Best Data Pipeline Software for Datus

Compare the Top Data Pipeline Software that integrates with Datus as of October 2026

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This a list of Data Pipeline software that integrates with Datus. Use the filters on the left to add additional filters for products that have integrations with Datus. View the products that work with Datus in the table below.

What is Data Pipeline Software for Datus?

Data pipeline software helps businesses automate the movement, transformation, and storage of data from various sources to destinations such as data warehouses, lakes, or analytic platforms. These platforms provide tools for extracting data from multiple sources, processing it in real-time or batch, and loading it into target systems for analysis or reporting (ETL: Extract, Transform, Load). Data pipeline software often includes features for data monitoring, error handling, scheduling, and integration with other software tools, making it easier for organizations to ensure data consistency, accuracy, and flow. By using this software, businesses can streamline data workflows, improve decision-making, and ensure that data is readily available for analysis. Compare and read user reviews of the best Data Pipeline software for Datus currently available using the table below. This list is updated regularly.

1

dbt

dbt Labs

dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.

4 Ratings

Starting Price: $100 per user/ month

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2

Dagster

Dagster Labs

Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.

Starting Price: $0

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3

Prefect

Prefect

Prefect is a workflow orchestration and automation platform designed for the modern context-driven era. It enables teams to turn Python functions into production-ready workflows with minimal effort. Prefect provides open-source foundations alongside managed platforms for enterprise-scale automation. The platform supports building and orchestrating data pipelines, workflows, and AI applications with full observability. Prefect Cloud offers managed orchestration with autoscaling, enterprise authentication, and built-in governance. Prefect Horizon extends automation to AI infrastructure by enabling deployment of MCP servers for AI agents. Trusted by leading organizations, Prefect helps teams scale automation without operational complexity.

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4

Apache Airflow

The Apache Software Foundation

Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows.

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