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Data / Kubernetes

More Database, Analytics Workloads Ran on Kubernetes in 2022

More than three in four participants in the new Data on Kubernetes survey now acknowledge the use of databases on Kubernetes, up from 50% in 2021.
Oct 21st, 2022 10:30am by
Featued image for: More Database, Analytics Workloads Ran on Kubernetes in 2022
Featured image by Maxim Berg via Unsplash.

The percentage of organizations running databases on Kubernetes leaped 26 percentage points in 2022 compared to last year, according to a new survey by the Data on Kubernetes (DoK) Community.

More than three-quarters (76%) of survey participants now acknowledge the use of databases on Kubernetes, up from 50% just last year. Analytics workloads have also jumped significantly, the report states, going from 39% to 67%.

Actually running stateful applications (those including data saved to persistent disk storage) is not relatively common in the abstract. A year ago, 55% of the Cloud Native Computing Foundation’s 2021 user survey were doing this. Yet, based on the DoK report, the mix of application types that use data on Kubernetes appears to be growing.

The new report surveyed more than 500 Kubernetes users that run data workloads on Kubernetes. Consistency and ease of management are the leading factors behind running data workloads on Kubernetes, which are both critical to ensuring that widespread, production use of containers can be handled.

Notably, among those using data on Kubernetes, there was no increase in utilizing persistent storage, and an actual decline in streaming or messaging workloads.

Bar chart, use of database, analytics and AI workloads on Kubernetes rose significantly in 2022

Data on Kubernetes Is a Day 2 Operations Issue

The DoK report’s other key findings included:

  • Most (72%) respondents started running data workloads on Kubernetes more than a year ago. With some experience handling Day 2 operations (production), survey participants indicated general satisfaction with the different types of stateful workloads being run on Kubernetes.
  • Automating application provisioning and configuration management is the challenge people cite most often in managing data workloads on Kubernetes.
  • Two out of three survey respondents (66%) are using operators to run data on Kubernetes, which can address some data management challenges, but only if they can also deal with other Day 2 issues like observability and managing the storage lifecycle.

Bar chart showing the Challenges of Managing Data Workloads on Kubernetes. Survey participants could choose more than one option. Automating app provisioning and config management 53% App life cycle management, storage life cycle 47% Observability, with metrics, alerts, log processing and workload analysis 45% Securing the environment 40% Autopilot capabilities (horizontal/vertical scaling, tuning, abnormal detection) 40% Upgrading, patching 34% Source: Data on Kubernetes 2022 Report, Data on Kubernetes Community

Transformative Impact on Organizations

The survey revealed a consensus that running data workloads on Kubernetes has a transformative impact on organizations. Perception of value is high, yet may overestimate real benefits.

  • One in three respondents (33%) believe running data on Kubernetes is having a transformative impact on productivity, with another 51% noting at least a significant positive impact. The numbers are only slightly lower when asked about the impact on revenue.
  • Sixty-seven percent said their organization and/or developers are at least 50% more productive after adopting Kubernetes to manage data workloads. That’s up from 57% in last year’s DoK survey. However, this is not comparable to actual productivity benchmarks, at least not yet.
  • Fifty-four percent claim that more than 10% of their organization’s revenue can be attributed to the ability to run data on Kubernetes. Taking a step back, we believe that it is tenuous at best to link Kubernetes to revenue this way. Fifty-two percent of organizations reported that more than half of their data workloads run on Kubernetes. But running production workloads with Kubernetes infrastructure is not the same thing as these workloads actually generating revenue.
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