7 Essential Ready-To-Use Data Engineering Docker Containers

Here’s the truth: setting up a robust data engineering environment is often more painful than it should be.

Between dependency conflicts, configuration files, and compatibility issues, you can spend days just getting your infrastructure ready before tackling actual data problems. That’s where Docker containers come in, giving you pre-configured environments you can deploy in minutes with just a few commands.

In this article, I’ve compiled seven essential Docker containers that come in handy for almost every data engineering task you’ll work on. Let’s begin.

Getting Started with Docker Hub

 
Before we dive into the specific containers, here’s the simple pattern you’ll use to pull and run (almost) any image from Docker Hub:

# Pull an image from Docker Hub
$ docker pull image_name:tag

# Run a container from that image
$ docker run -d -p host_port:container_port --name container_name image_name:tag

Now let’s explore the containers that’ll help your data engineering workflow.

1. Prefect: Modern Workflow Orchestration

 
Prefect orchestrates and monitors your data workflows with a developer-friendly, Pythonic approach.

Prefect is simpler to get started with than Airflow. In Prefect, workflows succeed by default and only fail when explicitly told to do so.

Key features:

• Define workflows as Python code instead of XML/YAML
• Built-in retries, notifications, and failure handling
• Intuitive UI for monitoring all your pipeline runs
• Scales well with minimal config changes

How to pull and run:

$ docker pull prefecthq/prefect
$ docker run -d -p 4200:4200 --name prefect prefecthq/prefect orion start

Access the UI at http://localhost:4200 and start creating workflows.

2. ClickHouse: Analytics Database

 
ClickHouse is a fast columnar database designed specifically for OLAP workloads and real-time analytics.

When you need to analyze billions of rows in milliseconds, ClickHouse can be a great choice. Its columnar storage engine makes aggregate queries blazing fast—often 100-1000x faster than traditional row-oriented databases.

Key features:

• Column-oriented storage for optimal analytical query performance
• Fast real-time data ingestion
• Linear scalability across multiple nodes
• SQL interface with extensions for time-series and arrays

How to pull and run:

$ docker pull clickhouse/clickhouse-server
$ docker run -d -p 8123:8123 -p 9000:9000 --name clickhouse clickhouse/clickhouse-server

You can connect via HTTP at http://localhost:8123 or using the native protocol on port 9000. This guide on using clickhouse with Docker network should be useful.

3. Apache Kafka: Stream Processing

 
Kafka is a distributed event streaming platform capable of handling a huge volume of events a day. It enables real-time event-driven architectures.

Key features:

• Store and process streams of records in real-time
• Scale horizontally across multiple nodes for high throughput
• Maintain message ordering within partitions
• Persist data for configurable retention periods

How to pull and run:

$ docker pull bitnami/kafka
$ docker run -d --name kafka -p 9092:9092 -e KAFKA_CFG_ZOOKEEPER_CONNECT=zookeeper:2181 bitnami/kafka

Note: You’ll need ZooKeeper running as well, or you could use a container that bundles both services. Once running, you can create topics and start streaming data through your system with minimal latency.

4. NiFi: Data Flow Automation

 
Apache NiFi is a powerful data integration and flow automation system with a visual interface for designing, controlling, and monitoring data pipelines.

It’s great for automating the movement of data between disparate systems with built-in processors for transformation, routing, and system integration.

Key features:

• Drag-and-drop UI for designing complex data flows
• Guaranteed delivery with back-pressure handling and data provenance
• Several built-in processors for connectivity and transformation
• Fine-grained security policies and data governance

How to pull and run:

$ docker pull apache/nifi:latest
$ docker run -d -p 8443:8443 --name nifi apache/nifi:latest

Access the NiFi UI securely at https://localhost:8443/nifi and start building visual data flows that connect your entire enterprise. The powerful processor library handles everything from simple file operations to complex API integrations.

5. Trino (formerly Presto SQL): Distributed SQL Query Engine

 
Trino is a distributed SQL query engine designed to query data from multiple sources including Hadoop, object storage, relational databases, and NoSQL systems.

It helps solve the data federation problem by allowing you to run fast analytical queries across multiple data sources simultaneously without moving the data.

Key features:

• Query data across multiple databases and data stores simultaneously
• Connect to multiple data sources including PostgreSQL, MySQL, MongoDB, etc.
• Process large volumes of data with distributed execution

How to pull and run:

$ docker pull trinodb/trino:latest
$ docker run -d -p 8080:8080 --name trino trinodb/trino:latest

Access the Trino UI at http://localhost:8080 and start executing queries across all your data sources through a single interface.

6. MinIO: Object Storage

 
MinIO provides S3-compatible object storage, perfect for creating data lakes or storing unstructured data.

It gives you cloud-like storage capabilities locally or on-prem, with the same API as Amazon S3 but under your control.

Key features:

• Store large volumes of unstructured data efficiently
• Compatible with Amazon S3 API for easy integration
• High-performance enough for AI/ML workloads

How to pull and run:

$ docker pull minio/minio
$ docker run -d -p 9000:9000 -p 9001:9001 --name minio minio/minio server /data --console-address ":9001"

Access the MinIO Console at http://localhost:9001 with default credentials minioadmin/minioadmin. You can immediately start creating buckets and uploading files through the interface.

You can run the minio docker image using Podman, too.

7. Metabase: Data Visualization

 
Metabase is an intuitive business intelligence and visualization tool that connects to your databases.

It enables anyone in your organization to ask questions about data and create dashboards through a user-friendly interface.

Key features:

• No-code interface for building charts and dashboards
• SQL editor for power users who want to write custom queries
• Scheduled reports and email/Slack notifications
• Embeddable dashboards for integration with other applications

How to pull and run:

$ docker pull metabase/metabase
$ docker run -d -p 3000:3000 --name metabase metabase/metabase

You can now access Metabase at http://localhost:3000. To connect your data sources, follow the setup wizard. Within minutes, you can start creating visualizations to derive insights from your data.

Wrapping Up

 
So yeah, data engineering doesn’t have to be complicated. With these essential Docker containers, you can skip the tedious setup process and focus on building data pipelines that deliver value to your organization.

Each component works well independently but becomes all the more useful when combined into a comprehensive data stack.

The best part? You can set up this entire stack in under 10 minutes with Docker Compose. You’ll have a production-ready data engineering environment up and running before your coffee gets cold. 😀

What’s your essential Docker container for data engineering? Let me know in the comments!
 
 

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.