Open Source Windows Data Integration Tools
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Data Integration Tools for Windows
View 113 business solutionsBrowse free open source Data Integration tools and projects for Windows below. Use the toggles on the left to filter open source Data Integration tools by OS, license, language, programming language, and project status.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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Pentaho Data Integration
Pentaho Data Integration ( ETL ) a.k.a Kettle
Pentaho Data Integration uses the Maven framework. Project distribution archive is produced under the assemblies module. Core implementation, database dialog, user interface, PDI engine, PDI engine extensions, PDI core plugins, and integration tests. Maven, version 3+, and Java JDK 1.8 are requisites. Use of the Pentaho checkstyle format (via mvn checkstyle:check and reviewing the report) and developing working Unit Tests helps to ensure that pull requests for bugs and improvements are processed quickly. In addition to the unit tests, there are integration tests that test cross-module operation. -
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Airbyte
Data integration platform for ELT pipelines from APIs, databases
We believe that only an open-source solution to data movement can cover the long tail of data sources while empowering data engineers to customize existing connectors. Our ultimate vision is to help you move data from any source to any destination. Airbyte already provides the largest catalog of 300+ connectors for APIs, databases, data warehouses, and data lakes. Moving critical data with Airbyte is as easy and reliable as flipping on a switch. Our teams process more than 300 billion rows each month for ambitious businesses of all sizes. Enable your data engineering teams to focus on projects that are more valuable to your business. Building and maintaining custom connectors have become 5x easier with Airbyte. With an average response rate of 10 minutes or less and a Customer Satisfaction score of 96/100, our team is ready to support your data integration journey all over the world. -
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Jitsu
Jitsu is an open-source Segment alternative
Jitsu is a fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days. Installing Jitsu is a matter of selecting your framework and adding few lines of code to your app. Jitsu is built to be framework agnostic, so regardless of your stack, we have a solution that'll work for your team. Connect data warehouse (Snowflake, Clickhouse, BigQuery, S3, Redshift ot Postgres) and query your data instantly. Jitsu can either stream data in real-time or send it in micro-batches (up to once a minute). Apply any transformation with Jitsu. Just write JavaScript code right in the UI to do anything with incoming data. And yes, the code editor supports code completion, debugging and many more. It feels like a full-featured IDE! -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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Dagster
An orchestration platform for the development, production
Dagster is an orchestration platform for the development, production, and observation of data assets. Dagster as a productivity platform: With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early. Dagster as a robust orchestration engine: Put your pipelines into production with a robust multi-tenant, multi-tool engine that scales technically and organizationally. Dagster as a unified control plane: The ‘single plane of glass’ data teams love to use. Rein in the chaos and maintain control over your data as the complexity scales. Centralize your metadata in one tool with built-in observability, diagnostics, cataloging, and lineage. Spot any issues and identify performance improvement opportunities. -
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PANDORA
Revolutionizing Biomedical Research with Advanced Machine Learning
PANDORA is a machine learning (ML) tool that can be used to integrate various data types, including clinical, transcriptome and microbiome data and find connections in large datasets. PANDORA can be easily installed using Docker, a pre-built version of the software can be pulled from DockerHub. In order to run a test instance of PANDORA, users will first need to prepare their local environment by downloading, installing, and configuring Docker. genular is a community behind SIMON an open-source Machine Learning KnowledgeDiscovery software, built by a vibrant community of people just like you! Join us and make SIMON even cooler! Exploratory analysis of machine learning results with the help of many different visualization techniques will give you instant insights into models and data. -
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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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CI Tools Demo
Docker Infrastructure via docker-compose
This repository provides a Docker-powered CI tools demo environment via a single command with docker-compose. It assembles popular CI/CD components—Jenkins, SonarQube, Nexus, GitLab, and Selenium Grid—each running in separate containers, facilitating self-contained integration testing or workshops. It’s not intended for production but serves as a practical demo or launchpad for containerized CI stacks. Each tool runs in an isolated container for modular experimentation. Maintained primarily for workshops and proofs of concept, not for production use. Includes legacy documentation and scripting for Mac users and older setups. -
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Common Core Ontologies
The Common Core Ontology Repository
The Common Core Ontologies (CCO) comprise twelve ontologies that are designed to represent and integrate taxonomies of generic classes and relations across all domains of interest. CCO is a mid-level extension of Basic Formal Ontology (BFO), an upper-level ontology framework widely used to structure and integrate ontologies in the biomedical domain (Arp, et al., 2015). BFO aims to represent the most generic categories of entity and the most generic types of relations that hold between them, by defining a small number of classes and relations. CCO then extends from BFO in the sense that every class in CCO is asserted to be a subclass of some class in BFO, and that CCO adopts the generic relations defined in BFO (e.g., has_part) (Smith and Grenon, 2004). Accordingly, CCO classes and relations are heavily constrained by the BFO framework, from which it inherits much of its basic semantic relationships. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
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nango
A single API for all your integrations.
Nango is a single API to interact with all other external APIs. It should be the only API you need to integrate to your app. Nango is an open-source solution for integrating third-party APIs with applications, simplifying API authentication, data syncing, and management. -
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nichenetr
NicheNet: predict active ligand-target links between interacting cells
nichenetr: the R implementation of the NicheNet method. The goal of NicheNet is to study intercellular communication from a computational perspective. NicheNet uses human or mouse gene expression data of interacting cells as input and combines this with a prior model that integrates existing knowledge on ligand-to-target signaling paths. This allows to predict ligand-receptor interactions that might drive gene expression changes in cells of interest. This model of prior information on potential ligand-target links can then be used to infer active ligand-target links between interacting cells. NicheNet prioritizes ligands according to their activity (i.e., how well they predict observed changes in gene expression in the receiver cell) and looks for affected targets with high potential to be regulated by these prioritized ligands. -
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Open Source Data Quality and Profiling
World's first open source data quality & data preparation project
This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy. This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data Creation, Meta Data Discovery, Anomaly Discovery, Data Cleansing, Reporting and Analytic. It also had Hadoop ( Big data ) support to move files to/from Hadoop Grid, Create, Load and Profile Hive Tables. This project is also known as "Aggregate Profiler" Resful API for this project is getting built as (Beta Version) https://sourceforge.net/projects/restful-api-for-osdq/ apache spark based data quality is getting built at https://sourceforge.net/projects/apache-spark-osdq/ -
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Metl ETL Data Integration
Simple message-based, web-based ETL integration
Metl is a simple, web-based ETL tool that allows for data integrations including database, files, messaging, and web services. Supports RDBMS, SOAP, HTTP, FTP, SFTP, XML, FIXLEN, CSV, JSON, ZIP, and more. Metl implements scheduled integration tasks without the need for custom coding or heavy infrastructure. It can be deployed in the cloud or in an internal data center, and it was built to allow developers to extend it with custom components. -
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PDI Data Vault framework
Data Vault loading automation using Pentaho Data Integration.
A metadata driven 'tool' to automate loading a designed Data Vault. It consists of a set of Pentaho Data Integration and database objects. Thel Virtual Machine (VMware) is a 64 bit Ubuntu Server 14.04, with MySQL (Percona Server) and PostgreSQL 9.4 as the database flavours and PDI version 5.2 CE. NB: Directory version_2.4 contains the most recent Virtual Machine. The readme.txt contains info about that VM. -
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PaloKettlePlugin is for Pentaho Data Integration aka Kettle. It's a Cell Input und Output Step for Palo Molap. The first code was developed by mybiq/3A-Strategy, the PDI-3 version has been developed by Stratebi. Now by 3A-Strategy and Litebi for PDI
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SnapLogic is an Open Source Data Integration framework that combines the power of state-of-the-art dynamic programming languages with standard Web interfaces to solve today's most pressing problems in data integration.
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Tarunyai
Tool is in development please donot download.Analytics tool
This tool is still in development phase please donot download for now. Tarunyai data integration prepares and blends data to create a complete picture of your business that drives actionable insights. -
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The Bio2RDF project aims to transforms silos of life science data into a globally distributed network of linked data for biological knowledge discovery. Bio2RDF creates and provides machine understandable descriptions of biological entities using the RDF/RDFS/OWL Semantic Web languages. Using both syntactic and semantic data integration techniques, Bio2RDF seamlessly integrates diverse biological data and enables powerful new SPARQL-based services across its globally distributed knowledge bases.
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Templates for integrating the data structures of Compiere, Openbravo or ADempiere for all kind of Pentaho Data Integration processes. Later on we plan to migrate these to Talend too.
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ARSystem plugins for Pentaho Kettle
AR-System step and db plugins for Pentaho Data Integration Kettle V5
Allows you to write per API to AR-System Server (BMC Remedy Action Request System). Includes two step output, one step input and one database plugin. The step plugins need the database plugin. -
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AWS data tools
AWS tools for data integration and more
Here I list some data tools I created for Amazon AWS S3 and Redshift -
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Alova.js
Workflow-Streamlined next-generation request tools
Extremely streamline API integration workflow. Quickly find APIs in the editor, and enjoy full type hints even in js projects with the API code automatically generated by Alova's extension. Request in various complex scenes by one line of code. Automatically manage paging data, and data preloading, reduce unnecessary data refresh, improve fluency by 300%, and reduce coding difficulty by 50%. Send requests immediately by watching state changes, useful in tab switching and condition querying. Global interceptor that supports silent token refresh, as well as providing unified management of token-based login, logout, token assignment, and token refresh. -
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Apache DevLake
Apache DevLake is an open-source dev data platform
Apache DevLake is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data from DevOps tools to extract insights for engineering excellence, developer experience, and community growth. Apache DevLake is designed for developer teams looking to make better sense of their development process and to bring a more data-driven approach to their own practices. You can ask Apache DevLake many questions regarding your development process. Just connect and query. Your Dev Data lives in many silos and tools. DevLake brings them all together to give you a complete view of your Software Development Life Cycle (SDLC). From DORA to scrum retros, DevLake implements metrics effortlessly with prebuilt dashboards supporting common frameworks and goals. DevLake fits teams of all shapes and sizes, and can be readily extended to support new data sources, metrics, and dashboards, with a flexible framework for data collection and transformation. -
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Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides efficient upserts, by mapping a given hoodie key (record key + partition path) consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records.
