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Key Tools in the Kubernetes-Native Data Stack

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

Key Tools in the Kubernetes-Native Data Stack​

The success of Kubernetes-native deployment in data engineering depends heavily on the right tools — not just for orchestration, but also for compute, storage, observability, and governance. This section explores the most critical technologies in a modern Kubernetes-native data stack, with examples of how platforms like IOMETE integrate or complement them.

Apache Airflow on Kubernetes​

Apache Airflow remains one of the most widely adopted workflow orchestration tools in the data ecosystem. Its flexibility in defining DAGs (Directed Acyclic Graphs) for complex data pipelines makes it a natural fit for Kubernetes — especially when paired with the KubernetesExecutor.

Why KubernetesExecutor?

  • Each task is launched in its own Pod
  • Native autoscaling without Celery
  • Full workload isolation and resource constraints

How IOMETE fits in:

IOMETE integrates seamlessly with Airflow through its SQL endpoints and Spark job APIs. Spark jobs triggered in Airflow can target IOMETE compute clusters, using Spark Connect or Apache Arrow Flight for fast transport. Workload monitoring and job history are available in IOMETE’s UI, while Airflow retains orchestration logic.

YAML snippet:

executor: KubernetesExecutor

This configuration enables Airflow to fully utilize Kubernetes-native scheduling and isolation, aligning with GitOps workflows and modern CI/CD practices.

Apache Spark Operator​

Deploying Spark applications on Kubernetes traditionally involved writing complex shell scripts or managing custom resources manually. The Apache Spark Operator solves this with Kubernetes-native CRDs (SparkApplication) that declaratively define jobs.

Key Features:

  • Submitting Spark jobs via YAML
  • Dynamic executor scaling
  • Monitoring and Spark UI integration
  • Native failure recovery and retries

How IOMETE fits in:

IOMETE abstracts much of this operational complexity while remaining fully Kubernetes-native under the hood. Users can launch Spark clusters with a few clicks or API calls, but these actions translate to Kubernetes-native resources behind the scenes (Pods, Jobs, PVCs, and autoscalers).

Example SparkApplication CRD:

apiVersion: sparkoperator.k8s.io/v1beta2
kind: SparkApplication
metadata:
  name: example-spark-pi
spec:
  type: Scala
  mode: cluster
  image: spark:3.3.1
  mainClass: org.apache.spark.examples.SparkPi
  mainApplicationFile: local:///opt/spark/examples/jars/spark-examples.jar

Apache Flink is widely used for real-time stream processing, with support for stateful computations, event-time windows, and low-latency analytics.

The Flink Kubernetes Operator enables:

  • Declarative deployment of Flink jobs and clusters
  • Savepoint management for checkpoint recovery
  • High availability via Kubernetes-native primitives

While IOMETE’s core runtime is based on Spark, it complements Flink workloads by providing a unified storage and metadata layer — particularly using Apache Iceberg tables written to object storage, which can be shared between batch (Spark) and streaming (Flink) pipelines.

ClickHouse, Trino, and Other Cloud-Native Engines​

Query engines and OLAP databases are increasingly being deployed Kubernetes-natively, bringing compute closer to your data and removing the need for managed services.

ClickHouse​

  • Columnar OLAP engine optimized for high-speed analytics
  • Uses StatefulSets and PVCs for durable, performant storage
  • Integrates with Kubernetes-native monitoring and backup tools

Trino​

  • Distributed SQL engine that queries multiple data sources (Hive, Iceberg, JDBC)
  • Well-suited for federated data lakes or hybrid architectures

How IOMETE fits in:

These engines can be deployed alongside IOMETE in the same Kubernetes environment. Trino can directly query IOMETE’s Iceberg-backed catalogs. ClickHouse can consume from object storage buckets written by IOMETE’s ETL jobs.

This modularity exemplifies the “composable data stack” philosophy: each service operates independently, yet integrates seamlessly via Kubernetes and open standards.

Other Essential Tools in the Stack​

  • MinIO: S3-compatible object store, often used with IOMETE in on-prem deployments
  • Prometheus + Grafana: Monitoring and alerting across Spark jobs, Airflow DAGs, and cluster health
  • Cert-Manager + Vault: Manage TLS, secrets, and authentication across services
  • KEDA (Kubernetes Event-driven Autoscaler): Used for scaling jobs based on queue lengths, Kafka topics, or custom metrics

Summary​

These tools form the foundation of Kubernetes-native data platforms. When deployed and managed together, they enable:

  • End-to-end declarative infrastructure
  • Autoscaling for both batch and stream processing
  • Unified governance, logging, and monitoring
  • Elastic, cloud-agnostic operations across teams

IOMETE’s advantage is in how it packages many of these capabilities — from Spark compute to catalog governance — into a unified, Kubernetes-native platform. It doesn’t replace these tools but acts as a glue layer that simplifies integration and speeds up delivery for data teams.

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