How I Learned to Stop Worrying About the Data and Love Workflow Automation with Komodor

AI/ML workloads are growing rapidly, and Kubernetes is emerging as the preferred platform for running data pipelines at scale, thanks to its unmatched scalability and flexibility. As data teams increasingly rely on workflow automation tools like Apache Airflow, Argo Workflows, Apache Spark, and Kubeflow, they face new operational challenges. While these tools simplify orchestration, they also introduce complexity, troubleshooting blind spots, and cost inefficiencies, especially for data engineers who aren’t Kubernetes experts.

Kubernetes extends beyond just managing workloads––it’s an entire ecosystem. Add-ons like workflow automation, data streaming, service meshes, policy engines, and autoscalers are essential for modern Kubernetes environments to ensure operational efficiencies. But, with the good often come downsides, they also tend to create new operational risks when misconfigured, leading to cascading failures, downtime, and unexpected cost overruns.

Komodor provides end-to-end visibility, automated troubleshooting, and intelligent optimization for everything running in Kubernetes clusters, not just workloads, but also the full add-ons ecosystem. Whether it’s failing Spark jobs, misconfigured Argo Workflows, or a rogue cert-manager instance causing TLS issues, it’s now possible for teams to detect, investigate, and pinpoint the exact root cause, enabling rapid remediation of common issues much more simply than before.

Why Kubernetes is Transforming Workflow Automation

The rise in data-intensive workloads has significantly increased the need for scalable, automated workflow management. Kubernetes has proven to be a natural fit because it handles containerized applications at scale, while enabling dynamic resource allocation that integrates with widely adopted cloud-native tooling and stacks.

This is why tools like Argo Workflows and ArgoCD are gaining popularity. Argo Workflows is a Kubernetes-native workflow orchestrator that runs multi-step data pipelines as directed acyclic graphs (DAGs). In a DAG, each node represents a task, and edges define dependencies between them. The "directed" part means that tasks flow in a specific order, while "acyclic" ensures there are no loops, preventing infinite execution cycles.

A Quick Primer on DAGs

DAGs are widely used in data science and engineering, because most data workflows follow a structured sequence—data is ingested, transformed, and then used for analysis or model training. A DAG allows engineers to break down complex workflows into modular, manageable steps, ensuring that each stage runs only when its dependencies are met.

For example, in an AI/ML pipeline, a DAG could define a sequence where raw data is first preprocessed, then fed into a feature engineering step, followed by model training, and finally evaluated before deployment. With Argo Workflows managing this process, data teams gain scalability, fault tolerance, and automation, ensuring that failures in one stage don’t disrupt the entire pipeline.

ArgoCD complements this by providing GitOps-driven continuous delivery, ensuring that the infrastructure and workflows remain in sync with their version-controlled definitions. This combination enables organizations to streamline and automate their data pipelines, while maintaining reliability and reproducibility in Kubernetes environments.

By leveraging these tools, data engineers can automate AI/ML pipelines, while bridging the gap between data engineering and Kubernetes operations. But even with workflow automation, troubleshooting remains a major challenge. This is where Komodor transforms the game even further.

The Hidden Challenges of Workflow Automation on Kubernetes

Despite the benefits of workflow automation, teams face three major challenges when running AI/ML workloads on Kubernetes.

• Lack of visibility into workflow failures. When a job fails, users often have no idea why, as logs are scattered across multiple pods, and Kubernetes doesn’t retain historical data on deleted resources.
• Runaway cloud costs. AI/ML workflows consume massive compute resources. Without proper monitoring and optimization, costs can spiral out of control before teams even notice.
• Dependency on platform teams.?When issues arise, they’re often escalated to DevOps, leading to ticket overload, slow resolution times, and development bottlenecks.

Which brings us to Apache Spark, one of the leading tools in data engineering. For those less familiar, Apache Spark plays a critical role in modern data processing, serving as a powerful batch processing framework for handling large datasets in distributed computing environments. Spark is commonly used for data transformations, making it an essential component of ETL processes, batch jobs, and machine learning workflows.

Many organizations run Spark jobs on Kubernetes to take advantage of its scalability and flexibility, but troubleshooting failures and managing resources in this dynamic environment remains a challenge. These jobs are often scheduled using Airflow, which efficiently orchestrates workflows, however it doesn’t provide troubleshooting capabilities when jobs fail.?

When a Spark job terminates unexpectedly, its logs disappear along with the pod, making debugging nearly impossible. Without pod-level insights, engineers are left guessing what went wrong, and troubleshooting becomes a process of trial and error. This is where Komodor enhances Spark (and Airflow) operations through deep visibility, automated issue detection, and troubleshooting, ensuring that workflows remain reliable and efficient.

Komodor: The First Full-Stack Kubernetes Management Platform

Komodor simplifies workflow automation troubleshooting, reduces cloud costs, and enables data teams to manage their own pipelines without Kubernetes expertise. But it doesn’t stop at workflows––it also covers the entire Kubernetes add-ons ecosystem. Kubernetes isn't just a platform, it’s an extensible system of CRDs, operators, and third-party add-ons. Without proper management, these add-ons can become a liability, not an asset.

Data engineers can now have a platform that provides mission control for add-ons like:

• Workflow Automation for tools like Argo Workflows, Airflow, Kubeflow––enabling teams to gain full visibility into every workflow execution, track failures across historical data, and optimize job performance.
• Data Streaming for tools like Kafka, Pulsar, Flink––for monitoring pod health, identifying partition imbalances, and preventing cascading failures downstream.
• Autoscalers, like HPA, KEDA, ensure correct pod and node scaling, preventing over-provisioning and unnecessary cloud costs.
• Cert-Manager and TLS management helps detect expiring certificates before they cause outages, providing preemptive alerts and remediation guidance.
• Service Meshes like Istio, Linkerd, are leveraged to identify connectivity issues and resolve policy misconfigurations that impact service-to-service communication.
• Infrastructure-as-Code based controls, e.g. Terraform, allow tracking drift in IaC definitions to prevent unexpected runtime failures and cost overruns.

By offering full-stack visibility and correlating insights across Kubernetes resources, applications, and add-ons, Komodor creates a single pane of glass for managing even the most complex environments.

Empowering Data Teams

Komodor simplifies workflow management for data teams, with:

• Real-Time Monitoring. Gain instant insights into workflow executions and pinpoint failures with detailed pod-level data.

• Automated Troubleshooting for Spark and More. Diagnose and resolve failed jobs efficiently, even when logs from terminated pods are unavailable.

• AI-Driven Root Cause Analysis. With Klaudia, proactively detect and resolve issues before they escalate, saving valuable time and resources.
• Cost and Performance Optimization. Monitor inefficient workloads and optimize resource allocation, preventing runaway costs in AI/ML workflows.
• Self-Service Troubleshooting. Enable data engineers to resolve issues without constant reliance on platform teams, reducing bottlenecks and accelerating development cycles.

Bridging the Gap for Data Engineers

Kubernetes has unlocked incredible opportunities for data engineering, but it must empower teams—not overburden them. Komodor bridges the gap between Kubernetes and data teams, providing the tools needed to independently manage workflows, optimize resources, and tackle troubleshooting challenges.

With full-stack Kubernetes management capabilities, organizations can give data engineers the tools they need to manage their own workflows without relying on DevOps intervention. By providing proactive monitoring, Komodor ensures that AI/ML pipelines remain cost-efficient and scalable, preventing resource waste and performance bottlenecks. In a single platform, it’s possible to have a single control plane for Kubernetes workloads and add-ons, simplifying operations and reducing complexity.?

With full-stack Kubernetes visibility and automated remediation playbooks, Komodor ensures AI/ML pipelines remain scalable, cost-efficient, and reliable. By simplifying operations and reducing complexity, it empowers organizations to focus on innovation—not firefighting.

By simplifying troubleshooting and delivering real-time insights, Komodor helps teams manage Kubernetes environments more effectively by reducing troubleshooting complexity and providing real-time insights. Ready to simplify workflow automation? Start your free trial today.