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Showing 9 open source projects for "python-bibtex"
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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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MongoEngine
A Python Object-Document-Mapper for working with MongoDB
MongoEngine is a Python Object-Document Mapper for working with MongoDB. -
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PyMongo
PyMongo - the Official MongoDB Python driver
The PyMongo distribution contains tools for interacting with MongoDB database from Python. The bson package is an implementation of the BSON format for Python. The pymongo package is a native Python driver for MongoDB. The gridfs package is a gridfs implementation on top of pymongo. -
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ArcticDB
ArcticDB is a high performance, serverless DataFrame database
Built for the modern Python Data Science ecosystem, ArcticDB transforms your ability to handle complex real-world data with an Incredibly fast proven Petabyte scale. ArcticDB is designed with quant users in mind. It allows you to self-manage your data leveraging your preferred infrastructure. Giving you the keys to protect your most valuable asset. Supports large concurrent writes to many tables ensuring datasets can be onboarded fast and in the most convenient format. -
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EdgeDB
A next-generation graph-relational database
The world's favorite graph-relational database is back for an action-packed sequel. Tune in for a series of lightning talks breaking down what's new and what's next for EdgeDB. EdgeDB is an open-source database designed as a spiritual successor to SQL and the relational paradigm. It aims to solve some hard design problems that make existing databases unnecessarily onerous to use. Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects... -
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Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. This project includes many examples of Jupyter notebooks. ... -
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Dokploy
Open Source Alternative to Vercel, Netlify and Heroku
Streamline your operations with our all-in-one platform, perfect for managing projects, data, and system health with simplicity and efficiency. Simplify your project and data management, ensure robust monitoring, and secure your backups—all without the fuss over minute details. Elevate your infrastructure with tools that offer precise control, detailed monitoring, and enhanced security, ensuring seamless management and robust performance. Streamline your deployments with our PaaS.... -
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Resources
Relative Examples and Resources of iBoxDB, etc...
IOs, Graph Database Wrapper and more examples for iBoxDB Download Mirrors https://sourceforge.net/settings/mirror_choices?projectname=application-database&filename=iboxdb411.zip&selected=pilotfiber -
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**Zastosowanie**: Głównym celem projektu jest optymalizacja wykorzystania pamięci w graficznych bazach danych poprzez analizę podobieństwa cosinusowego wektorów cech obrazów. Umożliwia identyfikację duplikatów (obrazów bardzo do siebie podobnych), co prowadzi do efektywniejszego zarządzania zasobami. **Program** W chwili obecnej program przeszukuje obrazy w katalogu, do którego ścieżka została zapisana w pliku 'Data\settings.txt'. **Użycie**: Program obsługuje się w terminalu. Po...
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Cosmos DB Spark
Apache Spark Connector for Azure Cosmos DB
Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data. -
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