Open Source Python Data Management Systems - Page 8
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Python Data Management Systems
View 4344 business solutionsBrowse free open source Python Data Management Systems and projects below. Use the toggles on the left to filter open source Python Data Management Systems by OS, license, language, programming language, and project status.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Bloxs
Build dashboards in Jupyter Notebook with numeric and chart boxes
Bloxs is a simple Python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook. -
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Blueprint MCP
Diagram generation for understanding codebases and system architecture
Blueprint MCP is a modular control plane designed for managing and orchestrating multiple game-server clusters in real time, giving operators fine-grained control over scaling, configuration, and deployment workflows across distributed infrastructure. It provides a central management REST API and dashboard where teams can view cluster health, adjust instance fleets, set auto-scaling policies, and monitor usage metrics in a unified interface. Blueprint-MCP also supports templated server configurations so game environments can be versioned, replicated, and deployed consistently across regions or cloud providers. The control plane includes hooks for event-driven automation, allowing rules like “scale up at peak hours” or “restart unhealthy nodes automatically” to be codified and managed without manual intervention. Security and access control are built in so administrators can assign roles, manage secrets, and enforce network policies across cluster resources. -
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Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed processing engine that uses a dataflow computational model to provide parallelizable stream processing and event processing capabilities similar to Flink, Spark, and Kafka Streams. You can use Bytewax for a variety of workloads from moving data à la Kafka Connect style all the way to advanced online machine learning workloads. Bytewax is not limited to streaming applications but excels anywhere that data can be distributed at the input and output. -
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A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.
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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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Parse, analyze and -- most importantly -- use COBOL data definitions. This gives you access to COBOL data from Python programs. Write data analyzers, one-time data conversion utilities and Python programs that are part of COBOL systems. Really.
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CSV*Extractor Pro (Windows command line)
Spool you scalar data in CSV format from 14 major Databases.
Command line tool for data export from major relational data stores (RDBMS). DB2 Advanced Enterprise Server DB2 Advanced Workgroup Server DB2 Developer Edition DB2 Enterprise Server DB2 Express DB2 Express C DB2 Workgroup Server Exadata Infobright Informix IDS Informix Innovator C MariaDB MySQL Oracle Oracle XE PostgreSQL SAP Sybase ASE SQL Lite SQL Server Enterprise SQL Server Express Sybase IQ Sybase SQL Anywhere TimesTen -
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CSV*Extractor for RDBMS (command line)
Extract table data is CSV format from 14 databases.
Spools data for a given query or table from 14 databases. Windows command-line application. -
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CSV*Loader PRO (Windows command line)
Loads CSV file to14 databases
Windows command line tool for CSV data load to 14 relational stores. -
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CSV*Loader for Oracle
Simplified CSV turbo loader to Oracle
Tired of writing control files? No problem! CSV*Loader will generate control file for SQL*Loader. Too slow? No problem! CSV*Loader turbo mode may load it 10x faster to your Oracle database than your good old Perl::DBI script. -
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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# CSV Splitter Uma ferramenta para dividir arquivos CSV em múltiplos arquivos com base na quantidade de registros especificada, mantendo a integridade dos dados e permitindo configurações de charset, separador e formatação. Ideal para lidar com grandes arquivos CSV que precisam ser fragmentados para melhor manuseio e processamento. ## Funcionalidades - **Divisão de CSV**: Divide o arquivo original em múltiplos arquivos CSV, com o número de registros por arquivo definido pelo usuário. - **Detecção Automática de Charset e Separador**: O charset e o separador do arquivo de origem podem ser detectados automaticamente ou especificados manualmente. - **Configuração de Destino Personalizável**: Permite definir charset e separador de destino. - **Formatação de Dados**: Formatação opcional para os padrões BR, EUA, EU e UK, com exemplos para ajudar na escolha do formato desejado. - **Interface Gráfica Intuitiva**: Interface com `Tkinter`, incluindo barra de progresso e log do proc
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CairoPlot is a Python module to plot graphics in an easy and intuitive way, creating beautiful results for presentations, websites and reports.
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CalPyLab is an open source data acquisition and (light) analysis software suite for scientific laboratories. The goal of this project is to provide a lightweight replacement for lab-view, to provide a quick and easy way to connect to your equipment.
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CellTypist
A tool for semi-automatic cell type classification, harmonization
CellTypist is an automated tool for cell type classification, harmonization, and integration. Classification, transfer cell type labels from the reference to query dataset. Harmonization, match and harmonize cell types defined by independent datasets. integration, integrate cell and cell types with supervision from harmonization. CellTypist recapitulates cell type structure and biology of independent datasets. Regularised linear models with Stochastic Gradient Descent provide a fast and accurate prediction. Scalable and flexible. Python-based implementation is easy to integrate into existing pipelines. A community-driven encyclopedia for cell types. -
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Cellicone is a project to develop an artificial life organism with the necessary components to make it comparable to biological life as we know it. This includes components ranging from proteins to cells to organs to limbs, and many steps between.
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Chartshare is a computer based system for the production, analysis, and distribution of Standard Celeration Charts (http://www.celeration.org).
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box. -
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clusterviz allows to cluster three-dimensional data. The clustering process is visualized using OpenGL. As clustering algorithms the family of k-means algorithms is implemented, including mixture models.
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CoSimA+
The Co-Simulation Adaptation Platform
The co-simulation adapation platform serves as programming framework and middleware to enable coupling of distributed, heterogeneous numerical models. The framework facilitates the adaptation and integration of new sub-models into a common simulation platform. -
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Collaborative Computing Project for NMR
Collaborative Computing Project for NMR (CCPN)
The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields. -
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CollectD-CSV Plotter
Web interface for visualizing CSV data from CollectD monitoring system
CollectD-CSV Plotter is a web interface for fetching and visualizing data that is saved in CollectD CSV format. An HTML form is used for defining the time interval and choosing the wanted measurements. The plotter is actually a front-end for collectd-csv module (http://pypi.python.org/pypi/collectd-csv/), which it uses for fetching the data. Technologies CollectD-CSV Plotter uses include CGI/Python, HTML5/JavaScript, jQuery, jQuery-UI and Gnuplot. -
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Computer Controlled Optical Microscopy is a package to enable camera equipped microscopes to generate multi-modal time-lapse image sequences of 3D volumes. This is achieved by periodically changing optical settings, xyz positions and acquiring images.
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Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression. -
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Data logger, manipulator and visualizer. Receives data in CSV format from stdio.
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Covalent workflow
Pythonic tool for running machine-learning/high performance workflows
Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent in AI/ML experimentation.
