Using Kubernetes for Data Pipelines: Beginner's Guide

Resource type


What Is Kubernetes?

Kubernetes is an open-source platform designed to automate deploying, scaling, and managing containerized applications. It's a system for running and coordinating applications across numerous machines. Kubernetes is known for its ability to run complex applications with numerous interdependent components across a cluster of machines while ensuring high availability and failover.

The basic unit of Kubernetes is a pod. Kubernetes uses a cluster of pods to run your applications. Each pod can run one or more related Docker containers. Kubernetes offers a high level of abstraction over the infrastructure layer, which means you don't have to worry about the underlying hardware or the operating system. Instead, you can focus on your application's functionality and performance.

Kubernetes has become a standard for running production applications in the cloud. Its ability to manage resources efficiently and scale applications dynamically makes it a suitable choice for managing data pipelines. It simplifies the complexities of running data-intensive applications, making it easier for data scientists and engineers to focus on their core tasks.

Basic Concepts of Kubernetes

Let’s look at the key components of Kubernetes.


In the Kubernetes ecosystem, pods are the smallest and simplest units. A pod encapsulates an application container (or, in some cases, multiple containers), storage resources, a unique network IP, and options that govern how the container(s) should run. Each pod is meant to run a single instance of a given application, and it can contain one or many containers.

A pod represents an application-specific "logical host" in a Kubernetes cluster. It contains one or more application containers which are relatively tightly coupled. For example, a pod might consist of an application container along with a helper container that's meant to assist the application. These containers share resources and dependencies, communicate with each other, and coordinate when and how they are terminated.


In Kubernetes, a service is an abstraction that defines a logical set of pods and a policy by which to access them. Services enable a loose coupling between dependent pods. A service routes traffic across a set of pods. Services are the abstraction that allow pods to die and replicate in Kubernetes without impacting your application.

A service in Kubernetes can be likened to a load balancer. It distributes network traffic to a set of pods, thus ensuring that the function they provide, as a group, is reliable and highly available. This is particularly important in a data pipeline, where continuity and reliability of data flow are crucial.


A deployment in Kubernetes provides declarative updates for pods and ReplicaSets. You describe the desired state in a deployment, and the deployment controller changes the actual state to the desired state at a controlled rate.

Deployments are the recommended way to manage the creation and scaling of pods, allowing for easy scaling and rolling updates. They are useful for managing data pipelines in Kubernetes, enabling you to scale your pipeline to handle larger data loads or update your pipeline's software with zero downtime.


A volume in Kubernetes is a directory, possibly with some data in it, which is accessible to the containers in a pod. A Kubernetes volume, however, has an explicit lifetime - the same as the pod that encloses it. This means that a volume outlives any containers that run within the pod, and data is preserved across container restarts.

Volumes also allow you to store data outside the container’s filesystem, preventing data loss when a container crashes or restarts. This is a crucial aspect when dealing with data pipelines, as data loss or unavailability could disrupt the entire pipeline.

Understanding Data Pipelines in Kubernetes

A data pipeline involves a series of steps, each of which performs a particular operation on the data, from extraction and transformation to loading it into an analytic system. In a Kubernetes-managed data pipeline, each step in the pipeline could be a pod, a set of pods, or a job.

Kubernetes ensures that these pods and jobs run efficiently and that the overall data pipeline continues to function even if individual pods fail. This is possible due to Kubernetes' self-healing mechanism, which restarts failed pods and reschedules pods when nodes die.

Kubernetes' scaling capabilities are also important for data pipelines. As the volume of data increases, Kubernetes allows you to easily scale your pipeline to handle the increased load. You can even set up your data pipeline to scale automatically based on CPU usage or other application-specific metrics.

What Is Involved in Using Kubernetes for a Data Pipeline?

Here are the general steps you would go through to set up your data pipeline in Kubernetes.

Install Kubernetes and Set Up Kubectl

The first step is to install Kubernetes. There are several ways to do this, but the easiest method is to use a cloud service like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS). These services provide a managed Kubernetes environment, relieving you from the complexities of installation and configuration.

Next, install kubectl, the command-line tool used to interact with the Kubernetes cluster. It's easy to install kubectl on any system, whether it's Linux, macOS, or Windows. After installation, configure kubectl to interact with your Kubernetes cluster. You'll need to know the address of your cluster, which you can find in the dashboard of your chosen cloud service.

Data Ingestion

Data ingestion involves extracting data from various sources and loading it into the Kubernetes cluster. The data may come from APIs, databases, or data streams, among other sources.

Kubernetes supports several data ingestion tools, including Apache Kafka, Fluentd, and Logstash. These tools can help you collect, transform, and load your data into the Kubernetes cluster.

Data Processing

Data processing involves cleaning, transforming, and analyzing the data to derive meaningful insights. Kubernetes supports several data processing tools, including Apache Beam, Apache Flink, and Apache Spark.

These tools can perform a wide range of tasks, such as filtering data, aggregating data, and performing complex computations. With Kubernetes, you can scale these tools across multiple nodes to process large amounts of data efficiently.

Data Storage

After processing the data, you need to store it in a format and location that makes it easy to access and analyze. Kubernetes supports a variety of data storage solutions, such as Amazon S3, Google Cloud Storage, and Azure Blob Storage.

These cloud-based storage solutions provide a scalable and durable way to store your data. They also offer advanced features, such as data versioning and lifecycle management.

Prepare Output Data and Deploy Output Services

You need to prepare the data for output. This may involve transforming the data into a format that's suitable for analysis or visualization. For instance, you might convert the data into a CSV file or load it into a database.

Next, deploy output services. These are services that consume the output data and provide insights to users. Examples of output services include data visualization tools, reporting tools, and machine learning models. Kubernetes can host these services, providing a scalable and reliable platform for delivering insights.

Testing Your Data Pipeline

Testing is critical to ensure that your pipeline works as expected and can handle the volume of data you expect to process. It's also important to test the performance of your pipeline, to ensure it can process data quickly and efficiently.

Kubernetes provides several tools for testing data pipelines, including Kube-bench and Sonobuoy. These tools can help you identify performance bottlenecks and ensure that your pipeline is robust and reliable.


Kubernetes offers several benefits for data pipelines, including scalability, reliability, and flexibility. By leveraging Kubernetes, you can build a robust data pipeline that can handle large volumes of data and deliver valuable insights. The steps discussed here should give you an idea of how to start using Kubernetes to handle data pipelines.


Author Bio: Gilad David Maayan

Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Check Point, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.