Open Source Windows Data Pipeline Tools
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Data Pipeline Tools for Windows
View 19 business solutionsBrowse free open source Data Pipeline tools and projects for Windows below. Use the toggles on the left to filter open source Data Pipeline tools by OS, license, language, programming language, and project status.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Pentaho
Pentaho offers comprehensive data integration and analytics platform.
Pentaho couples data integration with business analytics in a modern platform to easily access, visualize and explore data that impacts business results. Use it as a full suite or as individual components that are accessible on-premise, in the cloud, or on-the-go (mobile). Pentaho enables IT and developers to access and integrate data from any source and deliver it to your applications all from within an intuitive and easy to use graphical tool. The Pentaho Enterprise Edition Free Trial can be obtained from https://pentaho.com/download/ -
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lakeFS
lakeFS - Git-like capabilities for your object storage
Increase data quality and reduce the painful cost of errors. Data engineering best practices using git-like operations on data. lakeFS is an open-source data version control for data lakes. It enables zero-copy Dev / Test isolated environments, continuous quality validation, atomic rollback on bad data, reproducibility, and more. Data is dynamic, it changes over time. Dealing with that without a data version control system is error-prone and labor-intensive. With lakeFS, your data lake is version controlled and you can easily time-travel between consistent snapshots of the lake. Easier ETL testing - test your ETLs on top of production data, in isolation, without copying anything. Safely experiment and test on full production data. Easily Collaborate on production data with your team. Automate data quality checks within data pipelines. -
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Conduit
Conduit streams data between data stores. Kafka Connect replacement
Conduit is a data streaming tool written in Go. It aims to provide the best user experience for building and running real-time data pipelines. Conduit comes with batteries included, it provides a UI, common connectors, processors and observability data out of the box. Sync data between your production systems using an extensible, event-first experience with minimal dependencies that fit within your existing workflow. Eliminate the multi-step process you go through today. Just download the binary and start building. Conduit connectors give you the ability to pull and push data to any production datastore you need. If a datastore is missing, the simple SDK allows you to extend Conduit where you need it. Conduit pipelines listen for changes to a database, data warehouse, etc., and allows your data applications to act upon those changes in real-time. Run it in a way that works for you; use it as a standalone service or orchestrate it within your infrastructure. -
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Apache SeaTunnel
SeaTunnel is a distributed, high-performance data integration platform
SeaTunnel is a very easy-to-use ultra-high-performance distributed data integration platform that supports real-time synchronization of massive data. It can synchronize tens of billions of data stably and efficiently every day, and has been used in the production of nearly 100 companies. There are hundreds of commonly-used data sources of which versions are incompatible. With the emergence of new technologies, more data sources are appearing. It is difficult for users to find a tool that can fully and quickly support these data sources. Data synchronization needs to support various synchronization scenarios such as offline-full synchronization, offline-incremental synchronization, CDC, real-time synchronization, and full database synchronization. Existing data integration and data synchronization tools often require vast computing resources or JDBC connection resources to complete real-time synchronization of massive small tables. -
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Dolphin Scheduler
A distributed and extensible workflow scheduler platform
Apache DolphinScheduler is a distributed and extensible workflow scheduler platform with powerful DAG visual interfaces, dedicated to solving complex job dependencies in the data pipeline and providing various types of jobs available `out of the box`. Dedicated to solving the complex task dependencies in data processing, making the scheduler system out of the box for data processing. Decentralized multi-master and multi-worker, HA is supported by itself, overload processing. All process definition operations are visualized, Visualization process defines key information at a glance, One-click deployment. Support multi-tenant. Support many task types e.g., spark,flink,hive, mr, shell, python, sub_process. Support custom task types, Distributed scheduling, and the overall scheduling capability will increase linearly with the scale of the cluster. -
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Mage.ai
Build, run, and manage data pipelines for integrating data
Open-source data pipeline tool for transforming and integrating data. The modern replacement for Airflow. Effortlessly integrate and synchronize data from 3rd party sources. Build real-time and batch pipelines to transform data using Python, SQL, and R. Run, monitor, and orchestrate thousands of pipelines without losing sleep. Have you met anyone who said they loved developing in Airflow? That’s why we designed an easy developer experience that you’ll enjoy. Each step in your pipeline is a standalone file containing modular code that’s reusable and testable with data validations. No more DAGs with spaghetti code. Start developing locally with a single command or launch a dev environment in your cloud using Terraform. Write code in Python, SQL, or R in the same data pipeline for ultimate flexibility. -
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Apache Kafka
Mirror of Apache Kafka
Apache Kafka is a distributed streaming platform built around durable, partitioned logs called topics, enabling high-throughput, fault-tolerant event pipelines. Producers append records to partitions, brokers replicate them for durability, and consumer groups read them at their own pace while balancing work across instances. The commit/offset model and retention policies support patterns from real-time processing to event sourcing and audit trails. Exactly-once processing semantics, idempotent producers, and transactions help prevent duplicates across complex dataflows. Kafka Streams and Kafka Connect extend the core: Streams provides a library for stateful stream processing within applications, while Connect standardizes integration with external systems. With horizontal scalability, strong ordering guarantees within partitions, and mature tooling, Kafka serves as the backbone for event-driven architectures across analytics, microservices, and data integration. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
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StarRocks
StarRocks is a next-gen sub-second MPP database for full analytics
StarRocks is the next generation of real-time SQL engines for enterprise analytics. Real-time analytics is notoriously difficult. Complex data pipelines and de-normalized tables have always been a necessary evil. Processing any updates or deletes once data arrives has not been possible- until now. StarRocks solves these challenges and makes real-time analytics easy. Get amazing query performance on Star or Snowflake Schemas directly. From canceled orders to updated items, your analytics applications can easily handle them with StarRocks. From streaming data to change data capture, StarRocks meets the data ingestion demands of real-time analytics. Scale storage and computing power horizontally and support tens of thousands of concurrent users. All of your BI tools work with StarRocks through standard SQL. StarRocks provides superior performance. It is also a unified OLAP covering most data analytics scenarios. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Backstage
Backstage is an open platform for building developer portals
Powered by a centralized software catalog, Backstage restores order to your infrastructure and enables your product teams to ship high-quality code quickly, without compromising autonomy. At Spotify, we've always believed in the speed and ingenuity that comes from having autonomous development teams. But as we learned firsthand, the faster you grow, the more fragmented and complex your software ecosystem becomes. And then everything slows down again. By centralizing services and standardizing your tooling, Backstage streamlines your development environment from end to end. Instead of restricting autonomy, standardization frees your engineers from infrastructure complexity. So you can return to building and scaling, quickly and safely. Every team can see all the services they own and related resources (deployments, data pipelines, pull request status, etc.) -
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The Tengo Language
A fast script language for Go
Tengo is a small, dynamic, fast, secure script language for Go. Tengo is fast and secure because it's compiled/executed as bytecode on stack-based VM that's written in native Go. Securely Embeddable and Extensible. Compiler/runtime written in native Go (no external deps or cgo). Executable as a standalone language / REPL. Use cases, rules engine, state machine, data pipeline, transpiler. If you need to evaluate a simple expression, you can use Eval function instead. -
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CloverDX
Design, automate, operate and publish data pipelines at scale
Please, visit www.cloverdx.com for latest product versions. Data integration platform; can be used to transform/map/manipulate data in batch and near-realtime modes. Suppors various input/output formats (CSV,FIXLEN,Excel,XML,JSON,Parquet, Avro,EDI/X12,HL7,COBOL,LOTUS, etc.). Connects to RDBMS/JMS/Kafka/SOAP/Rest/LDAP/S3/HTTP/FTP/ZIP/TAR. CloverDX offers 100+ specialized components which can be further extended by creation of "macros" - subgraphs - and libraries, shareable with 3rd parties. Simple data manipulation jobs can be created visually. More complex business logic can be implemented using Clover's domain-specific-language CTL, in Java or languages like Python or JavaScript. Through its DataServices functionality, it allows to quickly turn data pipelines into REST API endpoints. The platform allows to easily scale your data job across multiple cores or nodes/machines. Supports Docker/Kubernetes deployments and offers AWS/Azure images in their respective marketplace -
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Datapipe
Real-time, incremental ETL library for ML with record-level depend
Datapipe is a real-time, incremental ETL library for Python with record-level dependency tracking. Datapipe is designed to streamline the creation of data processing pipelines. It excels in scenarios where data is continuously changing, requiring pipelines to adapt and process only the modified data efficiently. This library tracks dependencies for each record in the pipeline, ensuring minimal and efficient data processing. -
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Best-of Python
A ranked list of awesome Python open-source libraries
This curated list contains 390 awesome open-source projects with a total of 1.4M stars grouped into 28 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! Ranked list of awesome python libraries for web development. Correctly generate plurals, ordinals, indefinite articles; convert numbers. Libraries for loading, collecting, and extracting data from a variety of data sources and formats. Libraries for data batch- and stream-processing, workflow automation, job scheduling, and other data pipeline tasks. -
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BitSail
BitSail is a distributed high-performance data integration engine
BitSail is ByteDance's open source data integration engine which is based on distributed architecture and provides high performance. It supports data synchronization between multiple heterogeneous data sources, and provides global data integration solutions in batch, streaming, and incremental scenarios. At present, it serves almost all business lines in ByteDance, such as Douyin, Toutiao, etc., and synchronizes hundreds of trillions of data every day. BitSail has been widely used and supports hundreds of trillions of large traffic. At the same time, it has been verified in various scenarios such as the cloud native environment of the volcano engine and the on-premises private cloud environment. -
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Kestra
Kestra is an infinitely scalable orchestration and scheduling platform
Build reliable workflows, blazingly fast, deploy in just a few clicks. 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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Microsoft Integration
Microsoft Integration, Azure, Power Platform, Office 365 and much more
Microsoft Integration, Azure, BAPI, Office 365 and much more Stencils Pack it’s a Visio package that contains fully resizable Visio shapes (symbols/icons) that will help you to visually represent On-premise, Cloud or Hybrid Integration and Enterprise architectures scenarios (BizTalk Server, API Management, Logic Apps, Service Bus, Event Hub…), solutions diagrams and features or systems that use Microsoft Azure and related cloud and on-premises technologies in Visio 2016/2013. -
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PipeRider
Code review for data in dbt
PipeRider automatically compares your data to highlight the difference in impacted downstream dbt models so you can merge your Pull Requests with confidence. PipeRider can profile your dbt models and obtain information such as basic data composition, quantiles, histograms, text length, top categories, and more. PipeRider can integrate with dbt metrics and present the time-series data of metrics in the report. PipeRider generates a static HTML report each time it runs, which can be viewed locally or shared. You can compare two previously generated reports or use a single command to compare the differences between the current branch and the main branch. The latter is designed specifically for code review scenarios. In our pull requests on GitHub, we not only want to know which files have been changed, but also the impact of these changes on the data. PipeRider can easily generate comparison reports with a single command to provide this information. -
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Tributary
Streaming reactive and dataflow graphs in Python
Tributary is a library for constructing dataflow graphs in Python. Unlike many other DAG libraries in Python (airflow, luigi, prefect, dagster, dask, kedro, etc), tributary is not designed with data/etl pipelines or scheduling in mind. Instead, tributary is more similar to libraries like mdf, loman, pyungo, streamz, or pyfunctional, in that it is designed to be used as the implementation for a data model. One such example is the greeks library, which leverages tributary to build data models for options pricing. -
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go-streams
A lightweight stream processing library for Go
A lightweight stream processing library for Go. go-streams provides a simple and concise DSL to build data pipelines. In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. -
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rudderstack
Privacy and Security focused Segment-alternative, in Golang
Quickly deploy flexible, powerful customer data pipelines, then send the data to your entire stack—without the engineering headache. Our complete toolset makes it easy to level-up your customer data stack. Spare your data engineers the headache. Our 180+ integrations, along with custom webhook sources and destinations, save data teams hundred of hours. Say goodbye to different versions of the truth. Our SDKs track anonymous and known users at the source and reconcile users in your warehouse and SaaS tools. Go beyond event streaming and control all of your customer data on your own terms. Learn how we can help you build a customer data platform. RudderStack treats your data warehouse as a first-class citizen among destinations, with advanced features and configurable, near real-time sync. RudderStack is built API-first. It integrates seamlessly with the tools that the developers already use and love. -
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Next-generation data pipeline to statistically call methylated and differentially methylated loci See the manual in doc/manual.pdf Please note: Calling of (differentially) methylated _positions_ will soon be uploaded.
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CCDLAB
A FITS image data viewer & reducer, and UVIT Data Reduction Pipeline.
CCDLAB is a FITS image data viewer, reducer, and UVIT Data Pipeline. The latest CCDLAB installer can be downloaded here: https://github.com/user29A/CCDLAB/releases The Visual Studio 2017 project files can be found here: https://github.com/user29A/CCDLAB/ Those may not be the latest code files as code is generally updated a few times a week. If you want the latest project files then let me know. -
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to run Unzip the zip file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. Also copy winutils.exe from here into HADOOP_HOME\bin -
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Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
During the exploration phase of a machine learning project, a data scientist tries to find the optimal pipeline for his specific use case. This usually involves applying standard data cleaning steps, creating or selecting useful features, trying out different models, etc. Testing multiple pipelines requires many lines of code, and writing it all in the same notebook often makes it long and cluttered. On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be quite time-consuming. How many times have you conducted the same action to pre-process a raw dataset? How many times have you copy-and-pasted code from an old repository to re-use it in a new use case? ATOM is here to help solve these common issues. The package acts as a wrapper of the whole machine learning pipeline, helping the data scientist to rapidly find a good model for his problem.
