Data Pipelines on Kubernetes

1. Data Pipelines on Kubernetes

Kubernetes can also be used to create and maintain data pipelines. Let's discuss how.

2. What are Data Pipelines?

First of all, what are data pipelines? In the most general form, a data pipeline is a set of processes to move data from a source to a destination, transform data from one form into another, and analyze the data to get insights from it. Most data pipelines consist of three major steps: extract, transform, and load, or E-T-L. What does this mean? Let's first describe a so-called ETL pipeline. The extract step simply copies data from a source system, like a database, a file system, or an object store. After extraction, the data from various sources is transformed into a meaningful schema. This allows for easy analytics and helps keep track of what has already been extracted. After this transformation step, the data is loaded into a target data sink. In ETL, this is typically a data warehouse. In recent years, another type of data pipeline has gained popularity, and this is E-L-T. In ELT, extraction also takes place like in ETL, but then loading into a target data sink takes place immediately. This is typically a data lake. After loading takes place, data can be transformed into a meaningful schema when needed. The exchange of these last two steps allows data consumers like data scientists or data engineers to transform the loaded data as needed and hence offers additional flexibility compared to ETL.

3. Data Pipelines on Kubernetes

ETL, as well as ELT, data pipelines map nicely to Kubernetes objects. Our extract, transform, and load steps can be deployed using Deployments or StatefulSets, and the data that gets extracted and transformed can be stored using Persistent Volumes. Further, if we use scalable solutions for all of these steps, we can use Kubernetes to scale our Deployments and our storage where needed. For example, let's assume we have deployed five Pods to perform our Transform step, as shown in the figure. If these five pods cannot deliver enough throughput to transform all of our extracted data in due time, we could scale up the number of Pods to 6, 7, or more. The same is true for storage. As the amount of data increases in our pipeline, we can attach storage as needed and scale accordingly.

4. Open-Source Tools for Data Pipelines

Let us mention some typical open-source tools and software that can be used for data pipelines on Kubernetes. First of all, for extracting from various source systems, we can use tools like Apache Nifi and Apache Kafka, in particular, the Kafka Connect ecosystem. For transforming data, there is Apache Spark, again Apache Kafka, and Databases that use SQL like PostgreSQL. To load data into target data sinks, we can use Apache Spark and Apache Kafka, in particular KSQL and PostgreSQL. For storage, we can deploy object stores like Minio or block storage like Ceph.

5. Let's practice!

Let's now practice and deploy a simple data pipeline.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.