Open Source Linux Data Integration Tools
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Data Integration Tools for Linux
View 41 business solutionsBrowse free open source Data Integration tools and projects for Linux 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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Observability AI That Actually Does the WorkWhether you're a seasoned SRE or new to Grafana, the built-in AI assistant troubleshoots faster and surfaces root causes across your whole stack. Grafana Cloud unifies your telemetry signals into one clear map.
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The AI-powered unified PSA-RMM platform for modern MSPs.SuperOps.ai is the only PSA-RMM platform powered by intelligent automation and thoughtfully crafted for the new-age MSP. The platform also helps MSPs manage their projects, clients, and IT documents from a single place.
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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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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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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. -
Hybrid Bare Metal Cloud Infrastructure | Servers.comThree bare metal hosting solutions on one global network. Spin up on demand to cover peaks, then optimize for cost when usage stabilizes.
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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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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. -
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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. -
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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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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 -
One Subscription for Email, Storage, Meetings, and AIGet a custom business email at your domain, up to 5TB of pooled storage per user, video meetings with recording and noise cancellation, eSignature in Docs, and appointment booking through Calendar. Real-time collaboration across every device, with enterprise-grade security built in.
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EasyDataQuality for Pentaho Kettle
EasyDataQuality for Pentaho Data Integration in Kettle
EasyDQ plugins for Contact cleansing in Pentaho Data Integration in Kettle. -
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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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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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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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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. -
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KETL(tm) is a production ready ETL platform. The engine is built upon an open, multi-threaded, XML-based architecture. KETL's is designed to assist in the development and deployment of data integration efforts which require ETL and scheduling
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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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The Stem Cell Artificial Neural network project entails the analysis and integration of genomics data for extracting the stemness signature of several tissues by training a multiclass single-layer linear artificial neural network.
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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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BD integration
Heterogeneous BD integration
The increasing need to obtain a generalized view of the information resources, presented in various systems has led to the data integration mechanisms formation, which focus on efficient access organization to external, heterogeneous data sources through a single interface. The project includes the mass integration platform which allows to create global infrastructure of tens and hundreds of heterogeneous databases based on service-oriented approach. -
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The BioDataServer is a database integration system. It implements a mediator-wrapper architecture and offers a SQL interface. The data integration is based on user defined intergrated schema and adapter that wrap any kind of data source.
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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.
