Best Data Pipeline Software for Kubernetes
View:
Compare the Top Data Pipeline Software that integrates with Kubernetes as of July 2025
Sort By:
This a list of Data Pipeline software that integrates with Kubernetes. Use the filters on the left to add additional filters for products that have integrations with Kubernetes. View the products that work with Kubernetes in the table below.
What is Data Pipeline Software for Kubernetes?
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 Kubernetes currently available using the table below. This list is updated regularly.
-
1
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 -
2
Dataplane
Dataplane
The concept behind Dataplane is to make it quicker and easier to construct a data mesh with robust data pipelines and automated workflows for businesses and teams of all sizes. In addition to being more user friendly, there has been an emphasis on scaling, resilience, performance and security.Starting Price: Free -
3
TrueFoundry
TrueFoundry
TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.Starting Price: $5 per month -
4
StreamNative
StreamNative
StreamNative redefines streaming infrastructure by seamlessly integrating Kafka, MQ, and other protocols into a single, unified platform, providing unparalleled flexibility and efficiency for modern data processing needs. StreamNative offers a unified solution that adapts to the diverse requirements of streaming and messaging in a microservices-driven environment. By providing a comprehensive and intelligent approach to messaging and streaming, StreamNative empowers organizations to navigate the complexities and scalability of the modern data ecosystem with efficiency and agility. Apache Pulsar’s unique architecture decouples the message serving layer from the message storage layer to deliver a mature cloud-native data-streaming platform. Scalable and elastic to adapt to rapidly changing event traffic and business needs. Scale-up to millions of topics with architecture that decouples computing and storage.Starting Price: $1,000 per month -
5
GlassFlow
GlassFlow
GlassFlow is a serverless, event-driven data pipeline platform designed for Python developers. It enables users to build real-time data pipelines without the need for complex infrastructure like Kafka or Flink. By writing Python functions, developers can define data transformations, and GlassFlow manages the underlying infrastructure, offering auto-scaling, low latency, and optimal data retention. The platform supports integration with various data sources and destinations, including Google Pub/Sub, AWS Kinesis, and OpenAI, through its Python SDK and managed connectors. GlassFlow provides a low-code interface for quick pipeline setup, allowing users to create and deploy pipelines within minutes. It also offers features such as serverless function execution, real-time API connections, and alerting and reprocessing capabilities. The platform is designed to simplify the creation and management of event-driven data pipelines, making it accessible for Python developers.Starting Price: $350 per month -
6
Nextflow
Seqera Labs
Data-driven computational pipelines. Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. Its fluent DSL simplifies the implementation and deployment of complex parallel and reactive workflows on clouds and clusters. Nextflow is built around the idea that Linux is the lingua franca of data science. Nextflow allows you to write a computational pipeline by making it simpler to put together many different tasks. You may reuse your existing scripts and tools and you don't need to learn a new language or API to start using it. Nextflow supports Docker and Singularity containers technology. This, along with the integration of the GitHub code-sharing platform, allows you to write self-contained pipelines, manage versions, and rapidly reproduce any former configuration. Nextflow provides an abstraction layer between your pipeline's logic and the execution layer.Starting Price: Free -
7
Astro
Astronomer
For data teams looking to increase the availability of trusted data, Astronomer provides Astro, a modern data orchestration platform, powered by Apache Airflow, that enables the entire data team to build, run, and observe data pipelines-as-code. Astronomer is the commercial developer of Airflow, the de facto standard for expressing data flows as code, used by hundreds of thousands of teams across the world. -
8
Spring Cloud Data Flow
Spring
Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model. -
9
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. -
10
Observo AI
Observo AI
Observo AI is an AI-native data pipeline platform designed to address the challenges of managing vast amounts of telemetry data in security and DevOps operations. By leveraging machine learning and agentic AI, Observo AI automates data optimization, enabling enterprises to process AI-generated data more efficiently, securely, and cost-effectively. It reduces data processing costs by over 50% and accelerates incident response times by more than 40%. Observo AI's features include intelligent data deduplication and compression, real-time anomaly detection, and dynamic data routing to appropriate storage or analysis tools. It also enriches data streams with contextual information to enhance threat detection accuracy while minimizing false positives. Observo AI offers a searchable cloud data lake for efficient data storage and retrieval. -
11
DataKitchen
DataKitchen
Reclaim control of your data pipelines and deliver value instantly, without errors. The DataKitchen™ DataOps platform automates and coordinates all the people, tools, and environments in your entire data analytics organization – everything from orchestration, testing, and monitoring to development and deployment. You’ve already got the tools you need. Our platform automatically orchestrates your end-to-end multi-tool, multi-environment pipelines – from data access to value delivery. Catch embarrassing and costly errors before they reach the end-user by adding any number of automated tests at every node in your development and production pipelines. Spin-up repeatable work environments in minutes to enable teams to make changes and experiment – without breaking production. Fearlessly deploy new features into production with the push of a button. Free your teams from tedious, manual work that impedes innovation.
- Previous
- You're on page 1
- Next