumed that organizations would wholesale migrate their legacy systems to the cloud. In practice, this rarely happened on the expected timeline. Most enterprises found that core legacy systems — billing platforms, ERP systems, mainframe workloads — were too deeply embedded in business processes to replace quickly, and the risk of disruption outweighed the benefits of a full migration.
The more realistic prediction for 2019 and beyond was a hybrid approach: legacy systems would remain in place, but cloud services would be used to augment them. Specific augmentation patterns included:
- Connecting legacy data stores to cloud-based analytics and machine learning pipelines
- Using edge computing for IoT workloads that legacy systems were not designed to handle
- Wrapping legacy functionality in modern APIs to enable new front-end experiences
- Offloading compute-intensive workloads to cloud functions while keeping core logic on-premises
This pattern — often called “cloud augmentation” or the “strangler fig” migration pattern — became one of the dominant enterprise cloud strategies precisely because it reduced risk while delivering incremental value.
Looking Back: How Accurate Were These Predictions?
In retrospect, most of these 2019 predictions proved directionally correct. Multi-cloud complexity became one of the defining operational challenges of the following years. Container and Kubernetes adoption continued at pace. AI and ML services expanded dramatically across all major cloud platforms. The skills gap in cloud and data engineering remains a persistent issue for engineering leaders today.
The prediction that aged least well — or at least evolved significantly — was the focus on Apache Spark as a specific skill for full-stack developers. While Spark remains important in data engineering, the broader ecosystem around managed data services (Databricks, BigQuery, Snowflake, dbt) shifted how practitioners interact with distributed data processing, often abstracting away the framework-level details that the 2019 prediction emphasized.
The core insight from this 2019 analysis remains valid: cloud is not simply an infrastructure cost play. It is the platform on which enterprise competitiveness is built, and the organizations that invest in skills, orchestration, and architecture — not just compute budgets — are the ones best positioned to benefit from it.