By Sumit Gupta           22nd November 2023

As analytics engineers, I have had to deal with complex workflows, dependency management nightmares, and inconsistent environments. But what if I told you there’s a way to streamline deployments, optimize workflows, and ensure consistency across multiple platforms? That’s where Docker comes in.

In this guide, I’ll break down what Docker is, why it’s a must-have tool for data engineers, analytics engineers or even data scientists for their model development and how it simplifies our work. Let’s dive in! 

What is Docker?

Docker is an open-source platform that allows you to package applications and all their dependencies into lightweight, portable containers. These containers ensure that your software runs smoothly, whether in development, production, or testing, without compatibility issues.

Why is Docker Important for Data Engineers?

As data engineers, we work with ETL pipelines, data warehouses, and real-time applications. Traditionally, setting up these workflows was a nightmare—long deployment times, inconsistent environments, and dependency conflicts.

Docker changes the game by encapsulating everything—code, libraries, dependencies, and configurations—into a lightweight container. This allows for seamless transitions across different platforms and ensures that our workflows remain efficient and scalable.

Now that we understand what Docker is and why it matters, let’s explore its key advantages.

5 Key Advantages of Docker for Data Engineers

1. Consistency Across Environments
   • With Docker, your data pipelines run the same way in development, testing, and production. No more “it works on my machine” issues!
2. Improved Performance & Scalability
   • Whether you’re handling small tasks or processing massive datasets, Docker makes it easy to scale workflows up or down based on workload demands.
3. Faster Automation & Deployment
   • Docker integrates seamlessly into CI/CD pipelines, automating deployments and reducing manual efforts.
4. Optimized Resource Efficiency
   • Unlike traditional virtual machines, Docker uses system resources more efficiently, allowing you to run workloads with minimal overhead.
5. Enhanced Collaboration
   • Docker enables teams to share consistent environments, reducing configuration conflicts and making collaboration smoother.

To unlock Docker’s full potential, it’s crucial to understand its architecture.

What is Docker Architecture?

Docker works by using images, which define what to include in a container, and containers, which are running instances of those images.

For data engineers, Docker ensures consistency across development, testing, and production environments, making deployments seamless. Here’s a quick look at its core components:

Key Docker Components for Data Engineers 

The key components of Docker ensure deployment, streamline workflows and improve scalability. 

Below is a table of fundamental components of Docker for Data Engineers:

No.	Components	Usage
1.	Docker Engine	The runtime that runs and controls containers.
2.	Docker Images	Pre-made templates that are used to build containers.
3.	Docker Containers	Instances of Docker images that are currently running.
4.	Docker Compose	A tool for creating and executing multi-container applications
5.	Docker Hub	A repository for maintaining and exchanging Docker images

Set Up of Docker for Data Engineers 

Now that we understand the advantages and components of Docker, lets understand the installation process. 

Whether you are using Windows, macOS or Linux, data engineers can work easily across platforms. 

With this, let’s dig into the installation process of Docker.

Docker installation

1. You can install Docker using the Command Line Interface (CLI) by following these steps:

Note: Make sure Docker is enabled to launch at boot after installation

2. Check that Docker is running:

Concepts of Docker Containers & Images

Before we dive into the power of Docker, it’s important to first understand its two core building blocks – Containers and Images. 

These are the foundation of how Docker works, making it easy to run applications smoothly, scale efficiently, and keep everything consistent across different environments.

 1. Docker Containers & Images

Docker image is like a ready-made blueprint that includes everything an application needs to run – code, dependencies and configurations. 

When you launch an image, it becomes a container, which is like a running copy of that blueprint, functioning independently while preserving mobility and isolation.

Containers in data engineering makes it easy to manage distributed systems, databases, and ETL tasks to operate uniformly across many contexts. 

The commands in the docker image are run in this order. Let’s study the principal commands:

• Docker compose build: Before we can run our custom Docker images, we must build them. This command reads the Dockerfile associated with each service in the docker-compose.yml file and creates an image for it. If an image already exists, it updates it based on the latest changes.

• Docker compose up: This command brings our services to life by starting all containers defined in docker-compose.yml. It ensures that all dependencies, configurations, and services are correctly set up.

• If an image hasn’t been built yet, it automatically builds one.
• The -d flag runs containers in the background (detached mode).
• The –build flag forces a fresh build before starting.

• Docker ps: We require a method to figure out which containers are active. The IDs, names, statuses, and exposed ports of every container that is currently in use are displayed in a list by this command.

• Docker compose down: Once we’re done, we need to stop and remove all containers, networks, and volumes. This command safely shuts down everything that was started with docker compose up.

2. OS & its Configuration in Docker Image 

An image serves as your docker container’s blueprint. You can decide which variables to set, which modules to install, etc. Let’s think about our example:

The commands in the Docker image, also known as a Dockerfile, are executed sequentially. Let’s examine the main commands:

• FROM: In order to build up our setups, we require a foundational operating system. Additionally, we can add our configuration to pre-existing Docker images that are accessible via the Docker Hub. The official Delta Lake Docker image is used in our example.

• COPY: To transfer files or directories from our local disk to the image, utilize the copy function. When creating a docker image, the copy command is typically used to copy static files, settings, etc. 
• RUN: To execute a command on your image’s shell terminal, use Run. Usually, it is used to make folders, install libraries, etc.

• ENV:The environment variables of the picture are set by this command. We configured the Spark environment variables in our example.

• ENTRYPOINT: When the image starts, the entrypoint command runs a script. Depending on the parameters supplied to the docker cli when launching a container from this image, we utilize a script file (entrypoint.sh) in our example to start the worker and spark master nodes.

3. Use of Image to initiate Containers 

Do you know containers are like lightweight virtual machines that actually run your applications? 

They are created from images (defined in a Dockerfile), and you can spin up multiple containers from the same image as needed.

3.1 Interaction between the local OS and containers

In a typical data infrastructure setup, multiple containers don’t run independently. They need to talk to each other and the local operating system. 

To make this happen, we map ports in the docker-compose.yml file, allowing services like Spark clusters to communicate seamlessly over HTTP.

But communication is not about networking. Instead it is about sharing files. 

Using mounted volumes, we can sync files between containers and the host machine, ensuring real-time updates without manual intervention. 

Additionally, Docker volumes enable direct file sharing between containers, keeping everything connected and running smoothly. 

Whether it’s syncing logs, configurations, or datasets, these mechanisms make containerized workflows far more efficient and seamless.

Here’s a visual representation of Interaction between the local OS and containers:

The commands in the docker image (docker file) are run in this order. Let’s study the principal commands:

• PORTS:Ports are used to expose services running inside the containers to the host machine.
• VOLUMES:Volumes allow containers to store and share persistent data.

3. 2 Starting Containers with Docker CLI or Docker Compose

With a single command, we can define which image to use, assign a container name, set up volume mounts, and open specific ports. 

However, most data engineering setups involve multiple interconnected services like databases, compute engines, and orchestration tools, all needing to run simultaneously. 

While the Docker CLI allows us to start individual containers, Docker Compose is a much better solution for managing multi-container systems.

Here’s the visual representation of starting Containers with Docker CLI or Docker Compose:

Conclusion 

Docker is a game-changer for data engineers. It provides a scalable, efficient, and streamlined way to manage data workflows. By containerizing applications, ensuring consistency, and simplifying orchestration, Docker makes our lives significantly easier.

Whether you’re handling ETL workflows, real-time streaming, or cloud-native applications, Docker offers the flexibility and reliability needed to focus on what truly matters.

Looking ahead, Docker’s role in AI-driven data pipelines, cloud-native solutions, and automation will only grow.

So, are you ready to elevate your data workflows? Start containerizing your projects with Docker today!

Author Bio:

Sumit Gupta, data science leader with experience leading analytics teams in sales, marketing and product domain. Sumit is also a published author on Tableau with title “The Tableau Workshop”. Sumit writes about all things analytics and career progression for immigrants and more! You can follow Sumit on LinkedIn