Docker Containers- Data Engineers' Arsenal

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2. Data engineering is a lifesaver operation for data science professionals, including data scientists and data analysts. But the truth is, setting up an effective environment for data engineering today is highly complex. Data engineering is the backbone for modern analytics, machine learning, and data-driven decision-making systems. But with datasets becoming more complex and bigger in size, the tools needed to handle them should also be advanced enough. The solution is - Docker Containers. These are lightweight, portable environments that facilitate easy deployment and management of data tools across development, staging, and production environments WHY USE DOCKER CONTAINERS? Docker containers offer several advantages in data engineering, such as: Portability They can run consistently in various environments, including local machines and cloud servers and can be deployed seamlessly. Faster Development Developers can quickly spin up isolated environments, which can further help with faster testing, debugging, and iteration of applications. Resource Efficiency Containers share the host OS kernel, which makes them lightweight and more efficient as compared to traditional virtual machines. Simplified Dependency Management Each container includes all the necessary dependencies that eliminate any kind of conflicts and make application setup simple. Scalability and Automation Docker can also be integrated easily with orchestration tools like Kubernetes, which is helpful in automated scaling and load-balanced deployments. www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

3. GET STARTED WITH DOCKER HUB You will have to pull and run all the image from Docker Hub, no matter which containers you use. Use the following command for it: # 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 TOP DOCKER CONTAINERS FOR YOUR DATA ENGINEERING ENDEAVORS So, here are the popular ready-to-use Docker Containers that will make your data engineering tasks much easier. Tool Image prefecthq/prefect Prefect ClickHouse clickhouse/clickhouse-server Apache Kafka confluentinc/cp-kafka or bitnami/kafka Apache NiFi apache/nifi Trino trinodb/trino MinIO minio/minio Metabase metabase/metabase www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

4. PREFECT CLICKHOUSE How to Pull and Run: How to Pull and Run: $ docker pull prefecthq/prefect $ docker run -d -p 4200:4200 --name prefect prefecthq/prefect orion start $ docker pull clickhouse/clickhouse-server $ docker run -d -p 8123:8123 -p 9000:9000 --name clickhouse clickhouse/clickhouse-server Purpose Columnar database for high-speed analytics Purpose Modern workflow orchestration for data pipelines Prefect is a newer alternative to Airflow. It has been designed to orchestrate and monitor complex data workflows with minimal boilerplate. Data professionals can build dynamic DAGs using Python and enjoy excellent support for retries, caching, and parameterization. It is an open-source column-oriented database that has been optimized for fast analytical queries on huge datasets. Data science professionals prefer it for real-time dashboards and large-scale BI systems. Why Use Prefect? The official Prefect Docker image provides a quick way to spin up agents, servers, or flows for local testing and cloud execution. Why Use ClickHouse? With the Docker image, you can instantly launch fully functional ClickHouse server to begin running analytics on large datasets. Key Features: Key Features: Python-native DAGs with no YAML configuration High compression and fast I/O for large tables Distributed query support Built-in retry, logging, and state tracking Real-time data ingestion Cloud and self-hosted orchestration options SQL-compliant interface Use Case: Task-level versioning and dependency resolution Use Case: Use ClickHouse containers to analyze web logs, telemetry data, or high-velocity events with millisecond-level performance. Use Prefect containers to manage dynamic ETL workflows, schedule data syncs, and monitor pipeline health with minimal setup. www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

5. APACHE KAFKA NIFI How to Pull and Run: 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 $ docker pull apache/nif:latest $ docker run -d -p 8443:8443 --name nif apache/nif:latest Purpose Automate and manage data flows betwee systems Purpose Distributed event streaming and message queuing Apache NiFi facilitates drag-and-drop data integration with excellent processor support for routing, transforming, and ingesting data across systems. Kafka is an industry-standard platform used to build real-time data pipelines and streaming apps. It can easily handle millions of events per second. Why use Apache Kafka? It makes the complex setup of Kafka easier by packaging Zookeeper and Kafka in an easy-to configure environment using Docker Compose. Key Features: Why use NiFi? If you launch NiFi via Docker, you can access its web UI and processors quickly without handling Java or dependency issues manually. Key Features: Fault-tolerant, scalable architecture Visual flow-based programming interface High-throughput event processing Extensive processor library (FTP , Kafka, SQL, HTTP , etc.) Schema registry and connector support Built-in provenance and data lineage tracking Integration with stream processing frameworks Use Case: Secure data transfer and back-pressure control Use Case: Use Kafka containers to transport and buffer real-time data from applications, sensors, or logs into downstream analytics systems. Use NiFi containers to integrate disparate systems, sanitize streaming data, or automate ingestion from IoT devices and APIs. www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

6. TRINO MINIO How to Pull and Run: How to Pull and Run: $ docker pull trinodb/trino:latest $ docker run -d -p 8080:8080 --name trino trinodb/trino:latest $ docker pull minio/minio $ docker run -d -p 9000:9000 -p 9001:9001 --name minio minio/minio server /data --console-address ":9001" Purpose Object storage with S3 API compatibility Purpose Distributed SQL query engine for big data Trino is a very powerful SQL engine mostly used to query data across different storage systems such as S3, Hive, Kafka, PostgreSQL, and others – all in real-time Why use Trino? Trino Docker image offers instant deployment of the coordinator and worker nodes to start running federated queries across sources. Key Features: MinIO offers scalable, high-performance object storage, which is perfect for storing raw files, backups, and even large-scale datasets in a cloud-native way Why use MinIO? Using MinIO, you can emulate an S3-compatible storage system on your local machine or within private clouds with just a single Docker command. Key Features: Query multiple data sources with ANSI SQL Amazon S3-compatible API Extremely fast and parallelized query execution High-speed object uploads/downloads Pluggable connectors for databases, file systems, and data lakes Bucket policies, access controls, and encryption Supports security and access control Use Case: Works seamlessly with Spark, Trino, and DVC Use Case: Use Trino containers to build a virtual data lakehouse, enabling users to run interactive queries across structured and unstructured data. Use MinIO containers as a local S3 bucket to develop, test, and run data lake operations without depending on AWS. www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

7. METABASE How to Pull and Run: $ docker pull metabase/metabase $ docker run -d -p 3000:3000 --name metabase metabase/metabase Purpose Business intelligence and data visualization CONCLUSION It is another open-source data visualization tool that can connect to different kinds of databases so that data science professionals can explore and present insights without SQL knowledge. The core advantage of using Docker in data engineering is its consistency, speed, and scalability. No matter the environment, whether you are setting up a local test environment or deploying it to the cloud, or maybe just want to collaborate across teams, these Docker containers are a great tool offering a plug-and-play approach to modern data workflows Why use Metabase? It is the easiest way to start a BI dashboard within minutes for prototyping or internal reporting. Key Features: Connects to MySQL, PostgreSQL, MongoD`B, Redshift, and more Intuitive drag-and-drop dashboard builder Sharing, scheduling, and embedding reports Basic SQL editor for power users Use Case: Use Metabase containers to quickly visualize KPIs, build dashboards, or enable non-technical teams to explore data. www.usdsi.org © Copyright 2025. United States Data Science Institute. All Rights Reserved

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