Open Source Data Integration Tools
Sort By:
Data Integration Tools
View 58 business solutionsBrowse free open source Data Integration tools and projects for Windows and Mac below. Use the toggles on the left to filter open source Data Integration tools by OS, license, language, programming language, and project status.
-
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.
-
ClouDNS provides fast and secure Free DNS Hosting and Premium DNS Hosting with Global Anycast DNS Network.ClouDNS is the biggest European provider of globally Managed DNS services, including GeoDNS, Anycast DNS and DDoS protected DNS. We also offer Domain names, Monitoring services, SSL Certificates, and Google Workspace.
-
1
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/">
-
2
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. -
3
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. -
4
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. -
Optimize every aspect of hiring with Greenhouse RecruitingWhat’s next for many of us is changing. Your company’s ability to hire great talent is as important as ever – so you’ll be ready for whatever’s ahead. Whether you need to scale your team quickly or improve your hiring process, Greenhouse gives you the right technology, know-how and support to take on what’s next.
-
5
PHPCI
PHPCI is a free and open source continuous integration tool
PHPCI is a continuous integration (CI) server designed specifically for PHP applications. It automates tasks such as testing, code quality checks, and deployment, helping developers maintain code consistency and detect issues early. PHPCI supports various plugins and tools, including PHPUnit, PHPMD, and Codeception, making it highly customizable for different project needs. -
6
Recap
Recap tracks and transform schemas across your whole application
Recap is a schema language and multi-language toolkit to track and transform schemas across your whole application. Your data passes through web services, databases, message brokers, and object stores. Recap describes these schemas in a single language, regardless of which system your data passes through. Recap schemas can be defined in YAML, TOML, JSON, XML, or any other compatible language. -
7
Gradle Docker Compose Plugin
Simplifies usage of Docker Compose for integration testing
The Gradle Docker Compose Plugin by Avast integrates Docker Compose lifecycle management into Gradle builds. It allows developers to define and manage Docker containers required for integration testing or local development directly from their Gradle build scripts. This plugin automates the startup and shutdown of services, supports container health checks, and enables tight integration between application code and containerized services, enhancing reproducibility and automation in development pipelines. -
8
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. -
9
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. -
Queue Management System for Busy Service Providers | WaitWellThe queue management system that perfectly adapts to your workflows. Improve operational efficiency in weeks with the most configurable enterprise queue system.
-
10
PhantomJS-Node
PhantomJS integration module for NodeJS
PhantomJS-Node is a Node.js bridge to PhantomJS, enabling programmatic control of the headless browser for tasks like web scraping, automated testing, and page rendering. -
11
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. -
12
Hetionet
Hetionet: an integrative network of disease
Hetionet is a hetnet — network with multiple node and edge (relationship) types — which encodes biology. The hetnet was designed for Project Rephetio, which aims to systematically identify why drugs work and predict new therapies for drugs. The JSON and Neo4j formats contain node and edge properties, which are absent in the TSV and matrix formats, including licensing information. Therefore the recommended formats are JSON and Neo4j. Our hetio package in Python reads the JSON format, but it is otherwise a simple yet new format. The Neo4j graph database has an established and thriving ecosystem. However, if you would like to access Hetionet without Neo4j, then we suggest the JSON format. The matrix format refers to HetMat archives, which store edge adjacency matrices on disk. Additional usage information is available at the corresponding download locations. -
13
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/ -
14
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. -
15
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. -
16
KubeRay
A toolkit to run Ray applications on Kubernetes
KubeRay is a powerful, open-source Kubernetes operator that simplifies the deployment and management of Ray applications on Kubernetes. It offers several key components. KubeRay core: This is the official, fully-maintained component of KubeRay that provides three custom resource definitions, RayCluster, RayJob, and RayService. These resources are designed to help you run a wide range of workloads with ease. -
17
Nest Manager
NST Manager (SmartThings)
Nest Manager is a community SmartThings solution that integrates Nest devices—thermostats, Protects, and cameras—into the SmartThings ecosystem via a comprehensive SmartApp and device handlers. It offers a unified dashboard, rich device tiles, and automation hooks so users can monitor and control temperature, modes, and alerts alongside other smart home devices. The project emphasizes usability with guided setup flows, status summaries, and in-app diagnostics to help troubleshoot connectivity or permission issues. It exposes detailed attributes and commands, enabling powerful rules and scenes that coordinate Nest with sensors, presence, and schedules in SmartThings. Historical and environmental data can be surfaced to support energy-aware automations and notifications. For advanced users, it provides granular preferences to tune polling, event verbosity, and safety behaviors, turning SmartThings into a capable hub for Nest-centric homes. -
18
reticulate
R Interface to Python
reticulate is an R package from Posit that creates seamless interoperability between R and Python. It lets you call Python modules, classes, and functions from within R, automatically translating between R and Python data structures. Useful for combining Python tooling with R projects, data analysis, and RMarkdown reports. -
19
EasyDataQuality for Pentaho Kettle
EasyDataQuality for Pentaho Data Integration in Kettle
EasyDQ plugins for Contact cleansing in Pentaho Data Integration in Kettle. -
20
Jaspersoft ETL is a data integration platform providing high performance data extract-transform-load (ETL) capabilities. Jaspersoft ETL is appropriate for all analytic and operational data integration needs. Activity on this project is located at jas
-
21
Daffodil Replicator is a powerful Open Source Java tool for data integration, data migration and data protection in real time. It allows bi-directional data replication and synchronization between homogeneous / heterogeneous databases including Oracle, M
-
22
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.
-
23
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. -
24
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. -
25
COMA Community Edition
Schema Matching Solution for Data Integration
COMA CE is the community edition of the well-established COMA project developed at the University of Leipzig. It comprises the parsers, matcher library, matching framework and a sample GUI for tests and evaluations. COMA was initiated at the database chair of the University of Leipzig in 2002 and got much positive feedback ever since. It excels due to numerous matching strategies, which can be combined to large matching workflows, and which enable reliable match results between different kind of schemas.