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Why Data Engineering Teams Are Moving to Kubernetes

· 5 min read
Aytan Jalilova
Aytan Jalilova
Developer Advocate @ IOMETE

Why Data Engineering Teams Are Moving to Kubernetes​

As data systems become increasingly distributed, event-driven, and real-time, the operational burden on engineering teams grows. Legacy data infrastructure — often composed of brittle Hadoop stacks, fixed-capacity clusters, and disconnected orchestration tools — can no longer meet the demands of petabyte-scale data, 24/7 SLAs, and rapidly evolving analytics needs.

That’s why leading data engineering teams are shifting toward Kubernetes-native deployment as a foundational strategy.

Instead of managing VMs or static infrastructure, teams now treat their data pipelines as composable Kubernetes workloads. This shift allows data engineers to automate infrastructure, manage dependencies declaratively, and deliver data products at the speed of modern software engineering.

Why This Shift Is Happening​

1. Scalable Workloads with Native Autoscaling​

Modern data workloads spike and fall unpredictably — from daily ETL batches to sudden bursts of ML training. Kubernetes enables elastic autoscaling for Pods, whether they're Spark executors or ingestion microservices.

IOMETE leverages Kubernetes-native autoscaling to run Spark workloads with optimal resource usage. Clusters expand under heavy load and shrink during idle periods, reducing costs without sacrificing throughput.

2. Cloud Portability and Hybrid Compatibility​

Vendor lock-in is an architectural liability. Kubernetes-native systems like IOMETE provide infrastructure independence. Whether deployed on-prem, in AWS, or across hybrid environments, workloads run consistently using Kubernetes APIs.

For example, IOMETE supports storage backends like MinIO, Apache Ozone, and HDFS, allowing teams to plug into their existing storage fabric while gaining the benefits of a Kubernetes-based compute layer.

3. Declarative Infrastructure and GitOps​

Data teams increasingly adopt Infrastructure as Code (IaC) practices. With Kubernetes, everything — from Spark clusters to Airflow DAGs — can be defined in YAML and version-controlled.

Using ArgoCD, a GitOps tool supported by many IOMETE customers, teams declaratively manage pipeline deployments, secrets, and configs. Rollbacks are as simple as a git revert.

4. Built-in Observability and Control​

Kubernetes-native platforms provide out-of-the-box telemetry: logs, metrics, events, and alerts. When coupled with tools like Prometheus, Grafana, and Spark UIs, engineers can monitor system health, job performance, and resource usage in real time.

IOMETE enhances this with a unified web UI and API that exposes Spark logs, job history, and cluster status — tightly integrated with Kubernetes-native observability.

5. Policy-Driven Access and Governance​

With data decentralization comes the need for stronger controls. Kubernetes allows fine-grained Role-Based Access Control (RBAC), network policies, and Secrets management.

IOMETE builds on this with enterprise-grade features like column-level security, data masking, audit logging, and domain-based access isolation. It’s governance built directly into the data layer.

“Kubernetes-native design lets data engineers stop babysitting infrastructure and focus on building pipelines that scale.”

— Vusal Dadalov, CEO at IOMETE

Core Components of a Kubernetes-Native Data Architecture​

Let’s examine the architectural patterns and components used by Kubernetes-native data platforms like IOMETE. These patterns aren’t just theoretical — they’re deployed in production by enterprises building petabyte-scale, cloud-agnostic data platforms.

Pods, StatefulSets, and Persistent Volumes​

Pods​

In Kubernetes, a Pod is the smallest deployable compute unit. Each Spark executor, ingestion job, or transformation task can run in its own Pod.

IOMETE uses isolated Pods for each compute unit (e.g., a Spark executor), allowing precise scaling, fault isolation, and resource controls.

StatefulSets​

Some data services — such as Trino coordinators or catalog managers — require stable identities and persistent storage. StatefulSets ensure:

  • Predictable DNS names
  • Persistent volume retention across restarts
  • Ordered startup and shutdown

These are critical for systems like metadata catalogs or real-time OLAP engines that IOMETE can integrate with.

Persistent Volumes (PVs)​

Stateful workloads need durable storage. Kubernetes Persistent Volumes (PVs) provide a standardized interface to block, file, or object storage.

IOMETE allows Spark workloads to write to S3-compatible object storage (e.g., MinIO), HDFS, or Ozone, decoupling compute and storage to enhance elasticity.

Helm and Infrastructure as Code​

Helm, the package manager for Kubernetes, simplifies complex deployments through templated configuration files called Charts.

Helm is frequently used in IOMETE-native environments to:

  • Deploy Spark operators
  • Install Airflow with KubernetesExecutor
  • Configure object storage integrations
  • Manage versioned application updates

When paired with Terraform, Crossplane, or Kustomize, Helm enables full-stack infrastructure automation — compute, storage, jobs, and security.

CI/CD Pipelines and GitOps with ArgoCD​

Kubernetes-native platforms are most effective when paired with GitOps practices — using Git as the source of truth for infrastructure and configuration.

ArgoCD is a declarative GitOps controller that watches for changes in Git and syncs them to Kubernetes. It supports features like:

  • Progressive delivery (canary, blue/green)
  • Rollbacks to previous Git states
  • Audit trails for infrastructure changes

IOMETE’s design philosophy aligns with GitOps. Spark jobs, clusters, metadata catalogs, and RBAC policies can all be defined declaratively and deployed via ArgoCD.

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