Best Data Engineering Tools for Docker

Compare the Top Data Engineering Tools that integrate with Docker as of July 2025

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This a list of Data Engineering tools that integrate with Docker. Use the filters on the left to add additional filters for products that have integrations with Docker. View the products that work with Docker in the table below.

What are Data Engineering Tools for Docker?

Data engineering tools are designed to facilitate the process of preparing and managing large datasets for analysis. These tools support tasks like data extraction, transformation, and loading (ETL), allowing engineers to build efficient data pipelines that move and process data from various sources into storage systems. They help ensure data integrity and quality by providing features for validation, cleansing, and monitoring. Data engineering tools also often include capabilities for automation, scalability, and integration with big data platforms. By streamlining complex workflows, they enable organizations to handle large-scale data operations more efficiently and support advanced analytics and machine learning initiatives. Compare and read user reviews of the best Data Engineering tools for Docker currently available using the table below. This list is updated regularly.

1

Chalk

Chalk

Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.

Starting Price: Free

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2

IBM Databand

IBM

Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems.

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3

Knoldus

Knoldus

World's largest team of Functional Programming and Fast Data engineers focused on creating customized high-performance solutions. We move from "thought" to "thing" via rapid prototyping and proof of concept. Activate an ecosystem to deliver at scale with CI/CD to support your requirements. Understanding the strategic intent and stakeholder needs to develop a shared vision. Deploy MVP to launch the product in the most efficient & expedient manner possible. Continuous improvements and enhancements to support new requirements. Building great products and providing unmatched engineering services would not be possible without the knowledge and extensive usage of the latest tools and technology. We help you to capitalize on opportunities, respond to competitive threats, and scale successful investments by reducing organizational friction from your company’s structures, processes, and culture. Knoldus helps clients identify and capture the most value and meaningful insights from data.

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4

witboost

Agile Lab

witboost is a modular, scalable, fast, efficient data management system for your company to truly become data driven, reduce time-to-market, it expenditures and overheads. witboost comprises a series of modules. These are building blocks that can work as standalone solutions to address and solve a single need or problem, or they can be combined to create the perfect data management ecosystem for your company. Each module improves a specific data engineering function and they can be combined to create the perfect solution to answer your specific needs, guaranteeing a blazingly fact and smooth implementation, thus dramatically reducing time-to-market, time-to-value and consequently the TCO of your data engineering infrastructure. Smart Cities need digital twins to predict needs and avoid unforeseen problems, gathering data from thousands of sources and managing ever more complex telematics.

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5

Kestra

Kestra

Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. The UI automatically adjusts the YAML definition any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is defined declaratively in code, even if some workflow components are modified in other ways.

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