7 Essential Ready-to-Use Data Engineering Docker Containers

7 Essential Ready-to-Use Data Engineering Docker Containers

This article introduces seven essential Docker containers that streamline data engineering workflows, from ingestion to orchestration. It highlights how Docker simplifies environment setup, avoiding common issues like dependency conflicts and configuration problems.

Getting Started with Docker Hub

The article provides a basic pattern for pulling and running Docker images:

# 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

1. Prefect: Modern Workflow Orchestration

• Description: Prefect is a developer-friendly, Pythonic tool for orchestrating and monitoring data workflows.
• Key Features:
  • Define workflows using Python code.
  • Built-in retries, notifications, and failure handling.
  • Intuitive UI for monitoring.
  • Scalable with minimal configuration.
• How to pull and run:

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

*   **Access:** UI available at `http://localhost:4200`.

### 2. ClickHouse: Analytics Database

*   **Description:** ClickHouse is a fast, columnar database optimized for OLAP workloads and real-time analytics.
*   **Key Features:**
    *   Columnar storage for high query performance.
    *   Fast real-time data ingestion.
    *   Linear scalability.
    *   SQL interface with extensions.
*   **How to pull and run:**
    ```
docker pull clickhouse/clickhouse-server
docker run -d -p 8123:8123 -p 9000:9000 --name clickhouse clickhouse/clickhouse-server

• Access: Connect via HTTP at http://localhost:8123 or native protocol on port 9000.

3. Apache Kafka: Stream Processing

• Description: Kafka is a distributed event streaming platform for real-time, event-driven architectures.
• Key Features:
  • Process streams of records in real-time.
  • Horizontal scalability for high throughput.
  • Maintains message ordering within partitions.
  • Configurable data persistence.
• 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:** Requires ZooKeeper or a bundled container.

### 4. NiFi: Data Flow Automation

*   **Description:** Apache NiFi is a system for automating data integration and flow with a visual interface.
*   **Key Features:**
    *   Drag-and-drop UI for designing data flows.
    *   Guaranteed delivery with back-pressure handling.
    *   Extensive processor library.
    *   Fine-grained security and governance.
*   **How to pull and run:**
    ```
docker pull apache/nifi:latest
docker run -d -p 8443:8443 --name nifi apache/nifi:latest

• Access: UI available securely at https://localhost:8443/nifi.

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

• Description: Trino is a distributed SQL query engine for querying data from multiple sources.
• Key Features:
  • Query data across diverse sources simultaneously.
  • Connects to various databases (PostgreSQL, MySQL, MongoDB, etc.).
  • Processes large data volumes 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:** UI available at `http://localhost:8080`.

### 6. MinIO: Object Storage

*   **Description:** MinIO provides S3-compatible object storage for data lakes and unstructured data.
*   **Key Features:**
    *   Efficient storage for large unstructured data.
    *   Amazon S3 API compatibility.
    *   High performance 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: MinIO Console at http://localhost:9001 (credentials: minioadmin/minioadmin).

7. Metabase: Data Visualization

• Description: Metabase is an intuitive BI tool for creating charts and dashboards from databases.
• Key Features:
  • No-code interface for visualizations.
  • SQL editor for advanced users.
  • Scheduled reports and notifications.
  • Embeddable dashboards.
• How to pull and run:

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

*   **Access:** Metabase available at `http://localhost:3000`.

### Wrapping Up

The article concludes by emphasizing that data engineering doesn't need to be complex. These Docker containers simplify setup, allowing focus on building valuable data pipelines. The entire stack can be deployed using Docker Compose in minutes. The author invites readers to share their essential data engineering Docker containers in the comments.

### About the Author

Bala Priya C is a developer and technical writer from India, specializing in DevOps, data science, and natural language processing. She contributes to KDnuggets by authoring tutorials and guides.